Automatic Construction of Frame Ontology with Varying Granularities

for Effective Information Extraction and Information Retrieval

: A Case Study for Biomedical Applications

Summary:

The IR and IE techniques have acquired the level of maturity for a widespread use in our everyday lives, but they are still short of fully satisfying the user and thus in need of much further improvement, the problem of utilizing granularity being certainly one of them.

In order to search for the information of the right granularity in response to the requests of the user, we should be able to assess the granularity of individual pieces of information, which can in turn be dealt with by leveraging the granularity of the predicate and its argument(s) of each sentence. In this project, we developed methods for automatically constructing and managing various ontologies and for utilizing them to provide granularity options to existing IR and IE techniques. The ontologies are used to specify the granularity of predicates and arguments at different levels, so that it becomes possible to compare the granularity of different pieces of information, and to zoom in to those that are of the target granularity.

We used the domain of biology and medicine for our case study, since this is an area where many natural language processing techniques are currently in much use for effective information retrieval and extraction due to the explosively growing amount of information and where our research group has been working on customized natural language processing services with a number of fruitful results.

Publications:

l Seung-Cheol Baek and Jong C. Park, “Automatic Extraction of the Usage Information from the Component Words in Gene Ontology Terms to Enhance Consistency and Predictability”, The 3rd International Symposium on Language in Biology and Medicine (LBM), Korea, November 8-10, 2009.

l Hee-Jin Lee and Jong C. Park, “Towards Knowledge Discovery through Automatic Inference with Text Mining in Biology and Medicine”, The 3rd International Symposium on Semantic Mining in Biomedicine, Turku, Finland, September 1-3, 2008.

l Jin-Bok Lee, Tak-eun Kim and Jong C. Park, "An effective way to learn biological knowledge with linguistic resources," The 18th International Congress of Linguists (CIL 18), Seoul, Korea, July 21-26, 2008.