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Articles de revues sur le sujet « IL. Semantic web »

Auteur : Grafiati

Publié le 10 mars 2023

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1

Sheth, Amit, Cartic Ramakrishnan, and Christopher Thomas. "Semantics for the Semantic Web." International Journal on Semantic Web and Information Systems 1, no. 1 (2005): 1–18. http://dx.doi.org/10.4018/jswis.2005010101.

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Splendiani, Andrea, Albert Burger, Adrian Paschke, Paolo Romano, and M. Marshall. "Biomedical semantics in the Semantic Web." Journal of Biomedical Semantics 2, Suppl 1 (2011): S1. http://dx.doi.org/10.1186/2041-1480-2-s1-s1.

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Deelmann, Thomas. "Semantic Web." WIRTSCHAFTSINFORMATIK 49, no. 6 (2007): 464. http://dx.doi.org/10.1007/s11576-007-0131-5.

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ASHTARI, SOMAYEH, MALIHE DANESH, and Hossein Shirgahi. "A NOVEL USER PROFILE-BASED FUZZY APPROACH FOR EVALUATING TRUST IN SEMANTIC WEB." IIUM Engineering Journal 20, no. 1 (2019): 158–76. http://dx.doi.org/10.31436/iiumej.v20i1.1060.

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ABSTRACT: As a developed World Wide Web architecture, the Semantic Web collects traditional web contents with a formal and understandable semantic using a machine. The main purpose of the Semantic Web is to increase automation, web information processing, and improve interactions and collaboration among information systems. The subject of trust is one of the main challenges in the semantic web. Since different tools and individuals exist in the semantic web, a certain measure of trust in an entity cannot be used and a central system is responsible for data collection and estimating the reliability. In this study, a fuzzy system is used to evaluate the trust measure in the semantic web. For this purpose, the user profile data including a list of pages, user sessions, and visited pages in each session, and the time of page viewings are used as semantic parameters. After determining the general framework of trust in the semantic web, the effectiveness of the above mentioned semantic parameters on the trust measure is investigated and effective parameters are used for evaluation in the fuzzy system. The experiment results show that the proposed fuzzy method with a mean absolute error of 2.5% and an average precision of 97.5% could achieve the right value of trust in the semantic web. ABSTRAK: Sebagai senibina World Wide Web, Semantik Web mengumpul kandungan web tradisional bersama semantik formal dan mudah difahami menggunakan mesin. Tujuan utama Semantik Web adalah bagi meningkatkan automasi, pemprosesan maklumat web, dan meningkatkan interaksi dan kerjasama antara sistem maklumat. Kepercayaan adalah salah satu cabaran utama dalam web semantik. Disebabkan perbezaan alatan dan pelbagai individu wujud dalam web semantik, langkah tertentu sebagai entiti dipercayai tidak dapat digunakan dan sistem pusat adalah bertanggungjawab bagi pengumpulan data dan kebolehpercayaan anggaran. Dalam kajian ini, sistem rawak telah digunakan bagi menilai tahap kepercayaan dalam web semantik. Bagi tujuan ini, data profil pengguna termasuk senarai halaman, sesi pengguna, dan halaman yang dikunjungi dalam setiap sesi, dan masa paparan halaman telah digunakan sebagai parameter semantik. Selepas menentukan rangka umum kepercayaan dalam web semantik, keberkesanan parameter semantik yang dinyatakan di atas pada ukuran kepercayaan telah disiasat dan parameter yang berkesan telah digunakan bagi penilaian sistem rawak. Keputusan eksperimen menunjukkan bahawa kaedah rawak yang dicadangkan dengan ralat mutlak purata sebanyak 2.5% dan ketepatan purata sebanyak 97.5% dapat mencapai nilai kepercayaan yang benar dalam web semantik.

