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action representation</title> <meta name="description" content="Search results for: semantic action representation"> <meta name="keywords" content="semantic action representation"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="semantic action representation"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4102</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: semantic action representation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4102</span> Towards a Large Scale Deep Semantically Analyzed Corpus for Arabic: Annotation and Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Alansary">S. Alansary</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Nagi"> M. Nagi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an approach of conducting semantic annotation of Arabic corpus using the Universal Networking Language (UNL) framework. UNL is intended to be a promising strategy for providing a large collection of semantically annotated texts with formal, deep semantics rather than shallow. The result would constitute a semantic resource (semantic graphs) that is editable and that integrates various phenomena, including predicate-argument structure, scope, tense, thematic roles and rhetorical relations, into a single semantic formalism for knowledge representation. The paper will also present the Interactive Analysis tool for automatic semantic annotation (IAN). In addition, the cornerstone of the proposed methodology which are the disambiguation and transformation rules, will be presented. Semantic annotation using UNL has been applied to a corpus of 20,000 Arabic sentences representing the most frequent structures in the Arabic Wikipedia. The representation, at different linguistic levels was illustrated starting from the morphological level passing through the syntactic level till the semantic representation is reached. The output has been evaluated using the F-measure. It is 90% accurate. This demonstrates how powerful the formal environment is, as it enables intelligent text processing and search. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20analysis" title="semantic analysis">semantic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20annotation" title=" semantic annotation"> semantic annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic" title=" Arabic"> Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=universal%20networking%20language" title=" universal networking language"> universal networking language</a> </p> <a href="https://publications.waset.org/abstracts/17455/towards-a-large-scale-deep-semantically-analyzed-corpus-for-arabic-annotation-and-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17455.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">582</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4101</span> A Semantic and Concise Structure to Represent Human Actions </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tobias%20Str%C3%BCbing">Tobias Strübing</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatemeh%20Ziaeetabar"> Fatemeh Ziaeetabar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Humans usually manipulate objects with their hands. To represent these actions in a simple and understandable way, we need to use a semantic framework. For this purpose, the Semantic Event Chain (SEC) method has already been presented which is done by consideration of touching and non-touching relations between manipulated objects in a scene. This method was improved by a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of static (e.g. top, bottom) and dynamic spatial relations (e.g. moving apart, getting closer) between objects in an action scene. This leads to a better action prediction as well as the ability to distinguish between more actions. Each eSEC manipulation descriptor is a huge matrix with thirty rows and a massive set of the spatial relations between each pair of manipulated objects. The current eSEC framework has so far only been used in the category of manipulation actions, which eventually involve two hands. Here, we would like to extend this approach to a whole body action descriptor and make a conjoint activity representation structure. For this purpose, we need to do a statistical analysis to modify the current eSEC by summarizing while preserving its features, and introduce a new version called Enhanced eSEC or (e2SEC). This summarization can be done from two points of the view: 1) reducing the number of rows in an eSEC matrix, 2) shrinking the set of possible semantic spatial relations. To achieve these, we computed the importance of each matrix row in an statistical way, to see if it is possible to remove a particular one while all manipulations are still distinguishable from each other. On the other hand, we examined which semantic spatial relations can be merged without compromising the unity of the predefined manipulation actions. Therefore by performing the above analyses, we made the new e2SEC framework which has 20% fewer rows, 16.7% less static spatial and 11.1% less dynamic spatial relations. This simplification, while preserving the salient features of a semantic structure in representing actions, has a tremendous impact on the recognition and prediction of complex actions, as well as the interactions between humans and robots. It also creates a comprehensive platform to integrate with the body limbs descriptors and dramatically increases system performance, especially in complex real time applications such as human-robot interaction prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=enriched%20semantic%20event%20chain" title="enriched semantic event chain">enriched semantic event chain</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20action%20representation" title=" semantic action representation"> semantic action representation</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20relations" title=" spatial relations"> spatial relations</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a> </p> <a href="https://publications.waset.org/abstracts/129003/a-semantic-and-concise-structure-to-represent-human-actions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129003.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">126</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4100</span> Real-Time Episodic Memory Construction for Optimal Action Selection in Cognitive Robotics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deon%20de%20Jager">Deon de Jager</a>, <a href="https://publications.waset.org/abstracts/search?q=Yahya%20Zweiri"> Yahya Zweiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Dimitrios%20Makris"> Dimitrios Makris</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The three most important components in the cognitive architecture for cognitive robotics is memory representation, memory recall, and action-selection performed by the executive. In this paper, action selection, performed by the executive, is defined as a memory quantification and optimization process. The methodology describes the real-time construction of episodic memory through semantic memory optimization. The optimization is performed by set-based particle swarm optimization, using an adaptive entropy memory quantification approach for fitness evaluation. The performance of the approach is experimentally evaluated by simulation, where a UAV is tasked with the collection and delivery of a medical package. The experiments show that the UAV dynamically uses the episodic memory to autonomously control its velocity, while successfully completing its mission. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20robotics" title="cognitive robotics">cognitive robotics</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20memory" title=" semantic memory"> semantic memory</a>, <a href="https://publications.waset.org/abstracts/search?q=episodic%20memory" title=" episodic memory"> episodic memory</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20entropy%20principle" title=" maximum entropy principle"> maximum entropy principle</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/114710/real-time-episodic-memory-construction-for-optimal-action-selection-in-cognitive-robotics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/114710.