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search</title> <meta name="description" content="Search results for: semantic search"> <meta name="keywords" content="semantic search"> <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> <button class="d-block d-lg-none navbar-toggler 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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 search"> <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> 2376</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: semantic search</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2376</span> Arabic Quran Search Tool Based on Ontology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Alqahtani">Mohammad Alqahtani</a>, <a href="https://publications.waset.org/abstracts/search?q=Eric%20Atwell"> Eric Atwell</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper reviews and classifies most of the important types of search techniques that have been applied on the holy Quran. Then, it addresses the limitations in these techniques. Additionally, this paper surveys most existing Quranic ontologies and what are their deficiencies. Finally, it explains a new search tool called: A semantic search tool for Al Quran based on Qur’anic ontologies. This tool will overcome all limitations in the existing Quranic search applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=holy%20Quran" title="holy Quran">holy Quran</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing%20%28NLP%29" title=" natural language processing (NLP)"> natural language processing (NLP)</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20search" title=" semantic search"> semantic search</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval%20%28IR%29" title=" information retrieval (IR)"> information retrieval (IR)</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a> </p> <a href="https://publications.waset.org/abstracts/31315/arabic-quran-search-tool-based-on-ontology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31315.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">572</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">2375</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">552</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">2374</span> Enhanced Arabic Semantic Information Retrieval System Based on Arabic Text Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Elsehemy">A. Elsehemy</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Abdeen"> M. Abdeen </a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Nazmy"> T. Nazmy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since the appearance of the Semantic web, many semantic search techniques and models were proposed to exploit the information in ontology to enhance the traditional keyword-based search. Many advances were made in languages such as English, German, French and Spanish. However, other languages such as Arabic are not fully supported yet. In this paper we present a framework for ontology based information retrieval for Arabic language. Our system consists of four main modules, namely query parser, indexer, search and a ranking module. Our approach includes building a semantic index by linking ontology concepts to documents, including an annotation weight for each link, to be used in ranking the results. We also augmented the framework with an automatic document categorizer, which enhances the overall document ranking. We have built three Arabic domain ontologies: Sports, Economic and Politics as example for the Arabic language. We built a knowledge base that consists of 79 classes and more than 1456 instances. The system is evaluated using the precision and recall metrics. We have done many retrieval operations on a sample of 40,316 documents with a size 320 MB of pure text. The results show that the semantic search enhanced with text classification gives better performance results than the system without classification. <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=ontology%20based%20retrieval" title=" ontology based retrieval"> ontology based retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic%20semantic%20web" title=" Arabic semantic web"> Arabic semantic web</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic%20ontology" title=" Arabic ontology"> Arabic ontology</a> </p> <a href="https://publications.waset.org/abstracts/34945/enhanced-arabic-semantic-information-retrieval-system-based-on-arabic-text-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34945.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">525</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">2373</span> Semantic Search Engine Based on Query Expansion with Google Ranking and Similarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Shahin">Ahmad Shahin</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadi%20Chakik"> Fadi Chakik</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20Moudani"> Walid Moudani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our study is about elaborating a potential solution for a search engine that involves semantic technology to retrieve information and display it significantly. Semantic search engines are not used widely over the web as the majorities are still in Beta stage or under construction. Many problems face the current applications in semantic search, the major problem is to analyze and calculate the meaning of query in order to retrieve relevant information. Another problem is the ontology based index and its updates. Ranking results according to concept meaning and its relation with query is another challenge. In this paper, we are offering a light meta-engine (QESM) which uses Google search, and therefore Google’s index, with some adaptations to its returned results by adding multi-query expansion. The mission was to find a reliable ranking algorithm that involves semantics and uses concepts and meanings to rank results. At the beginning, the engine finds synonyms of each query term entered by the user based on a lexical database. Then, query expansion is applied to generate different semantically analogous sentences. These are generated randomly by combining the found synonyms and the original query terms. Our model suggests the use of semantic similarity measures between two sentences. Practically, we used this method to calculate semantic similarity between each query and the description of each page’s content generated by Google. The generated sentences are sent to Google engine one by one, and ranked again all together with the adapted ranking method (QESM). Finally, our system will place Google pages with higher similarities on the top of the results. We have conducted experimentations with 6 different queries. We have observed that most ranked results with QESM were altered with Google’s original generated pages. With our experimented queries, QESM generates frequently better accuracy than Google. In some worst cases, it behaves like Google. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20search%20engine" title="semantic search engine">semantic search engine</a>, <a href="https://publications.waset.org/abstracts/search?q=Google%20indexing" title=" Google indexing"> Google indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20expansion" title=" query expansion"> query expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measures" title=" similarity measures"> similarity measures</a> </p> <a href="https://publications.waset.org/abstracts/10857/semantic-search-engine-based-on-query-expansion-with-google-ranking-and-similarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10857.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">425</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">2372</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">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">2371</span> A Method of the Semantic on Image Auto-Annotation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lin%20Huo">Lin Huo</a>, <a href="https://publications.waset.org/abstracts/search?q=Xianwei%20Liu"> Xianwei Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jingxiong%20Zhou"> Jingxiong Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, due to the existence of semantic gap between image visual features and human concepts, the semantic of image auto-annotation has become an important topic. Firstly, by extract low-level visual features of the image, and the corresponding Hash method, mapping the feature into the corresponding Hash coding, eventually, transformed that into a group of binary string and store it, image auto-annotation by search is a popular method, we can use it to design and implement a method of image semantic auto-annotation. Finally, Through the test based on the Corel image set, and the results show that, this method is effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20auto-annotation" title="image auto-annotation">image auto-annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20correlograms" title=" color correlograms"> color correlograms</a>, <a href="https://publications.waset.org/abstracts/search?q=Hash%20code" title=" Hash code"> Hash code</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval" title=" image retrieval"> image retrieval</a> </p> <a href="https://publications.waset.org/abstracts/15628/a-method-of-the-semantic-on-image-auto-annotation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15628.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">497</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">2370</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">2369</span> Modified Active (MA) Algorithm to Generate Semantic Web Related Clustered Hierarchy for Keyword Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Leena%20Giri">G. Leena Giri</a>, <a href="https://publications.waset.org/abstracts/search?q=Archana%20Mathur"> Archana Mathur</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Manjula"> S. H. Manjula</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20R.%20Venugopal"> K. R. Venugopal</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20M.%20Patnaik"> L. M. Patnaik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Keyword search in XML documents is based on the notion of lowest common ancestors in the labelled trees model of XML documents and has recently gained a lot of research interest in the database community. In this paper, we propose the Modified Active (MA) algorithm which is an improvement over the active clustering algorithm by taking into consideration the entity aspect of the nodes to find the level of the node pertaining to a particular keyword input by the user. A portion of the bibliography database is used to experimentally evaluate the modified active algorithm and results show that it performs better than the active algorithm. Our modification improves the response time of the system and thereby increases the efficiency of the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword%20matching%20patterns" title="keyword matching patterns">keyword matching patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=MA%20algorithm" title=" MA algorithm"> MA algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20search" title=" semantic search"> semantic search</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a> </p> <a href="https://publications.waset.org/abstracts/6608/modified-active-ma-algorithm-to-generate-semantic-web-related-clustered-hierarchy-for-keyword-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6608.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">413</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">2368</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">2367</span> A Context-Sensitive Algorithm for Media Similarity Search </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guang-Ho%20Cha">Guang-Ho Cha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a context-sensitive media similarity search algorithm. One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. Many media search algorithms have used the Minkowski metric to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our search algorithm tackles this problem by employing a similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in a dataset. Similarity search in an image database based on this contextual information shows encouraging experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=context-sensitive%20search" title="context-sensitive search">context-sensitive search</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20search" title=" image search"> image search</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20ranking" title=" similarity ranking"> similarity ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20search" title=" similarity search"> similarity search</a> </p> <a href="https://publications.waset.org/abstracts/65150/a-context-sensitive-algorithm-for-media-similarity-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65150.