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Grosky, W. I., D. V. Sreenath, and F. Fotouhi. "Emergent semantics and the multimedia semantic web." ACM SIGMOD Record 31, no. 4 (2002): 54–58. http://dx.doi.org/10.1145/637411.637420.

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Greaves, Mark, and Peter Mika. "Semantic Web and Web 2.0." Journal of Web Semantics 6, no. 1 (2008): 1–3. http://dx.doi.org/10.1016/j.websem.2007.12.002.

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USCHOLD, MIKE. "A semantic continuum on the semantic web." Knowledge Engineering Review 17, no. 1 (2002): 87–91. http://dx.doi.org/10.1017/s0269888902000346.

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In the coming years, the Web is expected to evolve from a structure containing information resources that have little or no explicit semantics to a structure having a rich semantic infrastructure. The key defining feature that is intended to distinguish the future Semantic Web from today's Web is that the content of the Web will be usable by machines (i.e. software agents). Meaning needs to be communicated between agents who advertise and/or require the ability to perform tasks on the Web. Agents also need to determine the meaning of passive (i.e. non-agent) information resources on the web to perform these tasks.

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TF, Michael Raj,, and Siva Prakasam P. "Semantic Web: A Study on Web Service Composition Approaches." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (2017): 196–209. http://dx.doi.org/10.31142/ijtsrd115.

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Childs, Sue. "The Semantic Web." He@lth Information on the Internet 32, no. 1 (2003): 1–2. http://dx.doi.org/10.1258/146041403321544422.

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Ferguson, Jon C. "Semantic Web Technologies." Journal of Electronic Resources in Medical Libraries 4, no. 1-2 (2007): 113–25. http://dx.doi.org/10.1300/j383v04n01_11.

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McIlraith, S. A., T. C. Son, and Honglei Zeng. "Semantic Web services." IEEE Intelligent Systems 16, no. 2 (2001): 46–53. http://dx.doi.org/10.1109/5254.920599.

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Sheth, Amit, Cory Henson, and Satya S. Sahoo. "Semantic Sensor Web." IEEE Internet Computing 12, no. 4 (2008): 78–83. http://dx.doi.org/10.1109/mic.2008.87.

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Mehra, Misha, and Nishant Kumar Nishant Kumar. "Semantic Web Applications." DESIDOC Journal of Library & Information Technology 31, no. 4 (2011): 217–25. http://dx.doi.org/10.14429/djlit.31.4.1113.

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Berners-Lee, Tim, James Hendler, and Ora Lassila. "The Semantic Web." Scientific American 284, no. 5 (2001): 34–43. http://dx.doi.org/10.1038/scientificamerican0501-34.

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Stumme, Gerd, Andreas Hotho, and Bettina Berendt. "Semantic Web Mining." Journal of Web Semantics 4, no. 2 (2006): 124–43. http://dx.doi.org/10.1016/j.websem.2006.02.001.

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Antoniou, Grigoris, Mathieu d’Aquin, and Jeff Z. Pan. "Semantic Web dynamics." Journal of Web Semantics 9, no. 3 (2011): 245–46. http://dx.doi.org/10.1016/j.websem.2011.06.008.

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Greaves, Mark. "Semantic Web 2.0." IEEE Intelligent Systems 22, no. 2 (2007): 94–96. http://dx.doi.org/10.1109/mis.2007.40.

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Mörike, Michael. "Semantic Web 3.0." HMD Praxis der Wirtschaftsinformatik 47, no. 1 (2010): 4–5. http://dx.doi.org/10.1007/bf03340432.

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Parsia, Bijan. "Semantic Web Services." Bulletin of the American Society for Information Science and Technology 29, no. 4 (2005): 12–15. http://dx.doi.org/10.1002/bult.281.

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Albukhitan, Saeed, Ahmed Alnazer, and Tarek Helmy. "Semantic Annotation of Arabic Web Resources Using Semantic Web Services." Procedia Computer Science 83 (2016): 504–11. http://dx.doi.org/10.1016/j.procs.2016.04.243.

