MANSION-GS: seMANtics as the n-th dimenSION for Geographic Space

The extended understanding of geographic ecosystems, including the physical and logical description of space with associated data and activities as well as the dynamics inside, poses complex scenarios that cannot be obtained from a simple geographic-oriented data model. The main purpose of this current work is the conceptual integration of a physical space model with dynamic logic support able to describe the relations amongst the different elements composing the space as well as the relations between spaces and external elements. In the context of this work, semantics have the critical and central role of connecting and relating the different dimensions on the space, even though they are mostly a virtual dimension in the overall model

Uncertainty in Automated Ontology Matching: Lessons Learned from an Empirical Experimentation

Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such a process by providing well-consolidated support to link and semantically integrate datasets via interoperability. This paper approaches data integration from an application perspective, looking at techniques based on ontology matching. An ontology-based process may only be considered adequate by assuming manual matching of different sources of information. However, since the approach becomes unrealistic once the system scales up, automation of the matching process becomes a compelling need. Therefore, we have conducted experiments on actual data with the support of existing tools for automatic ontology matching from the scientific community. Even considering a relatively simple case study (i.e., the spatio-temporal alignment of global indicators), outcomes clearly show significant uncertainty resulting from errors and inaccuracies along the automated matching process. More concretely, this paper aims to test on real-world data a bottom-up knowledge-building approach, discuss the lessons learned from the experimental results of the case study, and draw conclusions about uncertainty and uncertainty management in an automated ontology matching process. While the most common evaluation metrics clearly demonstrate the unreliability of fully automated matching solutions, properly designed semi-supervised approaches seem to be mature for a more generalized application

Analysis techniques and models for resource optimization in Wireless Sensor/Actuator Network environment

Abstract. In the last few years, WSN has been object of an intense research activity that has determined an important improvement by technologic and computation point of view both. The notable level got and the increasing request of applications designed over Sensor Networks make WSN commercial diffusion next to be a fact. Limited resource orientation and high level application requirements result in a number of key open issues, such as Resource Optimization and Quality of Service. These last two issues require an important preliminary phase of analysis and evaluation that can provide the designer with knowledge of important relationships between parameters design and application desired characteristics. Mathematical models of local resource (node), of network influence on single resource, of QoS requests, and related analysis techniques to determine not only "how much" but also "in which way" resources are expensed are proposed in this paper

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

An individual-centric probabilistic extension for OWL: modelling the uncertainness

The theoretical benefits of semantics as well as their potential impact on IT are well known concepts, extensively discussed in literature. As more and more systems are currently using or referring semantic technologies, the challenging third version of the web (Semantic Web or Web 3.0) is progressively taking shape. On the other hand, apart from the relatively limited capabilities in terms of expressiveness characterizing current concrete semantic technologies, theoretical models and research prototypes are actually overlooking a significant number of practical issues including, among others, consolidated mechanisms to manage and maintain vocabularies, shared notations systems and support to high scale systems (Big Data). Focusing on the OWL model as the current reference technology to specify web semantics, in this paper we will discuss the problem of approaching the knowledge engineering exclusively according to a deterministic model and excluding a priori any kind of probabilistic semantic. Those limitations determine that most knowledge ecosystems including, at some level, probabilistic information are not well suited inside OWL environments. Therefore, despite the big potential of OWL, a consistent number of applications are still using more classic data models or unnatural hybrid environments. But OWL, even with its intrinsic limitations, reflects a model flexible enough to support extensions and integrations. In this work we propose a simple statistical extension for the model that can significantly spread the expressiveness and the purpose of OWL

A privacy-friendly model for an efficient and effective activity scheduling inside dynamic virtual organizations

The cooperation among people is one of the key factors for most processes and activities. The efficiency and the effectiveness of the cooperation has an intrinsic value, which significantly affects performances and outcomes. Open communities, as well as spontaneous or predefined virtual organizations, are demanding for a more solid and consistent support for activity scheduling and managing in a context of flexibility and respect of the individual needs. This paper proposes a privacy-friendly model to support virtual organizations in the scheduling and management of their most valuable resource: the time

Optimistic scheduling: facilitating the collaboration by prioritizing the individual needs

The collaboration among people is one of the key factors for the optimization of many processes and activities. The efficiency and the effectiveness of the collaboration has an intrinsic value which significantly affects performances and outcomes, at a quantitative and a qualitative level both. Open communities, as well as spontaneous or predefined virtual organizations, are demanding for a more solid and consistent support for activity scheduling and managing in a context of flexibility and respect of individual needs. This paper proposes a privacy-friendly model that can be materialized in concrete tools and applications to support virtual organizations in the scheduling and management of the most valuable resource: the time. The model is formally defined and, than, analysed and evaluated by simulations as the function of complex user behaviours. Finally, an implementation of the basic prototype aimed at a large scale deployment is described

Looking deeper into academic citations through network analysis: popularity, influence and impact

Google Scholar (GS) has progressively emerged as a tool which “provides a simple way to broadly search for scholarly literature across many disciplines and sources.” As a free tool that provides citation metrics, GS has opened the academic word to a much larger audience, according to an open information philosophy. GS’ profiles are largely used not only to have a quick look at the authors and their works but, more and more often, as a “de facto” metric to quickly evaluate the research impact. This process looks unstoppable and discussing about its fairness, advantages and disadvantages, as well as about social implications is out of the scope of this paper. We rather prefer to (1) briefly discuss the changes and the innovation that GS has introduced and to (2) propose possible improvements for analysis on academic citations. Our methods are aimed at considering a GS profile in its proper context, providing a social perspective on academic citations: Although maintaining a fundamentally quantitative focus, novel approaches, based on complex network analysis, distinguish between a research impact on the authors’ research network and a more general impact on the scientific community

Web of similarity

Despite the achieved maturity and popularity, the current semantic technology has severe limitations in real-world applications as it is unable to represent uncertain knowledge. Probabilistic Semantics partially address this issue. Unfortunately, their quantitative approach fails in many practical applications that require a more abstracted vision and conceptual model of the uncertainties. Indeed, Probabilistic Semantics can only model ecosystems where all the uncertainties are quantified. In this paper, we introduce a qualitative approach for the representation of the uncertainties in the Semantic Web. We propose a human-inspired model that defines the uncertainty as an explicit similarity, providing a flexible range of solutions for approximate semantic reasoning in uncertain ecosystems. The resulting semantic environment, referred to as Web of Similarity (WoS), is an extension of the Web of Data which is able to represent and process analogies among concepts and individuals. As the generic Semantic Web, the Web of Similarity is a global semantic infrastructure that can support specific systems or applications at a global scale. WoS is a step forward to get richer Web Semantics which are closer to the human ones