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4099</span> Lexical Semantic Analysis to Support Ontology Modeling of Maintenance Activities– Case Study of Offshore Riser Integrity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Ebrahimipour">Vahid Ebrahimipour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Word representation and context meaning of text-based documents play an essential role in knowledge modeling. Business procedures written in natural language are meant to store technical and engineering information, management decision and operation experience during the production system life cycle. Context meaning representation is highly dependent upon word sense, lexical relativity, and sematic features of the argument. This paper proposes a method for lexical semantic analysis and context meaning representation of maintenance activity in a mass production system. Our approach constructs a straightforward lexical semantic approach to analyze facilitates semantic and syntactic features of context structure of maintenance report to facilitate translation, interpretation, and conversion of human-readable interpretation into computer-readable representation and understandable with less heterogeneity and ambiguity. The methodology will enable users to obtain a representation format that maximizes shareability and accessibility for multi-purpose usage. It provides a contextualized structure to obtain a generic context model that can be utilized during the system life cycle. At first, it employs a co-occurrence-based clustering framework to recognize a group of highly frequent contextual features that correspond to a maintenance report text. Then the keywords are identified for syntactic and semantic extraction analysis. The analysis exercises causality-driven logic of keywords’ senses to divulge the structural and meaning dependency relationships between the words in a context. The output is a word contextualized representation of maintenance activity accommodating computer-based representation and inference using OWL/RDF. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lexical%20semantic%20analysis" title="lexical semantic analysis">lexical semantic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=metadata%20modeling" title=" metadata modeling"> metadata modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=contextual%20meaning%20extraction" title=" contextual meaning extraction"> contextual meaning extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology%20modeling" title=" ontology modeling"> ontology modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20representation" title=" knowledge representation"> knowledge representation</a> </p> <a href="https://publications.waset.org/abstracts/133830/lexical-semantic-analysis-to-support-ontology-modeling-of-maintenance-activities-case-study-of-offshore-riser-integrity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133830.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4098</span> Measuring Text-Based Semantics Relatedness Using WordNet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Madiha%20Khan">Madiha Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidrah%20Ramzan"> Sidrah Ramzan</a>, <a href="https://publications.waset.org/abstracts/search?q=Seemab%20Khan"> Seemab Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahzad%20Hassan"> Shahzad Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamran%20Saeed"> Kamran Saeed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Measuring semantic similarity between texts is calculating semantic relatedness between texts using various techniques. Our web application (Measuring Relatedness of Concepts-MRC) allows user to input two text corpuses and get semantic similarity percentage between both using WordNet. Our application goes through five stages for the computation of semantic relatedness. Those stages are: Preprocessing (extracts keywords from content), Feature Extraction (classification of words into Parts-of-Speech), Synonyms Extraction (retrieves synonyms against each keyword), Measuring Similarity (using keywords and synonyms, similarity is measured) and Visualization (graphical representation of similarity measure). Hence the user can measure similarity on basis of features as well. The end result is a percentage score and the word(s) which form the basis of similarity between both texts with use of different tools on same platform. In future work we look forward for a Web as a live corpus application that provides a simpler and user friendly tool to compare documents and extract useful information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Graphviz%20representation" title="Graphviz representation">Graphviz representation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20relatedness" title=" semantic relatedness"> semantic relatedness</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measurement" title=" similarity measurement"> similarity measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=WordNet%20similarity" title=" WordNet similarity"> WordNet similarity</a> </p> <a href="https://publications.waset.org/abstracts/95106/measuring-text-based-semantics-relatedness-using-wordnet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95106.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">237</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4097</span> Annotation Ontology for Semantic Web Development</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadeel%20Al%20Obaidy">Hadeel Al Obaidy</a>, <a href="https://publications.waset.org/abstracts/search?q=Amani%20Al%20Heela"> Amani Al Heela</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of this paper is to examine the concept of semantic web and the role that ontology and semantic annotation plays in the development of semantic web services. The paper focuses on semantic web infrastructure illustrating how ontology and annotation work to provide the learning capabilities for building content semantically. To improve productivity and quality of software, the paper applies approaches, notations and techniques offered by software engineering. It proposes a conceptual model to develop semantic web services for the infrastructure of web information retrieval system of digital libraries. The developed system uses ontology and annotation to build a knowledge based system to define and link the meaning of a web content to retrieve information for users’ queries. The results are more relevant through keywords and ontology rule expansion that will be more accurate to satisfy the requested information. The level of results accuracy would be enhanced since the query semantically analyzed work with the conceptual architecture of the proposed system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20web%20services" title="semantic web services">semantic web services</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20engineering" title=" software engineering"> software engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20library" title=" semantic library"> semantic library</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20representation" title=" knowledge representation"> knowledge representation</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a> </p> <a href="https://publications.waset.org/abstracts/103442/annotation-ontology-for-semantic-web-development" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103442.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">173</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4096</span> Arabic Text Classification: Review Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Hijazi">M. Hijazi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Zeki"> A. Zeki</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ismail"> A. Ismail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An enormous amount of valuable human knowledge is preserved in documents. The rapid growth in the number of machine-readable documents for public or private access requires the use of automatic text classification. Text classification can be defined as assigning or structuring documents into a defined set of classes known in advance. Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information written in the Arabic language on the web. This paper presents a review on the published researches of Arabic Text Classification using classical data representation, Bag of words (BoW), and using conceptual data representation based on semantic resources such as Arabic WordNet and Wikipedia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20text%20classification" title="Arabic text classification">Arabic text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic%20WordNet" title=" Arabic WordNet"> Arabic WordNet</a>, <a href="https://publications.waset.org/abstracts/search?q=bag%20of%20words" title=" bag of words"> bag of words</a>, <a href="https://publications.waset.org/abstracts/search?q=conceptual%20representation" title=" conceptual representation"> conceptual representation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20relations" title=" semantic relations"> semantic relations</a> </p> <a href="https://publications.waset.org/abstracts/42905/arabic-text-classification-review-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42905.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">426</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4095</span> Human Action Retrieval System Using Features Weight Updating Based Relevance Feedback Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Munaf%20Rashid">Munaf Rashid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For content-based human action retrieval systems, search accuracy is often inferior because of the following two reasons 1) global information pertaining to videos is totally ignored, only low level motion descriptors are considered as a significant feature to match the similarity between query and database videos, and 2) the semantic gap between the high level user concept and low level visual features. Hence, in this paper, we propose a method that will address these two issues and in doing so, this paper contributes in two ways. Firstly, we introduce a method that uses both global and local information in one framework for an action retrieval task. Secondly, to minimize the semantic gap, a user concept is involved by incorporating features weight updating (FWU) Relevance Feedback (RF) approach. We use statistical characteristics to dynamically update weights of the feature descriptors so that after every RF iteration feature space is modified accordingly. For testing and validation purpose two human action recognition datasets have been utilized, namely Weizmann and UCF. Results show that even with a number of visual challenges the proposed approach performs well. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=relevance%20feedback%20%28RF%29" title="relevance feedback (RF)">relevance feedback (RF)</a>, <a href="https://publications.waset.org/abstracts/search?q=action%20retrieval" title=" action retrieval"> action retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20gap" title=" semantic gap"> semantic gap</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20descriptor" title=" feature descriptor"> feature descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=codebook" title=" codebook"> codebook</a> </p> <a href="https://publications.waset.org/abstracts/41740/human-action-retrieval-system-using-features-weight-updating-based-relevance-feedback-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41740.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">472</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4094</span> From Shallow Semantic Representation to Deeper One: Verb Decomposition Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aliaksandr%20Huminski">Aliaksandr Huminski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Semantic Role Labeling (SRL) as shallow semantic parsing approach includes recognition and labeling arguments of a verb in a sentence. Verb participants are linked with specific semantic roles (Agent, Patient, Instrument, Location, etc.). Thus, SRL can answer on key questions such as ‘Who’, ‘When’, ‘What’, ‘Where’ in a text and it is widely applied in dialog systems, question-answering, named entity recognition, information retrieval, and other fields of NLP. However, SRL has the following flaw: Two sentences with identical (or almost identical) meaning can have different semantic role structures. Let consider 2 sentences: (1) John put butter on the bread. (2) John buttered the bread. SRL for (1) and (2) will be significantly different. For the verb put in (1) it is [Agent + Patient + Goal], but for the verb butter in (2) it is [Agent + Goal]. It happens because of one of the most interesting and intriguing features of a verb: Its ability to capture participants as in the case of the verb butter, or their features as, say, in the case of the verb drink where the participant’s feature being liquid is shared with the verb. This capture looks like a total fusion of meaning and cannot be decomposed in direct way (in comparison with compound verbs like babysit or breastfeed). From this perspective, SRL looks really shallow to represent semantic structure. If the key point in semantic representation is an opportunity to use it for making inferences and finding hidden reasons, it assumes by default that two different but semantically identical sentences must have the same semantic structure. Otherwise we will have different inferences from the same meaning. To overcome the above-mentioned flaw, the following approach is suggested. Assume that: P is a participant of relation; F is a feature of a participant; Vcp is a verb that captures a participant; Vcf is a verb that captures a feature of a participant; Vpr is a primitive verb or a verb that does not capture any participant and represents only a relation. In another word, a primitive verb is a verb whose meaning does not include meanings from its surroundings. Then Vcp and Vcf can be decomposed as: Vcp = Vpr +P; Vcf = Vpr +F. If all Vcp and Vcf will be represented this way, then primitive verbs Vpr can be considered as a canonical form for SRL. As a result of that, there will be no hidden participants caught by a verb since all participants will be explicitly unfolded. An obvious example of Vpr is the verb go, which represents pure movement. In this case the verb drink can be represented as man-made movement of liquid into specific direction. Extraction and using primitive verbs for SRL create a canonical representation unique for semantically identical sentences. It leads to the unification of semantic representation. In this case, the critical flaw related to SRL will be resolved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decomposition" title="decomposition">decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=labeling" title=" labeling"> labeling</a>, <a href="https://publications.waset.org/abstracts/search?q=primitive%20verbs" title=" primitive verbs"> primitive verbs</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20roles" title=" semantic roles"> semantic roles</a> </p> <a href="https://publications.waset.org/abstracts/62190/from-shallow-semantic-representation-to-deeper-one-verb-decomposition-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62190.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">366</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4093</span> Incorporating Lexical-Semantic Knowledge into Convolutional Neural Network Framework for Pediatric Disease Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaocong%20Liu">Xiaocong Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Huazhen%20Wang"> Huazhen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ting%20He"> Ting He</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaozheng%20Li"> Xiaozheng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Weihan%20Zhang"> Weihan Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20Chen"> Jian Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20medical%20record" title=" electronic medical record"> electronic medical record</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20representation" title=" feature representation"> feature representation</a>, <a href="https://publications.waset.org/abstracts/search?q=lexical%20semantics" title=" lexical semantics"> lexical semantics</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20decision" title=" semantic decision"> semantic decision</a> </p> <a href="https://publications.waset.org/abstracts/137499/incorporating-lexical-semantic-knowledge-into-convolutional-neural-network-framework-for-pediatric-disease-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137499.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">125</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4092</span> A Network of Nouns and Their Features :A Neurocomputational Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Skiker%20Kaoutar">Skiker Kaoutar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Maouene"> Mounir Maouene </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neuroimaging studies indicate that a large fronto-parieto-temporal network support nouns and their features, with some areas store semantic knowledge (visual, auditory, olfactory, gustatory,…), other areas store lexical representation and other areas are implicated in general semantic processing. However, it is not well understood how this fronto-parieto-temporal network can be modulated by different semantic tasks and different semantic relations between nouns. In this study, we combine a behavioral semantic network, functional MRI studies involving object’s related nouns and brain network studies to explain how different semantic tasks and different semantic relations between nouns can modulate the activity within the brain network of nouns and their features. We first describe how nouns and their features form a large scale brain network. For this end, we examine the connectivities between areas recruited during the processing of nouns to know which configurations of interaction areas are possible. We can thus identify if, for example, brain areas that store semantic knowledge communicate via functional/structural links with areas that store lexical representations. Second, we examine how this network is modulated by different semantic tasks involving nouns and finally, we examine how category specific activation may result from the semantic relations among nouns. The results indicate that brain network of nouns and their features is highly modulated and flexible by different semantic tasks and semantic relations. At the end, this study can be used as a guide to help neurosientifics to interpret the pattern of fMRI activations detected in the semantic processing of nouns. Specifically; this study can help to interpret the category specific activations observed extensively in a large number of neuroimaging studies and clinical studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nouns" title="nouns">nouns</a>, <a href="https://publications.waset.org/abstracts/search?q=features" title=" features"> features</a>, <a href="https://publications.waset.org/abstracts/search?q=network" title=" network"> network</a>, <a href="https://publications.waset.org/abstracts/search?q=category%20specificity" title=" category specificity"> category specificity</a> </p> <a href="https://publications.waset.org/abstracts/18889/a-network-of-nouns-and-their-features-a-neurocomputational-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18889.