Using an ontology for interoperability and browsing of museum, library and archive information

Ontologies play an important part in the development of the future ‘semantic web’; the CIDOC conceptual reference model (CRM) is an ontology aimed at the cultural heritage domain. This paper describes a Concept Browser, developed for the EU/IST-funded SCULPTEUR project (semantic and content-based multimedia exploitation for European benefit environment (programme IST-2001-no. 35372); May 2002 to May 2005), which is able to access different museum information systems through a common ontology, the CRM. The development of this Concept Browser has required mappings from the legacy museum database systems to the CRM. The crucial process of creating the mappings is described, using the C2RMF catalogue (EROS) and library databases as a case study

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. 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

A schema-based P2P network to enable publish-subscribe for multimedia content in open hypermedia systems

Open Hypermedia Systems (OHS) aim to provide efficient dissemination, adaptation and integration of hyperlinked multimedia resources. Content available in Peer-to-Peer (P2P) networks could add significant value to OHS provided that challenges for efficient discovery and prompt delivery of rich and up-to-date content are successfully addressed. This paper proposes an architecture that enables the operation of OHS over a P2P overlay network of OHS servers based on semantic annotation of (a) peer OHS servers and of (b) multimedia resources that can be obtained through the link services of the OHS. The architecture provides efficient resource discovery. Semantic query-based subscriptions over this P2P network can enable access to up-to-date content, while caching at certain peers enables prompt delivery of multimedia content. Advanced query resolution techniques are employed to match different parts of subscription queries (subqueries). These subscriptions can be shared among different interested peers, thus increasing the efficiency of multimedia content dissemination

A lightweight web video model with content and context descriptions for integration with linked data

The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches

Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches

Multimedia search without visual analysis: the value of linguistic and contextual information

This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features

Video semantic content analysis framework based on ontology combined MPEG-7

The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. New multimedia standard, MPEG-7, provides the rich functionalities to enable the generation of audiovisual descriptions and is expressed solely in XML Schema which provides little support for expressing semantic knowledge. In this paper, a video semantic content analysis framework based on ontology combined MPEG-7 is presented. Domain ontology is used to define high level semantic concepts and their relations in the context of the examined domain. MPEG-7 metadata terms of audiovisual descriptions and video content analysis algorithms are expressed in this ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how low-level features and algorithms for video analysis should be applied according to different perception content. Temporal Description Logic is used to describe the semantic events, and a reasoning algorithm is proposed for events detection. The proposed framework is demonstrated in sports video domain and shows promising results