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data</title> <meta name="description" content="Search results for: semantic data"> <meta name="keywords" content="semantic data"> <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 data"> <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> 25431</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: semantic data</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25431</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">25430</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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25429</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">25428</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">25427</span> Multidimensional Item Response Theory Models for Practical Application in Large Tests Designed to Measure Multiple Constructs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20Fernanda%20Ordo%C3%B1ez%20Martinez">Maria Fernanda Ordoñez Martinez</a>, <a href="https://publications.waset.org/abstracts/search?q=Alvaro%20Mauricio%20Montenegro"> Alvaro Mauricio Montenegro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision. <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=factorial%20analysis" title=" factorial analysis"> factorial analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=dimension%20reduction" title=" dimension reduction"> dimension reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20logistic%20regression" title=" penalized logistic regression"> penalized logistic regression</a> </p> <a href="https://publications.waset.org/abstracts/42128/multidimensional-item-response-theory-models-for-practical-application-in-large-tests-designed-to-measure-multiple-constructs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42128.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">443</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">25426</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">25425</span> A Secure System for Handling Information from Heterogeous Sources</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shoohira%20Aftab">Shoohira Aftab</a>, <a href="https://publications.waset.org/abstracts/search?q=Hammad%20Afzal"> Hammad Afzal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Information integration is a well known procedure to provide consolidated view on sets of heterogeneous information sources. It not only provides better statistical analysis of information but also facilitates users to query without any knowledge on the underlying heterogeneous information sources The problem of providing a consolidated view of information can be handled using Semantic data (information stored in such a way that is understandable by machines and integrate-able without manual human intervention). However, integrating information using semantic web technology without any access management enforced, will results in increase of privacy and confidentiality concerns. In this research we have designed and developed a framework that would allow information from heterogeneous formats to be consolidated, thus resolving the issue of interoperability. We have also devised an access control system for defining explicit privacy constraints. We designed and applied our framework on both semantic and non-semantic data from heterogeneous resources. Our approach is validated using scenario based testing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20integration" title="information integration">information integration</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20data" title=" semantic data"> semantic data</a>, <a href="https://publications.waset.org/abstracts/search?q=interoperability" title=" interoperability"> interoperability</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=access%20control%20system" title=" access control system"> access control system</a> </p> <a href="https://publications.waset.org/abstracts/15778/a-secure-system-for-handling-information-from-heterogeous-sources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15778.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">357</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">25424</span> Application of Improved Semantic Communication Technology in Remote Sensing Data Transmission</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tingwei%20Shu">Tingwei Shu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Zhou"> Dong Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Chengjun%20Guo"> Chengjun Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20communication" title="semantic communication">semantic communication</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20processing" title=" data processing"> data processing</a> </p> <a href="https://publications.waset.org/abstracts/167726/application-of-improved-semantic-communication-technology-in-remote-sensing-data-transmission" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167726.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">78</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">25423</span> Optimization Query Image Using Search Relevance Re-Ranking Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20G.%20Asmitha%20Chandini">T. G. Asmitha Chandini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Web-based image search re-ranking, as an successful method to get better the results. In a query keyword, the first stair is store the images is first retrieve based on the text-based information. The user to select a query keywordimage, by using this query keyword other images are re-ranked based on their visual properties with images.Now a day to day, people projected to match images in a semantic space which is used attributes or reference classes closely related to the basis of semantic image. though, understanding a worldwide visual semantic space to demonstrate highly different images from the web is difficult and inefficient. The re-ranking images, which automatically offline part learns dissimilar semantic spaces for different query keywords. The features of images are projected into their related semantic spaces to get particular images. At the online stage, images are re-ranked by compare their semantic signatures obtained the semantic précised by the query keyword image. The query-specific semantic signatures extensively improve both the proper and efficiency of image re-ranking. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Query" title="Query">Query</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword" title=" keyword"> keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/abstracts/search?q=re-ranking" title=" re-ranking"> re-ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic" title=" semantic"> semantic</a>, <a href="https://publications.waset.org/abstracts/search?q=signature" title=" signature"> signature</a> </p> <a href="https://publications.waset.org/abstracts/28398/optimization-query-image-using-search-relevance-re-ranking-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28398.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">549</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25422</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">25421</span> A Semantic E-Learning and E-Assessment System of Learners </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wiem%20Ben%20Khalifa">Wiem Ben Khalifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Dalila%20Souilem"> Dalila Souilem</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Neji"> Mahmoud Neji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The evolutions of Social Web and Semantic Web lead us to ask ourselves about the way of supporting the personalization of learning by means of intelligent filtering of educational resources published in the digital networks. We recommend personalized courses of learning articulated around a first educational course defined upstream. Resuming the context and the stakes in the personalization, we also suggest anchoring the personalization of learning in a community of interest within a group of learners enrolled in the same training. This reflection is supported by the display of an active and semantic system of learning dedicated to the constitution of personalized to measure courses and in the due time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Semantic%20Web" title="Semantic Web">Semantic Web</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20system" title=" semantic system"> semantic system</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=evaluation" title=" evaluation"> evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=e-learning" title=" e-learning"> e-learning</a> </p> <a href="https://publications.waset.org/abstracts/72932/a-semantic-e-learning-and-e-assessment-system-of-learners" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72932.