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modeling</title> <meta name="description" content="Search results for: semantic modeling"> <meta name="keywords" content="semantic modeling"> <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 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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 modeling"> <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> 4429</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: semantic modeling</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4429</span> Ontology-Based Approach for Temporal Semantic Modeling of Social Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sou%C3%A2ad%20Boudebza">Souâad Boudebza</a>, <a href="https://publications.waset.org/abstracts/search?q=Omar%20Nouali"> Omar Nouali</a>, <a href="https://publications.waset.org/abstracts/search?q=Fai%C3%A7al%20Azouaou"> Faiçal Azouaou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social networks have recently gained a growing interest on the web. Traditional formalisms for representing social networks are static and suffer from the lack of semantics. In this paper, we will show how semantic web technologies can be used to model social data. The SemTemp ontology aligns and extends existing ontologies such as FOAF, SIOC, SKOS and OWL-Time to provide a temporal and semantically rich description of social data. We also present a modeling scenario to illustrate how our ontology can be used to model social networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ontology" title="ontology">ontology</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=social%20network" title=" social network"> social network</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20modeling" title=" temporal modeling"> temporal modeling</a> </p> <a href="https://publications.waset.org/abstracts/42125/ontology-based-approach-for-temporal-semantic-modeling-of-social-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42125.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">387</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">4428</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">4427</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">4426</span> Topic Modelling Using Latent Dirichlet Allocation and Latent Semantic Indexing on SA Telco Twitter Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phumelele%20Kubheka">Phumelele Kubheka</a>, <a href="https://publications.waset.org/abstracts/search?q=Pius%20Owolawi"> Pius Owolawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Gbolahan%20Aiyetoro"> Gbolahan Aiyetoro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is one of the most popular social media platforms where users can share their opinions on different subjects. As of 2010, The Twitter platform generates more than 12 Terabytes of data daily, ~ 4.3 petabytes in a single year. For this reason, Twitter is a great source for big mining data. Many industries such as Telecommunication companies can leverage the availability of Twitter data to better understand their markets and make an appropriate business decision. This study performs topic modeling on Twitter data using Latent Dirichlet Allocation (LDA). The obtained results are benchmarked with another topic modeling technique, Latent Semantic Indexing (LSI). The study aims to retrieve topics on a Twitter dataset containing user tweets on South African Telcos. Results from this study show that LSI is much faster than LDA. However, LDA yields better results with higher topic coherence by 8% for the best-performing model represented in Table 1. A higher topic coherence score indicates better performance of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20Dirichlet%20allocation" title=" latent Dirichlet allocation"> latent Dirichlet allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=latent%20semantic%20indexing" title=" latent semantic indexing"> latent semantic indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=telco" title=" telco"> telco</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a> </p> <a href="https://publications.waset.org/abstracts/147818/topic-modelling-using-latent-dirichlet-allocation-and-latent-semantic-indexing-on-sa-telco-twitter-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147818.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">150</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">4425</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">4424</span> Analysis of Expert Information in Linguistic Terms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Poleshchuk">O. Poleshchuk</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Komarov"> E. Komarov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, semantic spaces with the properties of completeness and orthogonality (complete orthogonal semantic spaces) were chosen as models of expert evaluations. As the theoretical and practical studies have shown all the properties of complete orthogonal semantic spaces correspond to the thinking activity of experts that is why these semantic spaces were chosen for modeling. Two methods of construction such spaces were proposed. Models of comparative and fuzzy cluster analysis of expert evaluations were developed. The practical application of the developed methods has demonstrated their viability and validity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expert%20evaluation" title="expert evaluation">expert evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20analysis" title=" comparative analysis"> comparative analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20cluster%20analysis" title=" fuzzy cluster analysis"> fuzzy cluster analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=theoretical%20and%20practical%20studies" title=" theoretical and practical studies"> theoretical and practical studies</a> </p> <a href="https://publications.waset.org/abstracts/18594/analysis-of-expert-information-in-linguistic-terms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18594.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">531</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">4423</span> A Methodology to Integrate Data in the Company Based on the Semantic Standard in the Context of Industry 4.0</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chang%20Qin">Chang Qin</a>, <a href="https://publications.waset.org/abstracts/search?q=Daham%20Mustafa"> Daham Mustafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Abderrahmane%20Khiat"> Abderrahmane Khiat</a>, <a href="https://publications.waset.org/abstracts/search?q=Pierre%20Bienert"> Pierre Bienert</a>, <a href="https://publications.waset.org/abstracts/search?q=Paulo%20Zanini"> Paulo Zanini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, companies are facing lots of challenges in the process of digital transformation, which can be a complex and costly undertaking. Digital transformation involves the collection and analysis of large amounts of data, which can create challenges around data management and governance. Furthermore, it is also challenged to integrate data from multiple systems and technologies. Although with these pains, companies are still pursuing digitalization because by embracing advanced technologies, companies can improve efficiency, quality, decision-making, and customer experience while also creating different business models and revenue streams. In this paper, the issue that data is stored in data silos with different schema and structures is focused. The conventional approaches to addressing this issue involve utilizing data warehousing, data integration tools, data standardization, and business intelligence tools. However, these approaches primarily focus on the grammar and structure of the data and neglect the importance of semantic