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">521</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4091</span> Lexico-Semantic and Contextual Analysis of the Concept of Joy in Modern English Fiction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zarine%20Avetisyan">Zarine Avetisyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Concepts are part and parcel of everyday text and talk. Their ubiquity predetermines the topicality of the given research which aims at the semantic decomposition of concepts in general and the concept of joy in particular, as well as the study of lexico-semantic variants as means of realization of a certain concept in different “semantic settings”, namely in a certain context. To achieve the stated aim, the given research departs from the methods of componential and contextual analysis, studying lexico-semantic variants /LSVs/ of the concept of joy and the semantic signs embedded in those LSVs, such as the semantic sign of intensity, supporting emotions, etc. in the context of Modern English fiction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concept" title="concept">concept</a>, <a href="https://publications.waset.org/abstracts/search?q=context" title=" context"> context</a>, <a href="https://publications.waset.org/abstracts/search?q=lexico-semantic%20variant" title=" lexico-semantic variant"> lexico-semantic variant</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20sign" title=" semantic sign"> semantic sign</a> </p> <a href="https://publications.waset.org/abstracts/67474/lexico-semantic-and-contextual-analysis-of-the-concept-of-joy-in-modern-english-fiction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67474.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4090</span> Effect of Semantic Relational Cues in Action Memory Performance over School Ages </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farzaneh%20Badinlou">Farzaneh Badinlou</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Kormi-Nouri"> Reza Kormi-Nouri</a>, <a href="https://publications.waset.org/abstracts/search?q=Monika%20Knopf"> Monika Knopf</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamal%20Kharazi"> Kamal Kharazi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Research into long-term memory has demonstrated that the richness of the knowledge base cues in memory tasks improves retrieval process, which in turn influences learning and memory performance. The present research investigated the idea that adding cues connected to knowledge can affect memory performance in the context of action memory in children. In action memory studies, participants are instructed to learn a series of verb–object phrases as verbal learning and experience-based learning (learning by doing and learning by observation). It is well established that executing action phrases is a more memorable way to learn than verbally repeating the phrases, a finding called enactment effect. In the present study, a total of 410 students from four grade groups—2nd, 4th, 6th, and 8th—participated in this study. During the study, participants listened to verbal action phrases (VTs), performed the phrases (SPTs: subject-performed tasks), and observed the experimenter perform the phrases (EPTs: experimenter-performed tasks). During the test phase, cued recall test was administered. Semantic relational cues (i.e., well-integrated vs. poorly integrated items) were manipulated in the present study. In that, the participants were presented two lists of action phrases with high semantic integration between verb and noun, e.g., “write with the pen” and with low semantic integration between verb and noun, e.g., “pick up the glass”. Results revealed that experience-based learning had a better results than verbal learning for both well-integrated and poorly integrated items, though manipulations of semantic relational cues can moderate the enactment effect. In addition, children of different grade groups outperformed for well- than poorly integrated items, in flavour of older children. The results were discussed in relation to the effect of knowledge-based information in facilitating retrieval process in children. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=action%20memory" title="action memory">action memory</a>, <a href="https://publications.waset.org/abstracts/search?q=enactment%20effect" title=" enactment effect"> enactment effect</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge-based%20cues" title=" knowledge-based cues"> knowledge-based cues</a>, <a href="https://publications.waset.org/abstracts/search?q=school-aged%20children" title=" school-aged children"> school-aged children</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20relational%20cues" title=" semantic relational cues"> semantic relational cues</a> </p> <a href="https://publications.waset.org/abstracts/92008/effect-of-semantic-relational-cues-in-action-memory-performance-over-school-ages" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92008.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">275</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4089</span> Lexical-Semantic Processing by Chinese as a Second Language Learners</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Hsiu%20Lai">Yi-Hsiu Lai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study aimed to elucidate the lexical-semantic processing for Chinese as second language (CSL) learners. Twenty L1 speakers of Chinese and twenty CSL learners in Taiwan participated in a picture naming task and a category fluency task. Based on their Chinese proficiency levels, these CSL learners were further divided into two sub-groups: ten CSL learners of elementary Chinese proficiency level and ten CSL learners of intermediate Chinese proficiency level. Instruments for the naming task were sixty black-and-white pictures: thirty-five object pictures and twenty-five action pictures. Object pictures were divided into two categories: living objects and non-living objects. Action pictures were composed of two categories: action verbs and process verbs. As in the naming task, the category fluency task consisted of two semantic categories – objects (i.e., living and non-living objects) and actions (i.e., action and process verbs). Participants were asked to report as many items within a category as possible in one minute. Oral productions were tape-recorded and transcribed for further analysis. Both error types and error frequency were calculated. Statistical analysis was further conducted to examine these error types and frequency made by CSL learners. Additionally, category effects, pictorial effects and L2 proficiency were discussed. Findings in the present study helped characterize the lexical-semantic process of Chinese naming in CSL learners of different Chinese proficiency levels and made contributions to Chinese vocabulary teaching and learning in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lexical-semantic%20processing" title="lexical-semantic processing">lexical-semantic processing</a>, <a href="https://publications.waset.org/abstracts/search?q=Mandarin%20Chinese" title=" Mandarin Chinese"> Mandarin Chinese</a>, <a href="https://publications.waset.org/abstracts/search?q=naming" title=" naming"> naming</a>, <a href="https://publications.waset.org/abstracts/search?q=category%20effects" title=" category effects "> category effects </a> </p> <a href="https://publications.waset.org/abstracts/43848/lexical-semantic-processing-by-chinese-as-a-second-language-learners" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43848.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">462</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4088</span> Semantic Processing in Chinese: Category Effects, Task Effects and Age Effects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Hsiu%20Lai">Yi-Hsiu Lai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study aimed to elucidate the nature of semantic processing in Chinese. Language and cognition related to the issue of aging are examined from the perspective of picture naming and category fluency tasks. Twenty Chinese-speaking adults (ranging from 25 to 45 years old) and twenty Chinese-speaking seniors (ranging from 65 to 75 years old) in Taiwan participated in this study. Each of them individually completed two tasks: a picture naming task and a category fluency task. Instruments for the naming task were sixty black-and-white pictures: thirty-five object and twenty-five action pictures. Category fluency task also consisted of two semantic categories – objects (or nouns) and actions (or verbs). Participants were asked to report as many items within a category as possible in one minute. Scores of action fluency and of object fluency were a summation of correct responses in these two categories. Category effects (actions vs. objects) and age effects were examined in these tasks. Objects were further divided into two major types: living objects and non-living objects. Actions were also categorized into two major types: action verbs and process verbs. Reaction time to each picture/question was additionally calculated and analyzed. Results of the category fluency task indicated that the content of information in Chinese seniors was comparatively deteriorated, thus producing smaller number of semantic-lexical items. Significant group difference was also found in the results of reaction time. Category Effect was significant for both Chinese adults and seniors in the semantic fluency task. Findings in the present study helped characterize the nature of semantic processing in Chinese-speaking adults and seniors and contributed to the issue of language and aging. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20processing" title="semantic processing">semantic processing</a>, <a href="https://publications.waset.org/abstracts/search?q=aging" title=" aging"> aging</a>, <a href="https://publications.waset.org/abstracts/search?q=Chinese" title=" Chinese"> Chinese</a>, <a href="https://publications.waset.org/abstracts/search?q=category%20effects" title=" category effects"> category effects</a> </p> <a href="https://publications.waset.org/abstracts/43216/semantic-processing-in-chinese-category-effects-task-effects-and-age-effects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43216.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">361</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4087</span> Reverse Logistics Information Management Using Ontological Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Lhafiane">F. Lhafiane</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Elbyed"> A. Elbyed</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Bouchoum"> M. Bouchoum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reverse Logistics (RL) Process is considered as complex and dynamic network that involves many stakeholders such as: suppliers, manufactures, warehouse, retails, and costumers, this complexity is inherent in such process due to lack of perfect knowledge or conflicting information. Ontologies, on the other hand, can be considered as an approach to overcome the problem of sharing knowledge and communication among the various reverse logistics partners. In this paper, we propose a semantic representation based on hybrid architecture for building the Ontologies in an ascendant way, this method facilitates the semantic reconciliation between the heterogeneous information systems (ICT) that support reverse logistics Processes and product data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reverse%20Logistics" title="Reverse Logistics">Reverse Logistics</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20management" title=" information management"> information management</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneity" title=" heterogeneity"> heterogeneity</a>, <a href="https://publications.waset.org/abstracts/search?q=ontologies" title=" ontologies"> ontologies</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20web" title=" semantic web"> semantic web</a> </p> <a href="https://publications.waset.org/abstracts/23720/reverse-logistics-information-management-using-ontological-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23720.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">492</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4086</span> An Ontology-Based Framework to Support Asset Integrity Modeling: Case Study of Offshore Riser Integrity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Sheikhalishahi">Mohammad Sheikhalishahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Ebrahimipour"> Vahid Ebrahimipour</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Hossein%20Radman-Kian"> Amir Hossein Radman-Kian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes an Ontology framework for knowledge modeling and representation of the equipment integrity process in a typical oil and gas production plant. Our aim is to construct a knowledge modeling that facilitates translation, interpretation, and conversion of human-readable integrity interpretation into computer-readable representation. The framework provides a function structure related to fault propagation using ISO 14224 and ISO 15926 OWL-Lite/ Resource Description Framework (RDF) to obtain a generic system-level model of asset integrity that can be utilized in the integrity engineering process during the equipment life cycle. It employs standard terminology developed by ISO 15926 and ISO 14224 to map textual descriptions of equipment failure and then convert it to a causality-driven logic by semantic interpretation and computer-based representation using Lite/RDF. The framework applied for an offshore gas riser. The result shows that the approach can cross-link the failure-related integrity words and domain-specific logic to obtain a representation structure of equipment integrity with causality inference based on semantic extraction of inspection report context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asset%20integrity%20modeling" title="asset integrity modeling">asset integrity modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=interoperability" title=" interoperability"> interoperability</a>, <a href="https://publications.waset.org/abstracts/search?q=OWL" title=" OWL"> OWL</a>, <a href="https://publications.waset.org/abstracts/search?q=RDF%2FXML" title=" RDF/XML"> RDF/XML</a> </p> <a href="https://publications.waset.org/abstracts/131416/an-ontology-based-framework-to-support-asset-integrity-modeling-case-study-of-offshore-riser-integrity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131416.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4085</span> Fuzzy Semantic Annotation of Web Resources </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sahar%20Ma%C3%A2lej%20Dammak">Sahar Maâlej Dammak</a>, <a href="https://publications.waset.org/abstracts/search?q=Anis%20Jedidi"> Anis Jedidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafik%20Bouaziz"> Rafik Bouaziz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the great mass of pages managed through the world, and especially with the advent of the Web, their manual annotation is impossible. We focus, in this paper, on the semiautomatic annotation of the web pages. We propose an approach and a framework for semantic annotation of web pages entitled “Querying Web”. Our solution is an enhancement of the first result of annotation done by the “Semantic Radar” Plug-in on the web resources, by annotations using an enriched domain ontology. The concepts of the result of Semantic Radar may be connected to several terms of the ontology, but connections may be uncertain. We represent annotations as possibility distributions. We use the hierarchy defined in the ontology to compute degrees of possibilities. We want to achieve an automation of the fuzzy semantic annotation of web resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20semantic%20annotation" title="fuzzy semantic annotation">fuzzy semantic annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20web" title=" semantic web"> semantic web</a>, <a href="https://publications.waset.org/abstracts/search?q=domain%20ontologies" title=" domain ontologies"> domain ontologies</a>, <a href="https://publications.waset.org/abstracts/search?q=querying%20web" title=" querying web"> querying web</a> </p> <a href="https://publications.waset.org/abstracts/1854/fuzzy-semantic-annotation-of-web-resources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1854.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">374</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4084</span> A Study on Bilingual Semantic Processing: Category Effects and Age Effects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lai%20Yi-Hsiu">Lai Yi-Hsiu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study addressed the nature of bilingual semantic processing in Mandarin Chinese and Southern Min and examined category effects and age effects. Nineteen bilingual adults of Mandarin Chinese and Southern Min, nine monolingual seniors of Mandarin Chinese, and ten monolingual seniors of Southern Min in Taiwan individually completed two semantic tasks: Picture naming and category fluency tasks. The instruments for the naming task were sixty black-and-white pictures, including thirty-five object pictures and twenty-five action pictures. The category fluency task also consisted of two semantic categories – objects (or nouns) and actions (or verbs). The reaction time for each picture/question was additionally calculated and analyzed. Oral productions in Mandarin Chinese and in Southern Min were compared and discussed to examine the category effects and age effects. The results of the category fluency task indicated that the content of information of these seniors was comparatively deteriorated, and thus they produced a smaller number of semantic-lexical items. Significant group differences were also found in the reaction time results. Category effects were significant for both adults and seniors in the semantic fluency task. The findings of the present study will help characterize the nature of the bilingual semantic processing of adults and seniors, and contribute to the fields of contrastive and corpus linguistics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bilingual%20semantic%20processing" title="bilingual semantic processing">bilingual semantic processing</a>, <a href="https://publications.waset.org/abstracts/search?q=aging" title=" aging"> aging</a>, <a href="https://publications.waset.org/abstracts/search?q=Mandarin%20Chinese" title=" Mandarin Chinese"> Mandarin Chinese</a>, <a href="https://publications.waset.org/abstracts/search?q=Southern%20Min" title=" Southern Min"> Southern Min</a> </p> <a href="https://publications.waset.org/abstracts/43219/a-study-on-bilingual-semantic-processing-category-effects-and-age-effects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43219.