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Shirgahi, Hossein, Mehran Mohsenzadeh, and Hamid Haj Seyyed Javadi. "Trust estimation of the semantic web using semantic web clustering." Journal of Experimental & Theoretical Artificial Intelligence 29, no. 3 (2016): 537–56. http://dx.doi.org/10.1080/0952813x.2016.1199601.

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Hammami, Randa, Hatem Bellaaj, and Ahmed Hadj Kacem. "Semantic Web Services Discovery." International Journal on Semantic Web and Information Systems 14, no. 4 (2018): 57–72. http://dx.doi.org/10.4018/ijswis.2018100103.

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This article describes how Web services play an important role in several fields such as e-commerce and e-health. As the number of Web services is increasing rapidly, finding the best Web service according to users' requirements becomes more challenging. The traditional method of Web service discovery is based on keyword match. Due to this, many Web services which are most relevant to the user request are left undiscoverable. Some other emergent approaches are based on semantics to improve the quality of the discovered Web services in terms of relevance and satisfaction of user's need. In this paper, the authors present a survey of existing semantic Web services discovery approaches giving priority to relevant ones. Furthermore, this paper provides a critical and comparative analysis of the studied approaches and stands out major challenges to be addressed to substantially enhance the semantic Web service discovery.

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Slimani, Thabet. "Approaches for Semantic Web Service." International Journal of Service Science, Management, Engineering, and Technology 4, no. 3 (2013): 18–36. http://dx.doi.org/10.4018/ijssmet.2013070102.

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The main objective of the exploitation of semantic descriptions of services through Semantics is a better support for the life-cycle of Web services. The large number of developed ontologies, languages of representations, and integrated frameworks supporting the discovery, composition and invocation of services are a good indicator that research in the field of semantic web service (SWS) has been considerably active. The authors provide in this paper a detailed overview of the approaches and solutions, indicating their core characteristics and objectives required and provide indicators for the interested reader to follow up further insights and details about these solutions and related software.

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Alaa, Rana, Mariam Gawish, and Manuel Fernández-Veiga. "Improving Recommendations for Online Retail Markets Based on Ontology Evolution." Electronics 10, no. 14 (2021): 1650. http://dx.doi.org/10.3390/electronics10141650.

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The semantic web is considered to be an extension of the present web. In the semantic web, information is given with well-defined meanings, and thus helps people worldwide to cooperate together and exchange knowledge. The semantic web plays a significant role in describing the contents and services in a machine-readable form. It has been developed based on ontologies, which are deemed the backbone of the semantic web. Ontologies are a key technique with which semantics are annotated, and they provide common comprehensible foundation for resources on the semantic web. The use of semantics and artificial intelligence leads to what is known to be “Smarter Web”, where it will be easy to retrieve what customers want to see on e-commerce platforms, and thus will help users save time and enhance their search for the products they need. The semantic web is used as well as webs 3.0, which helps enhancing systems performance. Previous personalized recommendation methods based on ontologies identify users’ preferences by means of static snapshots of purchase data. However, as the user preferences evolve with time, the one-shot ontology construction is too constrained for capturing individual diverse opinions and users’ preferences evolution over time. This paper will present a novel recommendation system architecture based on ontology evolution, the proposed subsystem architecture for ontology evolution. Furthermore, the paper proposes an ontology building methodology based on a semi-automatic technique as well as development of online retail ontology. Additionally, a recommendation method based on the ontology reasoning is proposed. Based on the proposed method, e-retailers can develop a more convenient product recommendation system to support consumers’ purchase decisions.

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Avanija, J., and K. Ramar. "Semantic Clustering of Web Documents." International Journal of Information Technology and Web Engineering 7, no. 4 (2012): 20–33. http://dx.doi.org/10.4018/jitwe.2012100102.