modeling and semantic standardization, which are essential for achieving data interoperability. In this session, the challenge of data silos in Industry 4.0 is addressed by developing a semantic modeling approach compliant with Asset Administration Shell (AAS) models as an efficient standard for communication in Industry 4.0. The paper highlights how our approach can facilitate the data mapping process and semantic lifting according to existing industry standards such as ECLASS and other industrial dictionaries. It also incorporates the Asset Administration Shell technology to model and map the company’s data and utilize a knowledge graph for data storage and exploration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20interoperability%20in%20industry%204.0" title="data interoperability in industry 4.0">data interoperability in industry 4.0</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20integration" title=" digital integration"> digital integration</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20dictionary" title=" industrial dictionary"> industrial dictionary</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20modeling" title=" semantic modeling"> semantic modeling</a> </p> <a href="https://publications.waset.org/abstracts/168224/a-methodology-to-integrate-data-in-the-company-based-on-the-semantic-standard-in-the-context-of-industry-40" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168224.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">94</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">4422</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">4421</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">4420</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">4419</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">4418</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">4417</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">4416</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">4415</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">4414</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">4413</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">4412</span> Systems Versioning: A Features-Based Meta-Modeling Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ola%20A.%20Younis">Ola A. Younis</a>, <a href="https://publications.waset.org/abstracts/search?q=Said%20Ghoul"> Said Ghoul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Systems running these days are huge, complex and exist in many versions. Controlling these versions and tracking their changes became a very hard process as some versions are created using meaningless names or specifications. Many versions of a system are created with no clear difference between them. This leads to mismatching between a user’s request and the version he gets. In this paper, we present a system versions meta-modeling approach that produces versions based on system’s features. This model reduced the number of steps needed to configure a release and gave each version its unique specifications. This approach is applicable for systems that use features in its specification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=features" title="features">features</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-modeling" title=" meta-modeling"> meta-modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20modeling" title=" semantic modeling"> semantic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=SPL" title=" SPL"> SPL</a>, <a href="https://publications.waset.org/abstracts/search?q=VCS" title=" VCS"> VCS</a>, <a href="https://publications.waset.org/abstracts/search?q=versioning" title=" versioning"> versioning</a> </p> <a href="https://publications.waset.org/abstracts/7797/systems-versioning-a-features-based-meta-modeling-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7797.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">446</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">4411</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">4410</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">4409</span> Ontological Modeling Approach for Statistical Databases Publication in Linked Open Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bourama%20Mane">Bourama Mane</a>, <a href="https://publications.waset.org/abstracts/search?q=Ibrahima%20Fall"> Ibrahima Fall</a>, <a href="https://publications.waset.org/abstracts/search?q=Mamadou%20Samba%20Camara"> Mamadou Samba Camara</a>, <a href="https://publications.waset.org/abstracts/search?q=Alassane%20Bah"> Alassane Bah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> At the level of the National Statistical Institutes, there is a large volume of data which is generally in a format which conditions the method of publication of the information they contain. Each household or business data collection project includes a dissemination platform for its implementation. Thus, these dissemination methods previously used, do not promote rapid access to information and especially does not offer the option of being able to link data for in-depth processing. In this paper, we present an approach to modeling these data to publish them in a format intended for the Semantic Web. Our objective is to be able to publish all this data in a single platform and offer the option to link with other external data sources. An application of the approach will be made on data from major national surveys such as the one on employment, poverty, child labor and the general census of the population of Senegal. <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=linked%20open%20data" title=" linked open data"> linked open data</a>, <a href="https://publications.waset.org/abstracts/search?q=database" title=" database"> database</a>, <a href="https://publications.waset.org/abstracts/search?q=statistic" title=" statistic"> statistic</a> </p> <a href="https://publications.waset.org/abstracts/87628/ontological-modeling-approach-for-statistical-databases-publication-in-linked-open-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87628.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">175</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">4408</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">4407</span> A Domain Specific Modeling Language Semantic Model for Artefact Orientation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bunakiye%20R.%20Japheth">Bunakiye R. Japheth</a>, <a href="https://publications.waset.org/abstracts/search?q=Ogude%20U.%20Cyril"> Ogude U. Cyril</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since the process of transforming user requirements to modeling constructs are not very well supported by domain-specific frameworks, it became necessary to integrate domain requirements with the specific architectures to achieve an integrated customizable solutions space via artifact orientation. Domain-specific modeling language specifications of model-driven engineering technologies focus more on requirements within a particular domain, which can be tailored to aid the domain expert in expressing domain concepts effectively. Modeling processes through domain-specific language formalisms are highly volatile due to dependencies on domain concepts or used process models. A capable solution is given by artifact orientation that stresses on the results rather than expressing a strict dependence on complicated platforms for model creation and development. Based on this premise, domain-specific methods for producing artifacts without having to take into account the complexity and variability of platforms for model definitions can be integrated to support customizable development. In this paper, we discuss methods for the integration capabilities and necessities within a common structure and semantics that contribute a metamodel for artifact-orientation, which leads to a reusable software layer with concrete syntax capable of determining design intents from domain expert. These concepts forming the language formalism are established from models explained within the oil and gas pipelines industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=control%20process" title="control process">control process</a>, <a href="https://publications.waset.org/abstracts/search?q=metrics%20of%20engineering" title=" metrics of engineering"> metrics of engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=structured%20abstraction" title=" structured abstraction"> structured abstraction</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20model" title=" semantic model"> semantic model</a> </p> <a href="https://publications.waset.org/abstracts/99162/a-domain-specific-modeling-language-semantic-model-for-artefact-orientation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99162.