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">365</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">2366</span> On the Interactive Search with Web Documents </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mario%20Kubek">Mario Kubek</a>, <a href="https://publications.waset.org/abstracts/search?q=Herwig%20Unger"> Herwig Unger</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the large amount of information in the World Wide Web (WWW, web) and the lengthy and usually linearly ordered result lists of web search engines that do not indicate semantic relationships between their entries, the search for topically similar and related documents can become a tedious task. Especially, the process of formulating queries with proper terms representing specific information needs requires much effort from the user. This problem gets even bigger when the user's knowledge on a subject and its technical terms is not sufficient enough to do so. This article presents the new and interactive search application DocAnalyser that addresses this problem by enabling users to find similar and related web documents based on automatic query formulation and state-of-the-art search word extraction. Additionally, this tool can be used to track topics across semantically connected web documents <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DocAnalyser" title="DocAnalyser">DocAnalyser</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20web%20search" title=" interactive web search"> interactive web search</a>, <a href="https://publications.waset.org/abstracts/search?q=search%20word%20extraction" title=" search word extraction"> search word extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20formulation" title=" query formulation"> query formulation</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20topic%20detection" title=" source topic detection"> source topic detection</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20tracking" title=" topic tracking "> topic tracking </a> </p> <a href="https://publications.waset.org/abstracts/17687/on-the-interactive-search-with-web-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17687.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">393</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">2365</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">2364</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">2363</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">335</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">2362</span> Elemental Graph Data Model: A Semantic and Topological Representation of Building Elements</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yasmeen%20A.%20S.%20Essawy">Yasmeen A. S. Essawy</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Nassar"> Khaled Nassar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building%20information%20modeling%20%28BIM%29" title="building information modeling (BIM)">building information modeling (BIM)</a>, <a href="https://publications.waset.org/abstracts/search?q=elemental%20graph%20data%20model%20%28EGDM%29" title=" elemental graph data model (EGDM)"> elemental graph data model (EGDM)</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20and%20topological%20data%20models" title=" geometric and topological data models"> geometric and topological data models</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20theory" title=" graph theory"> graph theory</a> </p> <a href="https://publications.waset.org/abstracts/70542/elemental-graph-data-model-a-semantic-and-topological-representation-of-building-elements" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70542.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">382</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">2361</span> 3D-Vehicle Associated Research Fields for Smart City via Semantic Search Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haluk%20Eren">Haluk Eren</a>, <a href="https://publications.waset.org/abstracts/search?q=Mucahit%20Karaduman"> Mucahit Karaduman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents 15-year trends for scientific studies in a scientific database considering 3D and vehicle words. Two words are selected to find their associated publications in IEEE scholar database. Both of keywords are entered individually for the years 2002, 2012, and 2016 on the database to identify the preferred subjects of researchers in same years. We have classified closer research fields after searching and listing. Three years (2002, 2012, and 2016) have been investigated to figure out progress in specified time intervals. The first one is assumed as the initial progress in between 2002-2012, and the second one is in 2012-2016 that is fast development duration. We have found very interesting and beneficial results to understand the scholars’ research field preferences for a decade. This information will be highly desirable in smart city-based research purposes consisting of 3D and vehicle-related issues. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vehicle" title="Vehicle">Vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=three-dimensional" title=" three-dimensional"> three-dimensional</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20city" title=" smart city"> smart city</a>, <a href="https://publications.waset.org/abstracts/search?q=scholarly%20search" title=" scholarly search"> scholarly search</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic" title=" semantic"> semantic</a> </p> <a href="https://publications.waset.org/abstracts/75968/3d-vehicle-associated-research-fields-for-smart-city-via-semantic-search-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75968.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">329</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">2360</span> Using Corpora in Semantic Studies of English Adjectives</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oxana%20Lukoshus">Oxana Lukoshus</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The methods of corpus linguistics, a well-established field of research, are being increasingly applied in cognitive linguistics. Corpora data are especially useful for different quantitative studies of grammatical and other aspects of language. The main objective of this paper is to demonstrate how present-day corpora can be applied in semantic studies in general and in semantic studies of adjectives in particular. Polysemantic adjectives have been the subject of numerous studies. But most of them have been carried out on dictionaries. Undoubtedly, dictionaries are viewed as one of the basic data sources, but only at the initial steps of a research. The author usually starts with the analysis of the lexicographic data after which s/he comes up with a hypothesis. In the research conducted three polysemantic