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With the massive growth and large volume of the web it is very difficult to recover results based on the user preferences. The next generation web architecture, semantic web reduces the burden of the user by performing search based on semantics instead of keywords. Even in the context of semantic technologies optimization problem occurs but rarely considered. In this paper document clustering is applied to recover relevant documents. The authors propose an ontology based clustering algorithm using semantic similarity measure and Particle Swarm Optimization (PSO), which is applied to the annotated documents for optimizing the result. The proposed method uses Jena API and GATE tool API and the documents can be recovered based on their annotation features and relations. A preliminary experiment comparing the proposed method with K-Means shows that the proposed method is feasible and performs better than K-Means.

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Almeida, Mauricio, Renato Souza, and Fred Fonseca. "Semantics in the Semantic Web: A Critical Evaluation." KNOWLEDGE ORGANIZATION 38, no. 3 (2011): 187–203. http://dx.doi.org/10.5771/0943-7444-2011-3-187.

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Rastier, François. "Sémantique du web vs. Semantic Web ?" Syntaxe et sémantique 9, no. 1 (2008): 15. http://dx.doi.org/10.3917/ss.009.0015.

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Qin, Li. "XBRL, semantic web and web services." International Journal of Business and Systems Research 5, no. 5 (2011): 443. http://dx.doi.org/10.1504/ijbsr.2011.042093.

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Casey, Máire, and Claus Pahl. "Web Components and the Semantic Web." Electronic Notes in Theoretical Computer Science 82, no. 5 (2003): 156–63. http://dx.doi.org/10.1016/s1571-0661(04)80741-2.

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Bell, David, Christoph Bussler, and Jian Yang. "The Semantic Web and Web Services." Information Systems 31, no. 4-5 (2006): 229–31. http://dx.doi.org/10.1016/j.is.2005.03.001.

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Chomboon, K., N. Kaoungku, K. Kerdprasop, and N. Kerdprasop. "Data Mining in Semantic Web Data." International Journal of Computer Theory and Engineering 6, no. 6 (2014): 472–75. http://dx.doi.org/10.7763/ijcte.2014.v6.912.

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Blumauer, Andreas, Tassilo Pellegrini, and Adrian Paschke. "Corporate Semantic Web — der Einsatz von Semantic-Web-Technologien im Unternehmen." HMD Praxis der Wirtschaftsinformatik 47, no. 5 (2010): 105–14. http://dx.doi.org/10.1007/bf03340518.

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Liu, Haoyan, Lei Fang, Jian-Guang Lou, and Zhoujun Li. "Leveraging Web Semantic Knowledge in Word Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6746–53. http://dx.doi.org/10.1609/aaai.v33i01.33016746.

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Much recent work focuses on leveraging semantic lexicons like WordNet to enhance word representation learning (WRL) and achieves promising performance on many NLP tasks. However, most existing methods might have limitations because they require high-quality, manually created, semantic lexicons or linguistic structures. In this paper, we propose to leverage semantic knowledge automatically mined from web structured data to enhance WRL. We first construct a semantic similarity graph, which is referred as semantic knowledge, based on a large collection of semantic lists extracted from the web using several pre-defined HTML tag patterns. Then we introduce an efficient joint word representation learning model to capture semantics from both semantic knowledge and text corpora. Compared with recent work on improving WRL with semantic resources, our approach is more general, and can be easily scaled with no additional effort. Extensive experimental results show that our approach outperforms the state-of-the-art methods on word similarity, word sense disambiguation, text classification and textual similarity tasks.

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Himawan, Trinugi Wira Harjanti, Ruli Supriati, and Hari Setiyani. "Evolusi Penggunaan Teknologi Web 3.0 : Semantic Web." Journal of Information System,Graphics, Hospitality and Technology 2, no. 02 (2020): 54–60. http://dx.doi.org/10.37823/insight.v2i02.107.