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">334</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25420</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">386</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">25419</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">25418</span> A Study of Various Ontology Learning Systems from Text and a Look into Future</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatima%20Al-Aswadi">Fatima Al-Aswadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Chan%20Yong"> Chan Yong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the large volume of unstructured data that increases day by day on the web, the motivation of representing the knowledge in this data in the machine processable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The goal of Ontology learning from text is to elicit and represent domain knowledge in the machine readable form. This paper aims to give a follow-up review on the ontology learning systems from text and some of their defects. Furthermore, it discusses how far the ontology learning process will enhance in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concept%20discovery" title="concept discovery">concept discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology%20learning" title=" ontology learning"> ontology learning</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20relation" title=" semantic relation"> semantic relation</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/63156/a-study-of-various-ontology-learning-systems-from-text-and-a-look-into-future" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63156.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">25417</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">214</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">25416</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">25415</span> Incorporating Lexical-Semantic Knowledge into Convolutional Neural Network Framework for Pediatric Disease Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaocong%20Liu">Xiaocong Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Huazhen%20Wang"> Huazhen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ting%20He"> Ting He</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaozheng%20Li"> Xiaozheng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Weihan%20Zhang"> Weihan Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20Chen"> Jian Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20medical%20record" title=" electronic medical record"> electronic medical record</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20representation" title=" feature representation"> feature representation</a>, <a href="https://publications.waset.org/abstracts/search?q=lexical%20semantics" title=" lexical semantics"> lexical semantics</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20decision" title=" semantic decision"> semantic decision</a> </p> <a href="https://publications.waset.org/abstracts/137499/incorporating-lexical-semantic-knowledge-into-convolutional-neural-network-framework-for-pediatric-disease-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137499.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">125</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25414</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">25413</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">25412</span> Graph Planning Based Composition for Adaptable Semantic Web Services</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rihab%20Ben%20Lamine">Rihab Ben Lamine</a>, <a href="https://publications.waset.org/abstracts/search?q=Raoudha%20Ben%20Jemaa"> Raoudha Ben Jemaa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ikram%20Amous%20Ben%20Amor"> Ikram Amous Ben Amor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a graph planning technique for semantic adaptable Web Services composition. First, we use an ontology based context model for extending Web Services descriptions with information about the most suitable context for its use. Then, we transform the composition problem into a semantic context aware graph planning problem to build the optimal service composition based on user's context. The construction of the planning graph is based on semantic context aware Web Service discovery that allows for each step to add most suitable Web Services in terms of semantic compatibility between the services parameters and their context similarity with the user's context. In the backward search step, semantic and contextual similarity scores are used to find best composed Web Services list. Finally, in the ranking step, a score is calculated for each best solution and a set of ranked solutions is returned to the user. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20web%20service" title="semantic web service">semantic web service</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20service%20composition" title=" web service composition"> web service composition</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptation" title=" adaptation"> adaptation</a>, <a href="https://publications.waset.org/abstracts/search?q=context" title=" context"> context</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20planning" title=" graph planning"> graph planning</a> </p> <a href="https://publications.waset.org/abstracts/62455/graph-planning-based-composition-for-adaptable-semantic-web-services" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62455.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">520</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25411</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">25410</span> Discovering Semantic Links Between Synonyms, Hyponyms and Hypernyms</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 proposal aims for semantic enrichment between glossaries using the Simple Knowledge Organization System (SKOS) vocabulary to discover synonyms, hyponyms and hyperonyms semiautomatically, in Brazilian Portuguese, generating new semantic relationships based on WordNet. To evaluate the quality of this proposed model, experiments were performed by the use of two sets containing new relations, being one generated automatically and the other manually mapped by the domain expert. The applied evaluation metrics were precision, recall, f-score, and confidence interval. The results obtained demonstrate that the applied method in the field of Oil Production and Extraction (E&P) is effective, which suggests that it can be used to improve the quality of terminological mappings. The procedure, although adding complexity in its elaboration, can be reproduced in others domains. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ontology%20matching" title="ontology matching">ontology matching</a>, <a href="https://publications.waset.org/abstracts/search?q=mapping%20enrichment" title=" mapping enrichment"> mapping enrichment</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=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/103911/discovering-semantic-links-between-synonyms-hyponyms-and-hypernyms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103911.