Hacia una web semántica social

The internet’s evolution toward a scenario of greater potential, with users increasingly involved in its development and management, demands a lower level of semantic ambiguity in the documents that are provided. The proposed semantic web, a concept that appeared almost a decade ago, has had only modest impact. On the other hand, web 2.0, an autonomous evolution of the web toward a collaborative environment, has met with enormous success. The solution devised by web 2.0 has exceeded the limitations of natural language processing tools and statistical approaches. For that reason, it seems logical to analyze the potential contributions that web 2.0 concepts could make to further the development of the semantic web. Some ways of making the leap from a social web to a social semantic web are discussed

Toward a social semantic web environment

The internet’s evolution toward a scenario of greater potential, with users increasingly involved in its development and management, demands a lower level of semantic ambiguity in the documents that are provided. The proposed semantic web, a concept that appeared almost a decade ago, has had only modest impact. On the other hand, web 2.0, an autonomous evolution of the web toward a collaborative environment, has met with enormous success. The solution devised by web 2.0 has exceeded the limitations of natural language processing tools and statistical approaches. For that reason, it seems logical to analyze the potential contributions that web 2.0 concepts could make to further the development of the semantic web. Some ways of making the leap from a social web to a social semantic web are discussed

Hacia una web semántica social

The internet’s evolution toward a scenario of greater potential, with users increasingly involved in its development and management, demands a lower level of semantic ambiguity in the documents that are provided. The proposed semantic web, a concept that appeared almost a decade ago, has had only modest impact. On the other hand, web 2.0, an autonomous evolution of the web toward a collaborative environment, has met with enormous success. The solution devised by web 2.0 has exceeded the limitations of natural language processing tools and statistical approaches. For that reason, it seems logical to analyze the potential contributions that web 2.0 concepts could make to further the development of the semantic web. Some ways of making the leap from a social web to a social semantic web 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. 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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. 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TellEat: sharing experiences on the move

In a context where, due to the proliferation of mobile devices, virtual social environments on the Web are taking up a very concrete role in the way people experience their surroundings, the Future Internet seems to be headed toward a mixture of Social Web, Semantic Web and Augmented Reality. As a part of a larger project that aims at building a social network of both people and things, we designed and developed TellEat, an iPhone-based application that allows users in mobility to share facts concerning people or objects that participate in the social network, and to discover pertinent events that have been told by others. In this paper we discuss both the client application, with the interaction model and interface metaphors that have been designed to make the experience as playful as possible for users, and the server-side services that provide the necessary knowledge and reasoning mechanisms. We also present the results of preliminary tests with users

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201

Challenges and potential of the Semantic Web for tourism

The paper explores tourism challenges and potential of the Semantic Web from a theoretical and industry perspective. It first examines tourism business networks and explores a main theme of network interoperability - data standards- followed by technology deficiencies of Web 1.0 and 2.0 and Semantic Web solutions. It then explicates Semantic opportunities and challenges for tourism, including an industry perspective through a qualitative approach. Industry leaders considered that the new Web era was imminent and heralded benefits for supply and demand side interoperability, although management and technical challenges could impede progress and delay realisation

Common vocabularies for collective intelligence - work in progress

Web based applications and tools offer a great potential to increase the efficiency of information flow and communication among different agents during emergencies. Among the different factors, technical and non technical, that hinder the integration of an information model in emergency management sector, is a lack of a common, shared vocabulary. This paper furthers previous work in the area of ontology development, and presents a summary and overview of the goal, process and methodology to construct a shared set of metadata that can be used to map existing vocabulary. This paper is a work in progress report

Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference. Our method results in almost no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 47.5% and 40.5% for multilabel classification and visual semantic role labeling, respectively.Comment: 11 pages, published in EMNLP 201