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Generasi baru dari konsep teknologi yang digunakan pada halaman website mengalami perubahan yang juga selaras dengan kebutuhan bisnis yang ada dalam dunia digital. Halaman website mengalami transformasi dari pada mulanya adalah hanya berupa halaman website statis, kemudian pada versi atau generasi berikutnya menjadi halaman website 2.0 sampai dengan generasi ketiga atau halaman website 3.0 (web 3.0) atau yang juga dikenal dengan istilah website semantik (Semantic Web). Generasi ketiga dari teknologi halaman website inilah yang akan menjadi topik utama dari penulisan paper ini, dimana pembahasan yang akan dituliskan dan dijelaskan pada paper ini adalah mengenai karakteristik dari web 3.0, teknologi yang ada pada web 3.0 seperti RDF (Resource Description Framework), SPARQL dan Web Ontology. Kemudian dalam penulisan jurnal ini juga akan diberikan contoh atau implementasi dari penggunaan teknologi web 3.0 dan kemudian penjelasan teknis mengenai cara kerja web 3.0. Hasil dari penulisan paper ini adalah untuk mengetahui perbedaan apakah yang ditawarkan oleh halaman website yang mengadopsi atau menggunakan teknologi web 3.0 dan juga untuk mengetahui keunggulan yang dimiliki oleh teknologi web 3.0.

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JIANG, GUOFEI, GEORGE CYBENKO, and JAMES A. HENDLER. "SEMANTIC INTEROPERABILITY AND INFORMATION FLUIDITY." International Journal of Cooperative Information Systems 15, no. 01 (2006): 1–21. http://dx.doi.org/10.1142/s0218843006001268.

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Ontologies are developed to describe data semantics on the Semantic Web. Given the distributed nature and scale of the Semantic Web, a large number of ontologies with different terminologies and structures will be created to describe the same concepts and domains. Without semantic mapping, information fluidity within the Web could be blocked at the boundaries of these ontologies. Therefore, ontology mapping is needed to translate datasets represented by disparate ontologies. We believe that over time communities will incrementally build an ontology mapping between select ontologies based on their own communication interests. How will these interest-driven mapping activities eventually change semantic interoperability and information fluidity across the Web? This paper proposes metrics to quantify information fluidity and builds an analytical model with "small-world" graph theory to analyze the growth of the Semantic Web. Further with this model, we analyze how information fluidity can evolve by "market-driven" semantic mapping activities occurring across the Web. Our results can be useful in evaluating mapping efforts needed for large-scale heterogeneous information systems. One conclusion, based on this model, is that the development of decentralized ontology mappings can lead to significant information fluidity within the Semantic Web.

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Dzbor, M., A. Stutt, E. Motta, and T. Collins. "Representations for semantic learning webs: Semantic Web technology in learning support." Journal of Computer Assisted Learning 23, no. 1 (2007): 69–82. http://dx.doi.org/10.1111/j.1365-2729.2007.00202.x.

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Slimani, Thabet. "Semantic Annotation: The Mainstay of Semantic Web." International Journal of Computer Applications Technology and Research 2, no. 6 (2013): 763–70. http://dx.doi.org/10.7753/ijcatr0206.1025.

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Dorobăţ, Ilie Cristian, and Vlad Posea. "Iq Semantic: Semantic Web Intelligent Querying Framework." International conference KNOWLEDGE-BASED ORGANIZATION 26, no. 3 (2020): 103–7. http://dx.doi.org/10.2478/kbo-2020-0122.

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AbstractThe continuous expansion of the semantic web and of the linked open data cloud meant more semantic data are available for querying from endpoints all over the web. We propose extending a standard SPARQL interface with UI and Natural Language Processing features to allow easier and more intelligent querying. The paper describes some usage scenarios for easy querying and launches a discussion on the advantages of such an implementation.

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YENGİN, Deniz. "OPPORTUNITIES OF NEW MEDIA: SEMANTIC WEB." Turkish Online Journal of Design, Art and Communication 5, no. 1 (2015): 44–53. http://dx.doi.org/10.7456/10501100/004.

