14,961 research outputs found
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.; 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. 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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 TrendsÂź in Information Retrieval, 2(1â2), 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
Embedded intelligence for electrical network operation and control
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
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
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 âprogramâ behavior in-or train-each other
Knowledge Representation with Ontologies: The Present and Future
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
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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