How Ontology Based Information Retrieval Systems May Benefit from Lexical Text Analysis

Abstract

AbstractThe exponential growth of available electronic data is almost useless without efficient tools to retrieve the right information at the right time. This is especially crucial in the context of decision making (e.g. for politicians), innovative development (e.g. for scientists and industrials) or economic development (e.g. for market or concurrence studies). It is now widely acknowledged that information retrieval systems (IRS in short) need to take semantics into account. In this context, semantic Web technologies have been rapidly widespread and accepted. This article surveys semantic based methodologies designed to efficiently retrieve and exploit information. Some of them, based on terminologies, are fitted to open context, dealing with heterogeneous and unstructured data, while others, based on taxonomies or ontologies, are semantically richer but require formal knowledge representation of the studied domain. Hence, a continuum of solutions exists from terminology to ontology based IRSs. These approaches are often seen as concurrent and exclusive, but this chapter asserts that their advantages may be efficiently combined in a hybrid solution built upon domain ontology. The original approach presented here benefits from both lexical and ontological document description, and combines them in a software architecture dedicated to information retrieval in specific domains. Relevant documents are first identified via their conceptual indexing based on domain ontology, and then each document is segmented to highlight text fragments that deal with users’ information needs.The system thus specifies why these documents have been chosen and facilitates end-user information gathering.KeywordsDomain OntologyQuery TermInformation Retrieval SystemText SegmentationOntology ConceptThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.