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Data & Knowledge Engineering 64 (2008) 600–623 www.elsevier.com/locate/datak
Learning non-taxonomic relationships from web documents for domain ontology construction David Sa´nchez *, Antonio Moreno Intelligent Technologies for Advanced Knowledge Acquisition (ITAKA) Research Group, Department of Computer Science and Mathematics, Universitat Rovira i Virgili (URV), Avda, Paı¨sos Catalans, 26, 43007 Tarragona, Spain Received 16 April 2007; accepted 2 October 2007 Available online 7 October 2007
Abstract In recent years, much effort has been put in ontology learning. However, the knowledge acquisition process is typically focused in the taxonomic aspect. The discovery of non-taxonomic relationships is often neglected, even though it is a fundamental point in structuring domain knowledge. This paper presents an automatic and unsupervised methodology that addresses the non-taxonomic learning process for constructing domain ontologies. It is able to discover domain-related verbs, extract non-taxonomically related concepts and label relationships, using the Web as corpus. The paper also discusses how the obtained relationships can be automatically evaluated against WordNet and presents encouraging results for several domains. 2007 Elsevier B.V. All rights reserved. Keywords: Ontology learning; Non-taxonomic relationships; Web mining; Knowledge acquisition
1. Introduction Ontologies are defined as formal, explicit specifications of a shared conceptualization [49]. They are an essential component in many knowledge-intensive areas like the Semantic Web [54], knowledge management, and electronic commerce. The construction of domain ontologies relies on domain modellers and knowledge engineers, which are typically overwhelmed by the potential size, complexity and dynamicity of a specific domain. In consequence, the definition of exhaustive domain ontologies is a barrier that very few projects can overcome. Due to these reasons, nowadays, there is a need of methods that can tackle, or at least ease, the construction of domain ontologies. Automated Ontology Learning methods allow a reduction in the time and effort needed in the ontology development process [2]. From a formal point of view, an ontology boils down to an object model represented by a set of concepts or classes C, which are taxonomically related by the transitive IS-A relation H C · C and non-taxonomically related by named object relations R * C · C · String. Even though many approaches for ontology learning *
Corresponding author. Tel.: +34 977 559681; fax: +34 977 559710. E-mail address:
[email protected] (D. Sa´nchez).
0169-023X/$ - see front matter 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.datak.2007.10.001
D. Sa´nchez, A. Moreno / Data & Knowledge Engineering 64 (2008) 600–623
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David Sa´nchez is currently developing his Ph.D. at Technical University of Catalonia. He is a member of the ITAKA research group (Intelligent Techniques for Advanced Knowledge Acquisition) at the Computer Science and Mathematics Department in the University Rovira i Virgili. His research interests are intelligent agents and ontology learning from the Web.
Antonio Moreno is a Lecturer at the University Rovira i Virgili’s Computer Science and Mathematics Department. He is the founder and head of the ITAKA research group (Intelligent Techniques for Advanced Knowledge Acquisition). His main research interests are the application of agent technology to healthcare problems and ontology learning from the Web. He received a PhD on Artificial Intelligence from UPC (Technical University of Catalonia) in 2000. He is the coordinator of the Spanish network on agents and multi-agent technology.
