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

Sandip Debnath, Arun Upneja, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd International Conference on Knowledge Capture, K-CAP'05
Pages191-192
Number of pages2
DOIs
StatePublished - Dec 1 2005
Event3rd International Conference on Knowledge Capture, K-CAP'05 - Banff, AB, Canada
Duration: Oct 2 2005Oct 5 2005

Publication series

NameProceedings of the 3rd International Conference on Knowledge Capture, K-CAP'05

Other

Other3rd International Conference on Knowledge Capture, K-CAP'05
CountryCanada
CityBanff, AB
Period10/2/0510/5/05

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Networks and Communications

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    Debnath, S., Upneja, A., & Giles, C. L. (2005). Learning term-relationships for ontology construction: Creating business ontologies for event explanation. In Proceedings of the 3rd International Conference on Knowledge Capture, K-CAP'05 (pp. 191-192). (Proceedings of the 3rd International Conference on Knowledge Capture, K-CAP'05). https://doi.org/10.1145/1088622.1088663