A Methodology for Terminology-based Knowledge Acquisition and Integration

A Methodology for Terminology-based Knowledge Acquisition and Integration Hideki Mima1†, Sophia Ananiadou2, Goran Nenadic2 and Junichi Tsujii1 1Dept. of Information Science, University of Tokyo Hongo 7-3-1, Bunkyo-ku, Tokyo, Japan {mima, tsujii}@is.s.u-tokyo.ac.jp 2Computer Science, University of Salford Newton Building, Salford M5 4WT, UK {S.Ananiadou, G.Nenadic}@salford.ac.uk † Current affiliation: Dept. of Engineering, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113- 8656, Japan Abstract In this paper we propose an integrated knowledge management system in which terminology-based knowledge acquisition, knowledge integration, and XML-based knowledge retrieval are combined using tag information and ontology management tools. The main objective of the system is to facilitate knowledge acquisition through query answering against XML-based documents in the domain of molecular biology. Our system integrates automatic term recognition, term variation management, context-based automatic term clustering, ontology-based inference, and intelligent tag information retrieval. Tag-based retrieval is implemented through interval operations, which prove to be a powerful means for textual mining and knowledge acquisition. The aim is to provide efficient access to heterogeneous biological textual data and databases, enabling users to integrate a wide range of textual and non-textual resources effortlessly. Introduction With the recent increasing importance of electronic communication and data sharing over the Internet, there exist an increasingly growing number of publicly accessible knowledge sources, both in the form of documents and factual databases. These knowledge sources (KSs) are intrinsically heterogeneous and dynamic. They are heterogeneous since they are autonomously developed and maintained by independent organizations for different purposes. They are dynamic since constantly new information is being revised, added and removed. Such an heterogeneous and dynamic nature of KSs imposes challenges on systems that help users to locate and integrate knowledge relevant to their needs. Knowledge, encoded in textual documents, is organised around sets of specialised (technical) terms (e.g. names of proteins, genes, acids). Therefore, knowledge acquisition relies heavily on the recognition of terms. However, the main problems that make term recognition difficult are the lack of clear naming conventions and terminology variation (cf. Jacquemin and Tzoukermann (1999)), especially in the domain of molecular biology. Therefore, we need a scheme to integrate terminology management as a key prerequisite for knowledge acquisition and integration. However, automatic term extraction is not the ultimate goal itself, since the large number of new terms calls for a systematic way to access and retrieve the knowledge represented through them. Therefore, the extracted terms need to be placed in an appropriate framework by discovering relations between them, and by establishing the links between the terms and different factual databases. In order to solve the problem, several approaches have been proposed. MeSH Term in MEDLINE (2002) and Gene Ontology (2002) provide a top-down controlled ontology framework, which aims to describe and constrain the terminology in the domain of molecular biology. On the other hand, automatic term acquisition approaches have been developed in order to address a dynamic and corpus-driven knowledge acquisition methodology (Mima et al., 1999; 2001a). Different approaches to linking relevant resources have also been suggested. The Semantic Web framework (Berners-Lee (1998)) aims …

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