Collaborative editing of knowledge resources for cross-lingual text mining

The need to smoothly deal with textual documents expressed in different languages is increasingly becoming a relevant issue in modern text mining environments. Recently the research on this field has been considerably fostered by the necessity for Web users to easily search and browse the growing amount of heterogeneous multilingual contents available on-line as well as by the related spread of the Semantic Web. A common approach to cross-lingual text mining relies on the exploitation of sets of properly structured multilingual knowledge resources. The involvement of huge communities of users spread over different locations represents a valuable aid to create, enrich, and refine these knowledge resources. Collaborative editing Web environments are usually exploited to this purpose. This thesis analyzes the features of several knowledge editing tools, both semantic wikis and ontology editors, and discusses the main challenges related to the design and development of this kind of tools. Subsequently, it presents the design, implementation, and evaluation of the Wikyoto Knowledge Editor, called also Wikyoto. Wikyoto is the collaborative editing Web environment that enables Web users lacking any knowledge engineering background to edit the multilingual network of knowledge resources exploited by KYOTO, a cross-lingual text mining system developed in the context of the KYOTO European Project. To experiment real benefits from social editing of knowledge resources, it is important to provide common Web users with simplified and intuitive interfaces and interaction patterns. Users need to be motivated and properly driven so as to supply information useful for cross-lingual text mining. In addition, the management and coordination of their concurrent editing actions involve relevant technical issues. In the design of Wikyoto, all these requirements have been considered together with the structure and the set of knowledge resources exploited by KYOTO. Wikyoto aims at enabling common Web users to formalize cross-lingual knowledge by exploiting simplified language-driven interactions. At the same time, Wikyoto generates the set of complex knowledge structures needed by computers to mine information from textual contents. The learning curve of Wikyoto has been kept as shallow as possible by hiding the complexity of the knowledge structures to the users. This goal has been pursued by both enhancing the simplicity and interactivity of knowledge editing patterns and by using natural language interviews to carry out the most complex knowledge editing tasks. In this context, TMEKO, a methodology useful to support users to easily formalize cross-lingual information by natural language interviews has been defined. The collaborative creation of knowledge resources has been evaluated in Wikyoto

Mining the social semantic Web for making cross-domain recommendations

This is an electronic version of the paper presented at the Fifth BCS-IRSG Symposium on Future Directions in Information Access, held in Granada on 2003Cross-domain recommender systems filter and suggest items in a target domain by exploiting user preferences and/or domain knowledge available in a (likely related) source domain. In our research we are developing a framework for cross-domain recommendation capable of mining heterogeneous sources of information available in the so-called Social Semantic Web, such as semantically annotated data, user generated contents, and contextual signals

SOCIAL NETWORK DATA RETRIEVAL USING SEMANTIC TECHNOLOGY

Social network data analysis is an important problem due to proliferation of social network applications, amount of data these applications generate and potential of insight based on this big data. The objective of present work is to propose architecture for a semantic web application to facilitate meaningful social network data analytics as well as answering query about concerned ontology. Proposed technique links, on one hand, tools based on semantic technology provided by social network applications with data analytics tools and on the other hand extends this link to ontology authoring tools for further inference.   Results obtained from data analytics tool, results of query on generated ontology and benchmarking of the performance of data analytics tool are shown. It has been observed that a semantic web application utilizing above mentioned tools and technologies is more versatile and flexible and further improvements are possible by applying generic data mining algorithms to the above scenario.   Â

An Introduction to Social Semantic Web Mining & Big Data Analytics for Political Attitudes and Mentalities Research

The social web has become a major repository of social and behavioral data that is of exceptional interest to the social science and humanities research community. Computer science has only recently developed various technologies and techniques that allow for harvesting, organizing and analyzing such data and provide knowledge and insights into the structure and behavior or people on-line. Some of these techniques include social web mining, conceptual and social network analysis and modeling, tag clouds, topic maps, folksonomies, complex network visualizations, modeling of processes on networks, agent based models of social network emergence, speech recognition, computer vision, natural language processing, opinion mining and sentiment analysis, recommender systems, user profiling and semantic wikis. All of these techniques are briefly introduced, example studies are given and ideas as well as possible directions in the field of political attitudes and mentalities are given. In the end challenges for future studies are discussed