synonyms true, loyal, faithful have been analyzed in terms of differences and similarities in their semantic structure. A corpus-based approach in the study of the above-mentioned adjectives involves the following. After the analysis of the dictionary data there was the reference to the following corpora to study the distributional patterns of the words under study – the British National Corpus (BNC) and the Corpus of Contemporary American English (COCA). These corpora are continually updated and contain thousands of examples of the words under research which make them a useful and convenient data source. For the purpose of this study there were no special needs regarding genre, mode or time of the texts included in the corpora. Out of the range of possibilities offered by corpus-analysis software (e.g. word lists, statistics of word frequencies, etc.), the most useful tool for the semantic analysis was the extracting a list of co-occurrence for the given search words. Searching by lemmas, e.g. true, true to, and grouping the results by lemmas have proved to be the most efficient corpora feature for the adjectives under the study. Following the search process, the corpora provided a list of co-occurrences, which were then to be analyzed and classified. Not every co-occurrence was relevant for the analysis. For example, the phrases like An enormous sense of responsibility to protect the minds and hearts of the faithful from incursions by the state was perceived to be the basic duty of the church leaders or ‘True,’ said Phoebe, ‘but I'd probably get to be a Union Official immediately were left out as in the first example the faithful is a substantivized adjective and in the second example true is used alone with no other parts of speech. The subsequent analysis of the corpora data gave the grounds for the distribution groups of the adjectives under the study which were then investigated with the help of a semantic experiment. To sum it up, the corpora-based approach has proved to be a powerful, reliable and convenient tool to get the data for the further semantic study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corpora" title="corpora">corpora</a>, <a href="https://publications.waset.org/abstracts/search?q=corpus-based%20approach" title=" corpus-based approach"> corpus-based approach</a>, <a href="https://publications.waset.org/abstracts/search?q=polysemantic%20adjectives" title=" polysemantic adjectives"> polysemantic adjectives</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20studies" title=" semantic studies "> semantic studies </a> </p> <a href="https://publications.waset.org/abstracts/42781/using-corpora-in-semantic-studies-of-english-adjectives" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42781.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">314</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">2359</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">2358</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">2357</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">2356</span> Pattern Recognition Search: An Advancement Over Interpolation Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahpar%20Yilmaz">Shahpar Yilmaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasir%20Nadeem"> Yasir Nadeem</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20A.%20Mehdi"> Syed A. Mehdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Searching for a record in a dataset is always a frequent task for any data structure-related application. Hence, a fast and efficient algorithm for the approach has its importance in yielding the quickest results and enhancing the overall productivity of the company. Interpolation search is one such technique used to search through a sorted set of elements. This paper proposes a new algorithm, an advancement over interpolation search for the application of search over a sorted array. Pattern Recognition Search or PR Search (PRS), like interpolation search, is a pattern-based divide and conquer algorithm whose objective is to reduce the sample size in order to quicken the process and it does so by treating the array as a perfect arithmetic progression series and thereby deducing the key element’s position. We look to highlight some of the key drawbacks of interpolation search, which are accounted for in the Pattern Recognition Search. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=array" title="array">array</a>, <a href="https://publications.waset.org/abstracts/search?q=complexity" title=" complexity"> complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=index" title=" index"> index</a>, <a href="https://publications.waset.org/abstracts/search?q=sorting" title=" sorting"> sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=space" title=" space"> space</a>, <a href="https://publications.waset.org/abstracts/search?q=time" title=" time"> time</a> </p> <a href="https://publications.waset.org/abstracts/142819/pattern-recognition-search-an-advancement-over-interpolation-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142819.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">243</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">2355</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">2354</span> Semantic Data Schema Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A%C3%AFcha%20Ben%20Salem">Aïcha Ben Salem</a>, <a href="https://publications.waset.org/abstracts/search?q=Faouzi%20Boufares"> Faouzi Boufares</a>, <a href="https://publications.waset.org/abstracts/search?q=Sebastiao%20Correia"> Sebastiao Correia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=schema%20recognition" title="schema recognition">schema recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20data%20profiling" title=" semantic data profiling"> semantic data profiling</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-categorisation" title=" meta-categorisation"> meta-categorisation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20dependencies%20inter%20columns" title=" semantic dependencies inter columns"> semantic dependencies inter columns</a> </p> <a href="https://publications.waset.org/abstracts/34129/semantic-data-schema-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34129.