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">216</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">25409</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">25408</span> Application of Semantic Technologies in Rapid Reconfiguration of Factory Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20Zhang">J. Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Agyapong-Kodua"> K. Agyapong-Kodua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Digital factory based on visual design and simulation has emerged as a mainstream to reduce digital development life cycle. Some basic industrial systems are being integrated via semantic modelling, and products (P) matching process (P)-resource (R) requirements are designed to fulfill current customer demands. Nevertheless, product design is still limited to fixed product models and known knowledge of product engineers. Therefore, this paper presents a rapid reconfiguration method based on semantic technologies with PPR ontologies to reuse known and unknown knowledge. In order to avoid the influence of big data, our system uses a cloud manufactory and distributed database to improve the efficiency of querying meeting PPR requirements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20technologies" title="semantic technologies">semantic technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=factory%20system" title=" factory system"> factory system</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20factory" title=" digital factory"> digital factory</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20manufactory" title=" cloud manufactory"> cloud manufactory</a> </p> <a href="https://publications.waset.org/abstracts/17570/application-of-semantic-technologies-in-rapid-reconfiguration-of-factory-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17570.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">487</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">25407</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">25406</span> Resource Framework Descriptors for Interestingness in Data </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20B.%20Abhilash">C. B. Abhilash</a>, <a href="https://publications.waset.org/abstracts/search?q=Kavi%20Mahesh"> Kavi Mahesh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human beings are the most advanced species on earth; it's all because of the ability to communicate and share information via human language. In today's world, a huge amount of data is available on the web in text format. This has also resulted in the generation of big data in structured and unstructured formats. In general, the data is in the textual form, which is highly unstructured. To get insights and actionable content from this data, we need to incorporate the concepts of text mining and natural language processing. In our study, we mainly focus on Interesting data through which interesting facts are generated for the knowledge base. The approach is to derive the analytics from the text via the application of natural language processing. Using semantic web Resource framework descriptors (RDF), we generate the triple from the given data and derive the interesting patterns. The methodology also illustrates data integration using the RDF for reliable, interesting patterns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RDF" title="RDF">RDF</a>, <a href="https://publications.waset.org/abstracts/search?q=interestingness" title=" interestingness"> interestingness</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20base" title=" knowledge base"> knowledge base</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20data" title=" semantic data"> semantic data</a> </p> <a href="https://publications.waset.org/abstracts/130576/resource-framework-descriptors-for-interestingness-in-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130576.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">162</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">25405</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">25404</span> A Semantic Analysis of Modal Verbs in Barak Obama’s 2012 Presidential Campaign Speech</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kais%20A.%20Kadhim">Kais A. Kadhim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is a semantic analysis of the English modals in Obama’s speech. The main objective of this study is to analyze selected modal auxiliaries identified in selected speeches of Obama’s campaign based on Coates’ (1983) semantic clusters. A total of fifteen speeches of Obama’s campaign were selected as the primary data and the modal auxiliaries selected for analysis include will, would, can, could, should, must, ought, shall, may and might. All the modal auxiliaries taken from the speeches of Barack Obama were analyzed based on the framework of Coates’ semantic clusters. Such analytical framework was carried out to examine how modal auxiliaries are used in the context of persuading people in Obama’s campaign speeches. The findings reveal that modals of intention, prediction, futurity and modals of possibility, ability, permission are mostly used in Obama’s campaign speeches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=modals" title="modals">modals</a>, <a href="https://publications.waset.org/abstracts/search?q=meaning" title=" meaning"> meaning</a>, <a href="https://publications.waset.org/abstracts/search?q=persuasion" title=" persuasion"> persuasion</a>, <a href="https://publications.waset.org/abstracts/search?q=speech" title=" speech"> speech</a> </p> <a href="https://publications.waset.org/abstracts/13912/a-semantic-analysis-of-modal-verbs-in-barak-obamas-2012-presidential-campaign-speech" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13912.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">405</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">25403</span> Emerging Technology for Business Intelligence Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hsien-Tsen%20Wang">Hsien-Tsen Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</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=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a> </p> <a href="https://publications.waset.org/abstracts/162726/emerging-technology-for-business-intelligence-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162726.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">25402</span> Hybrid Approximate Structural-Semantic Frequent Subgraph Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Montaceur%20Zaghdoud">Montaceur Zaghdoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Moussaoui"> Mohamed Moussaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalel%20Akaichi"> Jalel Akaichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximate%20graph%20matching" title="approximate graph matching">approximate graph matching</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20frequent%20subgraph%20mining" title=" hybrid frequent subgraph mining"> hybrid frequent subgraph mining</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20mining" title=" graph mining"> graph mining</a>, <a href="https://publications.waset.org/abstracts/search?q=possibility%20theory" title=" possibility theory"> possibility theory</a> </p> <a href="https://publications.waset.org/abstracts/34195/hybrid-approximate-structural-semantic-frequent-subgraph-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34195.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">402</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%20data&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=semantic%20data&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=semantic%20data&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=semantic%20data&page=5">5</a></li> <li class="page-item"><a class="page-link" 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