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">571</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4083</span> Network Word Discovery Framework Based on Sentence Semantic Vector Similarity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ganfeng%20Yu">Ganfeng Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuefeng%20Ma"> Yuefeng Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanliang%20Yang"> Shanliang Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The word discovery is a key problem in text information retrieval technology. Methods in new word discovery tend to be closely related to words because they generally obtain new word results by analyzing words. With the popularity of social networks, individual netizens and online self-media have generated various network texts for the convenience of online life, including network words that are far from standard Chinese expression. How detect network words is one of the important goals in the field of text information retrieval today. In this paper, we integrate the word embedding model and clustering methods to propose a network word discovery framework based on sentence semantic similarity (S³-NWD) to detect network words effectively from the corpus. This framework constructs sentence semantic vectors through a distributed representation model, uses the similarity of sentence semantic vectors to determine the semantic relationship between sentences, and finally realizes network word discovery by the meaning of semantic replacement between sentences. The experiment verifies that the framework not only completes the rapid discovery of network words but also realizes the standard word meaning of the discovery of network words, which reflects the effectiveness of our work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20information%20retrieval" title="text information retrieval">text information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=new%20word%20discovery" title=" new word discovery"> new word discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20extraction" title=" information extraction"> information extraction</a> </p> <a href="https://publications.waset.org/abstracts/153917/network-word-discovery-framework-based-on-sentence-semantic-vector-similarity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153917.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">95</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4082</span> Optimization Query Image Using Search Relevance Re-Ranking Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20G.%20Asmitha%20Chandini">T. G. Asmitha Chandini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Web-based image search re-ranking, as an successful method to get better the results. In a query keyword, the first stair is store the images is first retrieve based on the text-based information. The user to select a query keywordimage, by using this query keyword other images are re-ranked based on their visual properties with images.Now a day to day, people projected to match images in a semantic space which is used attributes or reference classes closely related to the basis of semantic image. though, understanding a worldwide visual semantic space to demonstrate highly different images from the web is difficult and inefficient. The re-ranking images, which automatically offline part learns dissimilar semantic spaces for different query keywords. The features of images are projected into their related semantic spaces to get particular images. At the online stage, images are re-ranked by compare their semantic signatures obtained the semantic précised by the query keyword image. The query-specific semantic signatures extensively improve both the proper and efficiency of image re-ranking. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Query" title="Query">Query</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword" title=" keyword"> keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/abstracts/search?q=re-ranking" title=" re-ranking"> re-ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic" title=" semantic"> semantic</a>, <a href="https://publications.waset.org/abstracts/search?q=signature" title=" signature"> signature</a> </p> <a href="https://publications.waset.org/abstracts/28398/optimization-query-image-using-search-relevance-re-ranking-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28398.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">549</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4081</span> Semantic Indexing Improvement for Textual Documents: Contribution of Classification by Fuzzy Association Rules</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Maraoui">Mohsen Maraoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the aim of natural language processing applications improvement, such as information retrieval, machine translation, lexical disambiguation, we focus on statistical approach to semantic indexing for multilingual text documents based on conceptual network formalism. We propose to use this formalism as an indexing language to represent the descriptive concepts and their weighting. These concepts represent the content of the document. Our contribution is based on two steps. In the first step, we propose the extraction of index terms using the multilingual lexical resource Euro WordNet (EWN). In the second step, we pass from the representation of index terms to the representation of index concepts through conceptual network formalism. This network is generated using the EWN resource and pass by a classification step based on association rules model (in attempt to discover the non-taxonomic relations or contextual relations between the concepts of a document). These relations are latent relations buried in the text and carried by the semantic context of the co-occurrence of concepts in the document. Our proposed indexing approach can be applied to text documents in various languages because it is based on a linguistic method adapted to the language through a multilingual thesaurus. Next, we apply the same statistical process regardless of the language in order to extract the significant concepts and their associated weights. We prove that the proposed indexing approach provides encouraging results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concept%20extraction" title="concept extraction">concept extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=conceptual%20network%20formalism" title=" conceptual network formalism"> conceptual network formalism</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20association%20rules" title=" fuzzy association rules"> fuzzy association rules</a>, <a href="https://publications.waset.org/abstracts/search?q=multilingual%20thesaurus" title=" multilingual thesaurus"> multilingual thesaurus</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20indexing" title=" semantic indexing"> semantic indexing</a> </p> <a href="https://publications.waset.org/abstracts/98854/semantic-indexing-improvement-for-textual-documents-contribution-of-classification-by-fuzzy-association-rules" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98854.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4080</span> Challenges over Two Semantic Repositories - OWLIM and AllegroGraph</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paria%20Tajabor">Paria Tajabor</a>, <a href="https://publications.waset.org/abstracts/search?q=Azin%20Azarbani"> Azin Azarbani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this research study is exploring two kind of semantic repositories with regards to various factors to find the best approaches that an artificial manager can use to produce ontology in a system based on their interaction, association and research. To this end, as the best way to evaluate each system and comparing with others is analysis, several benchmarking over these two repositories were examined. These two semantic repositories: OWLIM and AllegroGraph will be the main core of this study. The general objective of this study is to be able to create an efficient and cost-effective manner reports which is required to support decision making in any large enterprise. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OWLIM" title="OWLIM">OWLIM</a>, <a href="https://publications.waset.org/abstracts/search?q=allegrograph" title=" allegrograph"> allegrograph</a>, <a href="https://publications.waset.org/abstracts/search?q=RDF" title=" RDF"> RDF</a>, <a href="https://publications.waset.org/abstracts/search?q=reasoning" title=" reasoning"> reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20repository" title=" semantic repository"> semantic repository</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic-web" title=" semantic-web"> semantic-web</a>, <a href="https://publications.waset.org/abstracts/search?q=SPARQL" title=" SPARQL"> SPARQL</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=query" title=" query"> query</a> </p> <a href="https://publications.waset.org/abstracts/41697/challenges-over-two-semantic-repositories-owlim-and-allegrograph" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41697.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">262</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4079</span> A Semantic E-Learning and E-Assessment System of Learners </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wiem%20Ben%20Khalifa">Wiem Ben Khalifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Dalila%20Souilem"> Dalila Souilem</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Neji"> Mahmoud Neji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The evolutions of Social Web and Semantic Web lead us to ask ourselves about the way of supporting the personalization of learning by means of intelligent filtering of educational resources published in the digital networks. We recommend personalized courses of learning articulated around a first educational course defined upstream. Resuming the context and the stakes in the personalization, we also suggest anchoring the personalization of learning in a community of interest within a group of learners enrolled in the same training. This reflection is supported by the display of an active and semantic system of learning dedicated to the constitution of personalized to measure courses and in the due time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Semantic%20Web" title="Semantic Web">Semantic Web</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20system" title=" semantic system"> semantic system</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=evaluation" title=" evaluation"> evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=e-learning" title=" e-learning"> e-learning</a> </p> <a href="https://publications.waset.org/abstracts/72932/a-semantic-e-learning-and-e-assessment-system-of-learners" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72932.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">334</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4078</span> Assessing the Structure of Non-Verbal Semantic Knowledge: The Evaluation and First Results of the Hungarian Semantic Association Test</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alinka%20Moln%C3%A1r-T%C3%B3th">Alinka Molnár-Tóth</a>, <a href="https://publications.waset.org/abstracts/search?q=T%C3%ADmea%20T%C3%A1nczos"> Tímea Tánczos</a>, <a href="https://publications.waset.org/abstracts/search?q=Regina%20Barna"> Regina Barna</a>, <a href="https://publications.waset.org/abstracts/search?q=Katalin%20Jakab"> Katalin Jakab</a>, <a href="https://publications.waset.org/abstracts/search?q=P%C3%A9ter%20Kliv%C3%A9nyi"> Péter Klivényi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Supported by neuroscientific findings, the so-called Hub-and-Spoke model of the human semantic system is based on two subcomponents of semantic cognition, namely the semantic control process and semantic representation. Our semantic knowledge is multimodal in nature, as the knowledge system stored in relation to a conception is extensive and broad, while different aspects of the conception may be relevant depending on the purpose. The motivation of our research is to develop a new diagnostic measurement procedure based on the preservation of semantic representation, which is appropriate to the specificities of the Hungarian language and which can be used to compare the non-verbal semantic knowledge of healthy and aphasic persons. The development of the test will broaden the Hungarian clinical diagnostic toolkit, which will allow for more specific therapy planning. The sample of healthy persons (n=480) was determined by the last census data for the representativeness of the sample. Based on the concept of the Pyramids and Palm Tree Test, and according to the characteristics of the Hungarian language, we have elaborated a test based on different types of semantic information, in which the subjects are presented with three pictures: they have to choose the one that best fits the target word above from the two lower options, based on the semantic relation defined. We have measured 5 types of semantic knowledge representations: associative relations, taxonomy, motional representations, concrete as well as abstract verbs. As the first step in our data analysis, we examined the normal distribution of our results, and since it was not normally distributed (p < 0.05), we used nonparametric statistics further into the analysis. Using descriptive statistics, we could determine the frequency of the correct and incorrect responses, and with this knowledge, we could later adjust and remove the items of questionable reliability. The reliability was tested using Cronbach’s α, and it can be safely said that all the results were in an acceptable range of reliability (α = 0.6-0.8). We then tested for the potential gender differences using the Mann Whitney-U test, however, we found no difference between the two (p < 0.05). Likewise, we didn’t see that the age had any effect on the results using one-way ANOVA (p < 0.05), however, the level of education did influence the results (p > 0.05). The relationships between the subtests were observed by the nonparametric Spearman’s rho correlation matrix, showing statistically significant correlation between the subtests (p > 0.05), signifying a linear relationship between the measured semantic functions. A margin of error of 5% was used in all cases. The research will contribute to the expansion of the clinical diagnostic toolkit and will be relevant for the individualised therapeutic design of treatment procedures. The use of a non-verbal test procedure will allow an early assessment of the most severe language conditions, which is a priority in the differential diagnosis. The measurement of reaction time is expected to advance prodrome research, as the tests can be easily conducted in the subclinical phase. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communication%20disorders" title="communication disorders">communication disorders</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnostic%20toolkit" title=" diagnostic toolkit"> diagnostic toolkit</a>, <a href="https://publications.waset.org/abstracts/search?q=neurorehabilitation" title=" neurorehabilitation"> neurorehabilitation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20knowlegde" title=" semantic knowlegde"> semantic knowlegde</a> </p> <a href="https://publications.waset.org/abstracts/167747/assessing-the-structure-of-non-verbal-semantic-knowledge-the-evaluation-and-first-results-of-the-hungarian-semantic-association-test" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167747.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4077</span> Automated Adaptions of Semantic User- and Service Profile Representations by Learning the User Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nicole%20Merkle">Nicole Merkle</a>, <a href="https://publications.waset.org/abstracts/search?q=Stefan%20Zander"> Stefan Zander</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ambient Assisted Living (AAL) describes a technological and methodological stack of (e.g. formal model-theoretic semantics, rule-based reasoning and machine learning), different aspects regarding the behavior, activities and characteristics of humans. Hence, a semantic representation of the user environment and its relevant elements are required in order to allow assistive agents to recognize situations and deduce appropriate actions. Furthermore, the user and his/her characteristics (e.g. physical, cognitive, preferences) need to be represented with a high degree of expressiveness in order to allow software agents a precise evaluation of the users’ context models. The correct interpretation of these context models highly depends on temporal, spatial circumstances as well as individual user preferences. In most AAL approaches, model representations of real world situations represent the current state of a universe of discourse at a given point in time by neglecting transitions between a set of states. However, the AAL domain currently lacks sufficient approaches that contemplate on the dynamic adaptions of context-related representations. Semantic representations of relevant real-world excerpts (e.g. user activities) help cognitive, rule-based agents to reason and make decisions in order to help users in appropriate tasks and situations. Furthermore, rules and reasoning on semantic models are not sufficient for handling uncertainty and fuzzy situations. A certain situation can require different (re-)actions in order to achieve the best results with respect to the user and his/her needs. But what is the best result? To answer this question, we