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Abstract

Domain ontologies very rarely model verbs as relations holding between concepts. However, the role of the verb as a central connecting element between concepts is undeniable. Verbs specify the interaction between the participants of some action or event by expressing relations between them. In parallel, it can be argued from an ontology engineering point of view that verbs express a relation between two classes that specify domain and range. The work described here is concerned with relation extraction for ontology extension along these lines. We describe a system (RelExt) that is capable of automatically identifying highly relevant triples (pairs of concepts connected by a relation) over concepts from an existing ontology. RelExt works by extracting relevant verbs and their grammatical arguments (i.e. terms) from a domain-specific text collection and computing corresponding relations through a combination of linguistic and statistical processing. The paper includes a detailed description of the system architecture and evaluation results on a constructed benchmark. RelExt has been developed in the context of the SmartWeb project, which aims at providing intelligent information services via mobile broadband devices on the FIFA World Cup that will be hosted in Germany in 2006. Such services include location based navigational information as well as question answering in the football domain.

Series

Lecture notes in computer science; vol. xxx

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Abstract

Standardization efforts in fnancial reporting have led to large numbers of machine-interpretable vocabularies that attempt to model complex accounting practices in XBRL (eXtended Business Reporting Language). Because reporting agencies do not require fine-grained semantic and terminological representations, these vocabularies cannot be easily reused. Ontology-based Information Extraction, in particular, requires much greater semantic and terminological structure, and the introduction of a linguistic structure currently absent from XBRL. In order to facilitate such reuse, we propose a three-faceted methodology that analyzes and enriches the XBRL vocabulary: (1) transform semantic structure by analyzing the semantic relationships between terms (e.g. taxonomic, meronymic); (2) enhance terminological structure by using several domain-specific (XBRL), domain-related (SAPTerm, etc.) and domain-independent (GoogleDefine, Wikipedia, etc.) terminologies; and (3) add linguistic structure at term level (e.g. part-of-speech, morphology, syntactic arguments). This paper outlines a first experiment towards implementing this methodology on the International Financial Reporting Standard XBRL vocabulary.