When the Social Meets the Semantic: Social Semantic Web or Web 2.5

The social trend is progressively becoming the key feature of current Web understanding (Web 2.0). This trend appears irrepressible as millions of users, directly or indirectly connected through social networks, are able to share and exchange any kind of content, information, feeling or experience. Social interactions radically changed the user approach. Furthermore, the socialization of content around social objects provides new unexplored commercial marketplaces and business opportunities. On the other hand, the progressive evolution of the web towards the Semantic Web (or Web 3.0) provides a formal representation of knowledge based on the meaning of data. When the social meets semantics, the social intelligence can be formed in the context of a semantic environment in which user and community profiles as well as any kind of interaction is semantically represented (Semantic Social Web). This paper first provides a conceptual analysis of the second and third version of the Web model. That discussion is aimed at the definition of a middle concept (Web 2.5) resulting in the convergence and integration of key features from the current and next generation Web. The Semantic Social Web (Web 2.5) has a clear theoretical meaning, understood as the bridge between the overused Web 2.0 and the not yet mature Semantic Web (Web 3.0).Pileggi, SF.; Fernández Llatas, C.; Traver Salcedo, V. (2012). When the Social Meets the Semantic: Social Semantic Web or Web 2.5. Future Internet. 4(3):852-854. doi:10.3390/fi4030852S85285443Chi, E. H. (2008). The Social Web: Research and Opportunities. Computer, 41(9), 88-91. doi:10.1109/mc.2008.401Bulterman, D. C. A. (2001). SMIL 2.0 part 1: overview, concepts, and structure. IEEE Multimedia, 8(4), 82-88. doi:10.1109/93.959106Boll, S. (2007). MultiTube--Where Web 2.0 and Multimedia Could Meet. IEEE Multimedia, 14(1), 9-13. doi:10.1109/mmul.2007.17Fraternali, P., Rossi, G., & Sánchez-Figueroa, F. (2010). Rich Internet Applications. IEEE Internet Computing, 14(3), 9-12. doi:10.1109/mic.2010.76Lassila, O., & Hendler, J. (2007). Embracing «Web 3.0». IEEE Internet Computing, 11(3), 90-93. doi:10.1109/mic.2007.52Dikaiakos, M. D., Katsaros, D., Mehra, P., Pallis, G., & Vakali, A. (2009). Cloud Computing: Distributed Internet Computing for IT and Scientific Research. IEEE Internet Computing, 13(5), 10-13. doi:10.1109/mic.2009.103Mangione-Smith, W. H. (1998). Mobile computing and smart spaces. IEEE Concurrency, 6(4), 5-7. doi:10.1109/4434.736391Greaves, M. (2007). Semantic Web 2.0. IEEE Intelligent Systems, 22(2), 94-96. doi:10.1109/mis.2007.40Bojars, U., Breslin, J. G., Peristeras, V., Tummarello, G., & Decker, S. (2008). Interlinking the Social Web with Semantics. IEEE Intelligent Systems, 23(3), 29-40. doi:10.1109/mis.2008.50Definition of Web 2.0http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.htmlZhang, D., Guo, B., & Yu, Z. (2011). The Emergence of Social and Community Intelligence. Computer, 44(7), 21-28. doi:10.1109/mc.2011.65Pentlan, A. (2005). Socially aware, computation and communication. Computer, 38(3), 33-40. doi:10.1109/mc.2005.104Staab, S., Domingos, P., Mika, P., Golbeck, J., Li Ding, Finin, T., … Vallacher, R. R. (2005). Social Networks Applied. IEEE Intelligent Systems, 20(1), 80-93. doi:10.1109/mis.2005.16The Semantic Webhttp://www.scientificamerican.com/article.cfm?id=the-semantic-webDecker, S., Melnik, S., van Harmelen, F., Fensel, D., Klein, M., Broekstra, J., … Horrocks, I. (2000). The Semantic Web: the roles of XML and RDF. IEEE Internet Computing, 4(5), 63-73. doi:10.1109/4236.877487OWL Web Ontology Language Overviewhttp://www.w3.org/TR/owl-features/Vetere, G., & Lenzerini, M. (2005). Models for semantic interoperability in service-oriented architectures. IBM Systems Journal, 44(4), 887-903. doi:10.1147/sj.444.0887Fensel, D., & Musen, M. A. (2001). The semantic web: a brain for humankind. IEEE Intelligent Systems, 16(2), 24-25. doi:10.1109/mis.2001.920595Shadbolt, N., Berners-Lee, T., & Hall, W. (2006). The Semantic Web Revisited. IEEE Intelligent Systems, 21(3), 96-101. doi:10.1109/mis.2006.62Dodds, P. S., & Danforth, C. M. (2009). Measuring the Happiness of Large-Scale Written Expression: Songs, Blogs, and Presidents. Journal of Happiness Studies, 11(4), 441-456. doi:10.1007/s10902-009-9150-9Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135. doi:10.1561/1500000011Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163-173. doi:10.1002/asi.21662Blogmeterhttp://www.blogmeter.it/Christakis, N. A., & Fowler, J. H. (2010). Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE, 5(9), e12948. doi:10.1371/journal.pone.0012948Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169-2188. doi:10.1002/asi.21149Bernal, P. A. (2010). Web 2.5: The Symbiotic Web. International Review of Law, Computers & Technology, 24(1), 25-37. doi:10.1080/13600860903570145Mikroyannidis, A. (2007). Toward a Social Semantic Web. Computer, 40(11), 113-115. doi:10.1109/mc.2007.405Jung, J. J. (2012). Computational reputation model based on selecting consensus choices: An empirical study on semantic wiki platform. Expert Systems with Applications, 39(10), 9002-9007. doi:10.1016/j.eswa.2012.02.03

Computing semantic relatedness using DBPedia

Extracting the semantic relatedness of terms is an important topic in several areas, including data mining, information retrieval and web recommendation. This paper presents an approach for computing the semantic relatedness of terms using the knowledge base of DBpedia — a community effort to extract structured information from Wikipedia. Several approaches to extract semantic relatedness from Wikipedia using bag-of-words vector models are already available in the literature. The research presented in this paper explores a novel approach using paths on an ontological graph extracted from DBpedia. It is based on an algorithm for finding and weighting a collection of paths connecting concept nodes. This algorithm was implemented on a tool called Shakti that extract relevant ontological data for a given domain from DBpedia using its SPARQL endpoint. To validate the proposed approach Shakti was used to recommend web pages on a Portuguese social site related to alternative music and the results of that experiment are reported in this paper