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">4406</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">4405</span> Description of a Structural Health Monitoring and Control System Using Open Building Information Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wahhaj%20Ahmed%20Farooqi">Wahhaj Ahmed Farooqi</a>, <a href="https://publications.waset.org/abstracts/search?q=Bilal%20Ahmad"> Bilal Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Maritza%20Zambrano%20Bernal"> Sandra Maritza Zambrano Bernal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In view of structural engineering, monitoring of structural responses over time is of great importance with respect to recent developments of construction technologies. Recently, developments of advanced computing tools have enabled researcher’s better execution of structural health monitoring (SHM) and control systems. In the last decade, building information modeling (BIM) has substantially enhanced the workflow of planning and operating engineering structures. Typically, building information can be stored and exchanged via model files that are based on the Industry Foundation Classes (IFC) standard. In this study a modeling approach for semantic modeling of SHM and control systems is integrated into the BIM methodology using the IFC standard. For validation of the modeling approach, a laboratory test structure, a four-story shear frame structure, is modeled using a conventional BIM software tool. An IFC schema extension is applied to describe information related to monitoring and control of a prototype SHM and control system installed on the laboratory test structure. The SHM and control system is described by a semantic model applying Unified Modeling Language (UML). Subsequently, the semantic model is mapped into the IFC schema. The test structure is composed of four aluminum slabs and plate-to-column connections are fully fixed. In the center of the top story, semi-active tuned liquid column damper (TLCD) is installed. The TLCD is used to reduce effects of structural responses in context of dynamic vibration and displacement. The wireless prototype SHM and control system is composed of wireless sensor nodes. For testing the SHM and control system, acceleration response is automatically recorded by the sensor nodes equipped with accelerometers and analyzed using embedded computing. As a result, SHM and control systems can be described within open BIM, dynamic responses and information of damages can be stored, documented, and exchanged on the formal basis of the IFC standard. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring" title="structural health monitoring">structural health monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20building%20information%20modeling" title=" open building information modeling"> open building information modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=industry%20foundation%20classes" title=" industry foundation classes"> industry foundation classes</a>, <a href="https://publications.waset.org/abstracts/search?q=unified%20modeling%20language" title=" unified modeling language"> unified modeling language</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-active%20tuned%20liquid%20column%20damper" title=" semi-active tuned liquid column damper"> semi-active tuned liquid column damper</a>, <a href="https://publications.waset.org/abstracts/search?q=nondestructive%20testing" title=" nondestructive testing"> nondestructive testing</a> </p> <a href="https://publications.waset.org/abstracts/117345/description-of-a-structural-health-monitoring-and-control-system-using-open-building-information-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/117345.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">151</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">4404</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">4403</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">4402</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">4401</span> An Intensional Conceptualization Model for Ontology-Based Semantic Integration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fateh%20Adhnouss">Fateh Adhnouss</a>, <a href="https://publications.waset.org/abstracts/search?q=Husam%20El-Asfour"> Husam El-Asfour</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenneth%20McIsaac"> Kenneth McIsaac</a>, <a href="https://publications.waset.org/abstracts/search?q=AbdulMutalib%20Wahaishi"> AbdulMutalib Wahaishi</a>, <a href="https://publications.waset.org/abstracts/search?q=Idris%20El-Feghia"> Idris El-Feghia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conceptualization is an essential component of semantic ontology-based approaches. There have been several approaches that rely on extensional structure and extensional reduction structure in order to construct conceptualization. In this paper, several limitations are highlighted relating to their applicability to the construction of conceptualizations in dynamic and open environments. These limitations arise from a number of strong assumptions that do not apply to such environments. An intensional structure is strongly argued to be a natural and adequate modeling approach. This paper presents a conceptualization structure based on property relations and propositions theory (PRP) to the model ontology that is suitable for open environments. The model extends the First-Order Logic (FOL) notation and defines the formal representation that enables interoperability between software systems and supports semantic integration for software systems in open, dynamic environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conceptualization" title="conceptualization">conceptualization</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=extensional%20structure" title=" extensional structure"> extensional structure</a>, <a href="https://publications.waset.org/abstracts/search?q=intensional%20structure" title=" intensional structure"> intensional structure</a> </p> <a href="https://publications.waset.org/abstracts/151398/an-intensional-conceptualization-model-for-ontology-based-semantic-integration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151398.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">115</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">4400</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 class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">449</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%20modeling&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=semantic%20modeling&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=semantic%20modeling&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=semantic%20modeling&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=semantic%20modeling&page=6">6</a></li> <li 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