Learning term-relationships for ontology construction: creating business ontologies for event explanation

Authors:
Sandip Debnath;Arun Upneja;C. Lee Giles
Affiliations:
Penn State University, University Park, PA;Penn State University, University Park, PA;Penn State University, University Park, PA
Venue:
Proceedings of the 3rd international conference on Knowledge capture
Year:
2005

Citing 2
Cited 0

A learning based model for headline extraction of news articles to find explanatory sentences for events

Proceedings of the 3rd international conference on Knowledge capture
Knowledge discovery in web-directories: finding term-relations to build a business ontology

EC-Web'05 Proceedings of the 6th international conference on E-Commerce and Web Technologies

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Abstract

Term-Relationship plays major role in several areas of research including document relevance, domain ontology construction or metadata extraction. As part of our work in finding explanation for market events (such as sudden or significant stock price change for a company), we use business ontologies to facilitate the relevance ranking of documents. We use the ontologies even further to rank individual sentences in a news article so that irrelevant events (in the form of sentences) in a relevant article will not get undue importance. This two-step ranking helps us in extracting important sentences which can be labelled as "responsible" or "explanatory" sentences for a significant stock price change. In this paper we show the performance evaluation of our relevance model using ontologies. We also show a few examples of sentences which can be thought of as providing explanation for some recent price changes.