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.

Basili, R., Vindigni, M., & Zanzotto, F.m. (2003). Integrating ontological and linguistic knowledge for Conceptual Information Extraction. In Proceedings of IEEE/WIC Web Intelligence (WI 2003) (GSS Conference Rating CORE:B, LiveSHINE:A, MA:B) [10.1109/WI.2003.1241190].

Integrating ontological and linguistic knowledge for Conceptual Information Extraction

BASILI, ROBERTO;VINDIGNI, MICHELE;ZANZOTTO, FABIO MASSIMO
2003

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.
IEEE/WIC International Conference on Web Intelligence, WI 2003
Rilevanza internazionale
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
English
Computer science; Costs; Data mining; Databases; Intelligent structures; Investments; Ontologies; Taxonomy; Text categorization
http://dx.medra.org/10.1109/WI.2003.1241190
Intervento a convegno
Basili, R., Vindigni, M., & Zanzotto, F.m. (2003). Integrating ontological and linguistic knowledge for Conceptual Information Extraction. In Proceedings of IEEE/WIC Web Intelligence (WI 2003) (GSS Conference Rating CORE:B, LiveSHINE:A, MA:B) [10.1109/WI.2003.1241190].
Basili, R; Vindigni, M; Zanzotto, Fm
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/165443
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