14,961 research outputs found

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

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    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.; FernĂĄndez 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., & SĂĄnchez-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., 
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    Embedded intelligence for electrical network operation and control

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    Integrating multiple types of intelligent, mulitagent data analysis within a smart grid can pave the way for flexible, extensible, and robust solutions to power network management

    DNA-inspired online behavioral modeling and its application to spambot detection

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    We propose a strikingly novel, simple, and effective approach to model online user behavior: we extract and analyze digital DNA sequences from user online actions and we use Twitter as a benchmark to test our proposal. We obtain an incisive and compact DNA-inspired characterization of user actions. Then, we apply standard DNA analysis techniques to discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports our proposal, showing its effectiveness and viability. To the best of our knowledge, we are the first ones to identify and adapt DNA-inspired techniques to online user behavioral modeling. While Twitter spambot detection is a specific use case on a specific social media, our proposed methodology is platform and technology agnostic, hence paving the way for diverse behavioral characterization tasks

    Using humanoid robots to study human behavior

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    Our understanding of human behavior advances as our humanoid robotics work progresses-and vice versa. This team's work focuses on trajectory formation and planning, learning from demonstration, oculomotor control and interactive behaviors. They are programming robotic behavior based on how we humans “program” behavior in-or train-each other

    Knowledge Representation with Ontologies: The Present and Future

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    Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent

    Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management

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    Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential customers). In addition, this technique can be used with Rapid Miner tools for both labeled and unlabeled data
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