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">418</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">2353</span> Using Textual Pre-Processing and Text Mining to Create Semantic Links</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ricardo%20Avila">Ricardo Avila</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20Lopes"> Gabriel Lopes</a>, <a href="https://publications.waset.org/abstracts/search?q=Vania%20Vidal"> Vania Vidal</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20Macedo"> Jose Macedo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article offers a approach to the automatic discovery of semantic concepts and links in the domain of Oil Exploration and Production (E&P). Machine learning methods combined with textual pre-processing techniques were used to detect local patterns in texts and, thus, generate new concepts and new semantic links. Even using more specific vocabularies within the oil domain, our approach has achieved satisfactory results, suggesting that the proposal can be applied in other domains and languages, requiring only minor adjustments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20links" title="semantic links">semantic links</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=linked%20data" title=" linked data"> linked data</a>, <a href="https://publications.waset.org/abstracts/search?q=SKOS" title=" SKOS"> SKOS</a> </p> <a href="https://publications.waset.org/abstracts/103903/using-textual-pre-processing-and-text-mining-to-create-semantic-links" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103903.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">179</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">2352</span> Semantic Textual Similarity on Contracts: Exploring Multiple Negative Ranking Losses for Sentence Transformers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yogendra%20Sisodia">Yogendra Sisodia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Researchers are becoming more interested in extracting useful information from legal documents thanks to the development of large-scale language models in natural language processing (NLP), and deep learning has accelerated the creation of powerful text mining models. Legal fields like contracts benefit greatly from semantic text search since it makes it quick and easy to find related clauses. After collecting sentence embeddings, it is relatively simple to locate sentences with a comparable meaning throughout the entire legal corpus. The author of this research investigated two pre-trained language models for this task: MiniLM and Roberta, and further fine-tuned them on Legal Contracts. The author used Multiple Negative Ranking Loss for the creation of sentence transformers. The fine-tuned language models and sentence transformers showed promising results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=legal%20contracts" title="legal contracts">legal contracts</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20negative%20ranking%20loss" title=" multiple negative ranking loss"> multiple negative ranking loss</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20inference" title=" natural language inference"> natural language inference</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20transformers" title=" sentence transformers"> sentence transformers</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20textual%20similarity" title=" semantic textual similarity"> semantic textual similarity</a> </p> <a href="https://publications.waset.org/abstracts/156624/semantic-textual-similarity-on-contracts-exploring-multiple-negative-ranking-losses-for-sentence-transformers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156624.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">108</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">2351</span> Using the Semantic Web Technologies to Bring Adaptability in E-Learning Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatima%20Faiza%20Ahmed">Fatima Faiza Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Farrukh%20Hussain"> Syed Farrukh Hussain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The last few decades have seen a large proportion of our population bending towards e-learning technologies, starting from learning tools used in primary and elementary schools to competency based e-learning systems specifically designed for applications like finance and marketing. The huge diversity in this crowd brings about a large number of challenges for the designers of these e-learning systems, one of which is the adaptability of such systems. This paper focuses on adaptability in the learning material in an e-learning course and how artificial intelligence and the semantic web can be used as an effective tool for this purpose. The study proved that the semantic web, still a hot topic in the area of computer science can prove to be a powerful tool in designing and implementing adaptable e-learning systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptable%20e-learning" title="adaptable e-learning">adaptable e-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=HTMLParser" title=" HTMLParser"> HTMLParser</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20extraction" title=" information extraction"> information extraction</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/77268/using-the-semantic-web-technologies-to-bring-adaptability-in-e-learning-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77268.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">339</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">2350</span> Social Semantic Web-Based Analytics Approach to Support Lifelong Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Halimi">Khaled Halimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassina%20Seridi-Bouchelaghem"> Hassina Seridi-Bouchelaghem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to describe how learning analytics approaches based on social semantic web techniques can be applied to enhance the lifelong learning experiences in a connectivist perspective. For this reason, a prototype of a system called <em>SoLearn</em> (Social Learning Environment) that supports this approach. We observed and studied literature related to lifelong learning systems, social semantic web and ontologies, connectivism theory, learning analytics approaches and reviewed implemented systems based on these fields to extract and draw conclusions about necessary features for enhancing the lifelong learning process. The semantic analytics of learning can be used for viewing, studying and analysing the massive data generated by learners, which helps them to understand through recommendations, charts and figures their learning and behaviour, and to detect where they have weaknesses or limitations. This paper emphasises that implementing a learning analytics approach based on social semantic web representations can enhance the learning process. From one hand, the analysis process leverages the meaning expressed by semantics presented in the ontology (relationships between concepts). From the other hand, the analysis process exploits the discovery of new knowledge by means of inferring mechanism of the semantic web. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=connectivism" title="connectivism">connectivism</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=lifelong%20learning" title=" lifelong learning"> lifelong learning</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20semantic%20web" title=" social semantic web"> social semantic web</a> </p> <a href="https://publications.waset.org/abstracts/100850/social-semantic-web-based-analytics-approach-to-support-lifelong-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100850.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">215</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">2349</span> The Influence of Noise on Aerial Image Semantic Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pengchao%20Wei">Pengchao Wei</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangzhong%20Fang"> Xiangzhong Fang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Noise is ubiquitous in this world. Denoising is an essential technology, especially in image semantic segmentation, where noises are generally categorized into two main types i.e. feature noise and label noise. The main focus of this paper is aiming at modeling label noise, investigating the behaviors of different types of label noise on image semantic segmentation tasks using K-Nearest-Neighbor and Convolutional Neural Network classifier. The performance without label noise and with is evaluated and illustrated in this paper. In addition to that, the influence of feature noise on the image semantic segmentation task is researched as well and a feature noise reduction method is applied to mitigate its influence in the learning procedure. <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=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20noise" title=" feature noise"> feature noise</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20semantic%20segmentation" title=" image semantic segmentation"> image semantic segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest-neighbor" title=" k-nearest-neighbor"> k-nearest-neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=label%20noise" title=" label noise"> label noise</a> </p> <a href="https://publications.waset.org/abstracts/141479/the-influence-of-noise-on-aerial-image-semantic-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141479.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">220</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">2348</span> Social Media Idea Ontology: A Concept for Semantic Search of Product Ideas in Customer Knowledge through User-Centered Metrics and Natural Language Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Martin%20H%C2%A8ausl">Martin H¨ausl</a>, <a href="https://publications.waset.org/abstracts/search?q=Maximilian%20Auch"> Maximilian Auch</a>, <a href="https://publications.waset.org/abstracts/search?q=Johannes%20Forster"> Johannes Forster</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Mandl"> Peter Mandl</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Schill"> Alexander Schill</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to survive on the market, companies must constantly develop improved and new products. These products are designed to serve the needs of their customers in the best possible way. The creation of new products is also called innovation and is primarily driven by a company’s internal research and development department. However, a new approach has been taking place for some years now, involving external knowledge in the innovation process. This approach is called open innovation and identifies customer knowledge as the most important source in the innovation process. This paper presents a concept of using social media posts as an external source to support the open innovation approach in its initial phase, the Ideation phase. For this purpose, the social media posts are semantically structured with the help of an ontology and the authors are evaluated using graph-theoretical metrics such as density. For the structuring and evaluation of relevant social media posts, we also use the findings of Natural Language Processing, e. g. Named Entity Recognition, specific dictionaries, Triple Tagger and Part-of-Speech-Tagger. The selection and evaluation of the tools used are discussed in this paper. Using our ontology and metrics to structure social media posts enables users to semantically search these posts for new product ideas and thus gain an improved insight into the external sources such as customer needs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=idea%20ontology" title="idea ontology">idea ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=innovation%20management" title=" innovation management"> innovation management</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20search" title=" semantic search"> semantic search</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20information%20extraction" title=" open information extraction"> open information extraction</a> </p> <a href="https://publications.waset.org/abstracts/71424/social-media-idea-ontology-a-concept-for-semantic-search-of-product-ideas-in-customer-knowledge-through-user-centered-metrics-and-natural-language-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71424.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">188</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">2347</span> A Survey of Semantic Integration Approaches in Bioinformatics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chaimaa%20Messaoudi">Chaimaa Messaoudi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachida%20Fissoune"> Rachida Fissoune</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Badir"> Hassan Badir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biological%20ontology" title="biological ontology">biological ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=linked%20data" title=" linked data"> linked data</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20data%20integration" title=" semantic data integration"> semantic data integration</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/60697/a-survey-of-semantic-integration-approaches-in-bioinformatics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60697.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span 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