need to consider that every smart agent requires to achieve an objective, but this objective is mostly defined by domain experts who can also fail in their estimation of what is desired by the user and what not. Hence, a smart agent has to be able to learn from context history data and estimate or predict what is most likely in certain contexts. Furthermore, different agents with contrary objectives can cause collisions as their actions influence the user’s context and constituting conditions in unintended or uncontrolled ways. We present an approach for dynamically updating a semantic model with respect to the current user context that allows flexibility of the software agents and enhances their conformance in order to improve the user experience. The presented approach adapts rules by learning sensor evidence and user actions using probabilistic reasoning approaches, based on given expert knowledge. The semantic domain model consists basically of device-, service- and user profile representations. In this paper, we present how this semantic domain model can be used in order to compute the probability of matching rules and actions. We apply this probability estimation to compare the current domain model representation with the computed one in order to adapt the formal semantic representation. Our approach aims at minimizing the likelihood of unintended interferences in order to eliminate conflicts and unpredictable side-effects by updating pre-defined expert knowledge according to the most probable context representation. This enables agents to adapt to dynamic changes in the environment which enhances the provision of adequate assistance and affects positively the user satisfaction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ambient%20intelligence" title="ambient intelligence">ambient intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20web" title=" semantic web"> semantic web</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20agents" title=" software agents"> software agents</a> </p> <a href="https://publications.waset.org/abstracts/61800/automated-adaptions-of-semantic-user-and-service-profile-representations-by-learning-the-user-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61800.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">281</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4076</span> Secure Bio Semantic Computing Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hiroshi%20Yamaguchi">Hiroshi Yamaguchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Phillip%20C.%20Y.%20Sheu"> Phillip C. Y. Sheu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ryo%20Fujita"> Ryo Fujita</a>, <a href="https://publications.waset.org/abstracts/search?q=Shigeo%20Tsujii"> Shigeo Tsujii</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the secure BioSemantic Scheme is presented to bridge biological/biomedical research problems and computational solutions via semantic computing. Due to the diversity of problems in various research fields, the semantic capability description language (SCDL) plays and important role as a common language and generic form for problem formalization. SCDL is expected the essential for future semantic and logical computing in Biosemantic field. We show several example to Biomedical problems in this paper. Moreover, in the coming age of cloud computing, the security problem is considered to be crucial issue and we presented a practical scheme to cope with this problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomedical%20applications" title="biomedical applications">biomedical applications</a>, <a href="https://publications.waset.org/abstracts/search?q=private%20information%20retrieval%20%28PIR%29" title=" private information retrieval (PIR)"> private information retrieval (PIR)</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20capability%20description%20language%20%28SCDL%29" title=" semantic capability description language (SCDL)"> semantic capability description language (SCDL)</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20computing" title=" semantic computing"> semantic computing</a> </p> <a href="https://publications.waset.org/abstracts/27808/secure-bio-semantic-computing-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27808.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">390</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4075</span> Investigating the Concept of Joy in Modern English Fiction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zarine%20Avetisyan">Zarine Avetisyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paradigm of Modern Linguistics incorporates disciplines which allow to analyze both language and discourse units and to demonstrate the multi-layeredness of lingo-cultural consciousness. By implementing lingo-cognitive approach to discourse and communication studies, the present paper tries to create the integral linguistic picture of the concept of joy and to analyze the lexico-semantic groups and relevant lexico-semantic variants of its realization in the context of Modern English fiction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concept%20of%20joy" title="concept of joy">concept of joy</a>, <a href="https://publications.waset.org/abstracts/search?q=lexico-semantic%20variant" title=" lexico-semantic variant"> lexico-semantic variant</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20sign" title=" semantic sign"> semantic sign</a>, <a href="https://publications.waset.org/abstracts/search?q=cognition" title=" cognition"> cognition</a> </p> <a href="https://publications.waset.org/abstracts/50821/investigating-the-concept-of-joy-in-modern-english-fiction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50821.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4074</span> Exploring Syntactic and Semantic Features for Text-Based Authorship Attribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haiyan%20Wu">Haiyan Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying%20Liu"> Ying Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaoyun%20Shi"> Shaoyun Shi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=authorship%20attribution" title="authorship attribution">authorship attribution</a>, <a href="https://publications.waset.org/abstracts/search?q=attention%20mechanism" title=" attention mechanism"> attention mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=syntactic%20feature" title=" syntactic feature"> syntactic feature</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/129270/exploring-syntactic-and-semantic-features-for-text-based-authorship-attribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129270.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4073</span> Graph Planning Based Composition for Adaptable Semantic Web Services</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rihab%20Ben%20Lamine">Rihab Ben Lamine</a>, <a href="https://publications.waset.org/abstracts/search?q=Raoudha%20Ben%20Jemaa"> Raoudha Ben Jemaa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ikram%20Amous%20Ben%20Amor"> Ikram Amous Ben Amor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a graph planning technique for semantic adaptable Web Services composition. First, we use an ontology based context model for extending Web Services descriptions with information about the most suitable context for its use. Then, we transform the composition problem into a semantic context aware graph planning problem to build the optimal service composition based on user's context. The construction of the planning graph is based on semantic context aware Web Service discovery that allows for each step to add most suitable Web Services in terms of semantic compatibility between the services parameters and their context similarity with the user's context. In the backward search step, semantic and contextual similarity scores are used to find best composed Web Services list. Finally, in the ranking step, a score is calculated for each best solution and a set of ranked solutions is returned to the user. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20web%20service" title="semantic web service">semantic web service</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20service%20composition" title=" web service composition"> web service composition</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptation" title=" adaptation"> adaptation</a>, <a href="https://publications.waset.org/abstracts/search?q=context" title=" context"> context</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20planning" title=" graph planning"> graph planning</a> </p> <a href="https://publications.waset.org/abstracts/62455/graph-planning-based-composition-for-adaptable-semantic-web-services" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62455.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">520</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=semantic%20action%20representation&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=semantic%20action%20representation&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=semantic%20action%20representation&page=4">4</a></li> <li class="page-item"><a class="page-link" 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