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Zhou, Lin Hua, Jian Bo Fan, and Ke Jia He. "SWBDS: Web Data Sources Management Infrastructure for Semantic Web Applications." Advanced Materials Research 186 (January 2011): 438–42. http://dx.doi.org/10.4028/www.scientific.net/amr.186.438.

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The Semantic Web envisions a World Wide Web in which datasources are encapsulated and described with rich semantics, and demander can issue complex queries. A critical problem in the situation is how to efficiently describe, organize and search these encapsulated datasources. This paper describes the Semantic Web-Based Data service(SWBDS for short) approach, which addresses these challenges. SWBDS introduces an ontology-based approach to, mapping web data sources to data service (DS for short), publishing DS with the shared domain ontology, and answering queries through DS provided interface. We define the domain ontology to illustrate the DS interface. The domain ontology is described in OWL that can be understood and processed by machines. Therefore, SWBDS can provide reasoning functions and facilitate datasources management with little human effort. This new system model makes full use of legacy applications and is flexible for future extensions.

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Celik, Duygu, and Atilla Elci. "Semantic composition of business processes using Armstrong's Axioms." Knowledge Engineering Review 29, no. 2 (2014): 248–64. http://dx.doi.org/10.1017/s0269888914000083.

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AbstractLack of sufficient semantic description in the content of Web services makes it difficult to find and compose suitable Web services during analysis, search, and matching processes. Semantic Web Services are Web services that have been enhanced with formal semantic description, which provides well-defined meaning. Due to insertion of semantics, meeting user demands will be made possible through logical deductions achieving resolutions automatically. We have developed an inference-based semantic business process composition agent (SCA) that employs inference techniques. The semantic composition agent system is responsible for the synthesis of new services from existing ones in a semi-automatic fashion. SCA System composes available Web Ontology Language for Web services atomic processes utilizing Revised Armstrong's Axioms (RAAs) in inferring functional dependencies. RAAs are embedded in the knowledge base ontologies of SCA System. Experiments show that the proposed SCA System produces process sequences as a composition plan that satisfies user's requirement for a complex task. The novelty of the SCA System is that for the first time Armstrong's Axioms are revised and used for semantic-based planning and inferencing of Web services.

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Ryen, Vetle, Ahmet Soylu, and Dumitru Roman. "Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review." Future Internet 14, no. 5 (2022): 129. http://dx.doi.org/10.3390/fi14050129.

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Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typically used for large-scale integration and analysis of data using graph-based data models. One of the central concepts in this area is the Semantic Web, with the vision of providing a well-defined meaning to information and services on the Web through a set of standards. Particularly, linked data and ontologies have been quite essential for data sharing, discovery, integration, and reuse. In this paper, we provide a systematic literature review on knowledge graph creation from structured and semi-structured data sources using Semantic Web technologies. The review takes into account four prominent publication venues, namely, Extended Semantic Web Conference, International Semantic Web Conference, Journal of Web Semantics, and Semantic Web Journal. The review highlights the tools, methods, types of data sources, ontologies, and publication methods, together with the challenges, limitations, and lessons learned in the knowledge graph creation processes.

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Коновальчук, Е., E. Konoval'chuk, В. Лавлинский, et al. "ALGORITHMS FOR COMBINING INFORMATION ABOUT WEB-PAGES WITH BACKGROUND ONTOLOGICAL KNOWLEDGE." Modeling of systems and processes 12, no. 2 (2019): 32–37. http://dx.doi.org/10.12737/article_5db1e3e60bef13.23500137.

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Currently, web information technologies are widely developed on the basis of the development of approaches related to the addition of semantics to web data, and the development of the apparatus of semantic networks. This article proposes the development of methods of semantic information retrieval based on algorithms of its association. This approach allows to use the analysis of web-pages with background ontological knowledge and increases its efficiency in comparison with standard approaches.

