Semantic Association Analysis in Ontology-Based Information Retrieval

Semantic Association Analysis in Ontology-Based Information Retrieval

Payam M. Barnaghi (University of Nottingham, Malaysia), Wei Wang (University of Nottingham, Malaysia) and Jayan C. Kurian (University of Nottingham, Malaysia)
DOI: 10.4018/978-1-59904-879-6.ch013
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The Semantic Web is an extension to the current Web in which information is provided in machine-processable format. It allows interoperable data representation and expression of meaningful relationships between the information resources. In other words, it is envisaged with the supremacy of deduction capabilities on the Web, that being one of the limitations of the current Web. In a Semantic Web framework, an ontology provides a knowledge sharing structure. The research on Semantic Web in the past few years has offered an opportunity for conventional information search and retrieval systems to migrate from keyword to semantics-based methods. The fundamental difference is that the Semantic Web is not a Web of interlinked documents; rather, it is a Web of relations between resources denoting real world objects, together with well-defined metadata attached to those resources. In this chapter, we first investigate various approaches towards ontology development, ontology population from heterogeneous data sources, semantic association discovery, semantic association ranking and presentation, and social network analysis, and then we present our methodology for an ontology-based information search and retrieval. In particular, we are interested in developing efficient algorithms to resolve the semantic association discovery and analysis issues.
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The current Web provides a universal platform to explore and contribute to the global information network. Undoubtedly, the Web has emerged as the world’s major information resource with immediate accessibility in a world-wide scale. Currently, in most of the cases, in order to transform available information into meaningful knowledge, machines have to depend on the human inference ability (Craven, DiPasquo, Freitag, McCallum, Mitchell, Nigam, et al., 2000). Contemporary popular online search engines and information retrieval systems index and search the Web documents based on analysis of the document link structures and keywords. The keywords are often extracted from the documents according to the frequency of occurrence and considered as standalone entities without application contexts and other semantic relationships. This superficial understanding of content prevents retrieving implicitly-related information in most of the cases. It also in some cases returns irrelevant results to the user. In the context of multimedia Web, the current search systems are even more limited. Most of the multimedia search on the current Web relies on text explanations extracted from accompanying pages or tags provided by content authors. There are commercially successful Web sites for multimedia publishing, sharing, and retrieval on the Web such as MySpace1, YouTube2, and Flickr3. These Web sites have demonstrated a great achievement in acquiring millions of users to form communities and contribute to content generation; they also provide interfaces to search and view the published contents, but the search functions regularly rely on conventional methods and keyword matching mechanisms. As overwhelming information is published on the Web, new information search and retrieval methods are needed in order to enable users to find more relevant information based not only on keywords, but also context and preferences of each individual user. This has lead to the introduction of a new era for Web information search and retrieval, namely, community-based and semantic-enhanced search.

The emergent Semantic Web technologies provide the possibility to realize the vision of meaningful relations and structured data on the Web. As an extension to the current Web, the Semantic Web technologies enable computers and people to work in cooperation (Berners-Lee, Hendler, & Lassila, 2001). The Semantic Web focuses on publishing and retrieving machine-processable Web contents (Dayal, Kuno, & Wilkinson, 2003). In the Semantic Web framework, flexible and interoperable structures such as Web ontology language (OWL)4 and resource description framework (RDF)5 are used to represent resources. The relationships between entities in a particular domain can be explicitly expressed using an ontology (Chandrasekaran, Josephson, & Benjamins, 1999). To describe multimedia data and documents on the Semantic Web, the ontology concepts are required to be mapped to the metadata description structure, which is usually referred to as semantic annotation. The semantic-enhanced search focuses on utilizing the structured description and knowledge description ontologies to enhance the results of information search and retrieval process on the Web. The better the relationships are processed and analyzed, the more relevant context results are obtained to be shown to users.

Key Terms in this Chapter

Ontology: Object description and relationship between objects in a domain.

Semantic Association Analysis: Discovering complex and meaningful relationship between objects.

Semantic Web: A Web of relations between resources together with well-defined metadata attached to those resources.

Information Search and Retrieval: Finding out queried information and its descriptive details.

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