NDLI: Integrating ontological and linguistic knowledge for conceptual information extraction

Content Provider	IEEE Xplore Digital Library
Author	Basili, R. Vindigni, M. Zanzotto, F.M.
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci., Rome Univ., Italy (Basili, R.; Vindigni, M.; Zanzotto, F.M.)
Abstract	Text understanding makes strong assumptions about the conceptualisation of the underlying knowledge domain. This mediates between the accomplishment of the specific task at the one hand and the knowledge expressed in the target text fragments at the other. However, building domain conceptualisations from scratch is a very complex and time-consuming task. Traditionally, the reuse of available domain resources, although not constituting always the best, has been applied as an accurate and cost effective solution. Here, we investigate the possibility of exploiting sources of domain knowledge (e.g. a subject reference system) to build a linguistically motivated domain concept hierarchy. The limitation connected with the use of domain taxonomies as ontological resources will be firstly discussed in the specific light of IE, i.e. for supporting linguistic inference. We then define a method for integrating the taxonomical domain knowledge and a general-purpose lexical knowledge base, like WordNet. A case study, i.e. the integration of the MeSH, Medical Subject Headings, and WordNet, will be then presented as a proof of the effectiveness and accuracy of the overall approach.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Computational Intelligence (TCCI) Web Intelligence Consortium (WIC)
Starting Page	175
Ending Page	181
File Size	278074
Page Count	7
File Format	PDF
ISBN	0769519326
DOI	10.1109/WI.2003.1241190
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Costs Databases Taxonomy Investments Text categorization Ontologies Data mining Intelligent structures
Content Type	Text
Resource Type	Article

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