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El Guemhioui, Karim, and Steven A. Demurjian. "Semantic Reconciliation of Electronic Health Records Using Semantic Web Technologies." International Journal of Information Technology and Web Engineering 12, no. 2 (2017): 26–48. http://dx.doi.org/10.4018/ijitwe.2017040102.

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In this paper, the authors present an approach to reconcile the semantics of distinct medical terms found in personal health records (PHRs - that store data controlled by patients) and electronic medical records (EMRs - that store data controlled by providers) that are utilized to describe the same concept in different systems. The authors present a solution for semantic reconciliation based on RDF and related semantic web technologies. As part of the solution, the authors utilize a centralized repository of ontologies to: uniformly interrogate the medical coding systems in which those terms are defined, extract all of their published synonyms, and save the results as RDF triples. The final step in the process is to employ a reasoner to infer non-explicit synonymy among those terms, hence evidencing the underlying semantics to the PHR and EMR systems for possible further processing.

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Thuraisingham, Bhavani, Natasha Tsybulnik, and Ashraful Alam. "Administering the Semantic Web." International Journal of Information Security and Privacy 1, no. 1 (2007): 18–34. http://dx.doi.org/10.4018/jisp.2007010102.

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Hitzler, Pascal, and Frank van Harmelen. "A reasonable Semantic Web." Semantic Web 1, no. 1,2 (2010): 39–44. http://dx.doi.org/10.3233/sw-2010-0010.

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Eljinini, Mohammad Ali H. "The Medical Semantic Web." International Journal of Information Technology and Web Engineering 6, no. 2 (2011): 18–28. http://dx.doi.org/10.4018/jitwe.2011040102.

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In this paper, the need for the right information for patients with chronic diseases is elaborated, followed by some scenarios of how the semantic web can be utilised to retrieve useful and precise information by stakeholders. In previous work, the author has demonstrated the automation of knowledge acquisition from the current web is becoming an important step towards this goal. The aim was twofold; first to learn what types of information exist in chronic disease-related websites, and secondly how to extract and structure such information into machine understandable form. It has been shown that these websites exhibit many common concepts which resulted in the construction of the ontology to guide in extracting information for new unseen websites. Also, the study has resulted in the development of a platform for information extraction that utilises the ontology. Continuous work has opened many issues which are disussed in this paper. While further work is still needed, the experiments to date have shown encouraging results.

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Murray-Rust, P., S. Tyrrell, and Y. Zhang. "The crystallographic semantic web." Acta Crystallographica Section A Foundations of Crystallography 61, a1 (2005): c481. http://dx.doi.org/10.1107/s0108767305080025.

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La Barre, Kathryn. "A semantic (faceted) web?" Les cahiers du numérique 6, no. 3 (2010): 103–31. http://dx.doi.org/10.3166/lcn.6.3.103-131.

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Li, Yuan-Fang, Jeff Z. Pan, Shonali Krishnaswamy, Manfred Hauswirth, and Hai H. Nguyen. "The Ubiquitous Semantic Web." International Journal on Semantic Web and Information Systems 10, no. 4 (2014): 1–16. http://dx.doi.org/10.4018/ijswis.2014100101.

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The Semantic Web represents an evolution of the World Wide Web towards one of entities and their relationships, rather than pages and links. Such a progression makes it possible to represent, integrate, query and reason about structured online data. Recent years have witnessed tremendous growth of mobile computing, represented by the widespread adoption of smart phones and tablets. The versatility of such smart devices and the capabilities of semantic technologies form a great foundation for a ubiquitous Semantic Web that will contribute to further realising the true potential of both disciplines. In this paper, the authors argue for values provided by the ubiquitous Semantic Web using a mobile service discovery scenario. They also provide a brief overview of state-of-the-art research in this emerging area. Finally, the authors conclude with a summary of challenges and important research problems.

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