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Ontology-based Information Extraction for Business Intelligence

by: Horacio Saggion and Kalina Bontcheva and Adam Funk and Diana Maynard

In: Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference ISWC/ASWC2007, Busan, South Korea , Vol. 4825Berlin, Heidelberg: Springer Verlag , November (2007) , p. 837--850.

Resources (URL, PDF, PS...)

URL:	http://iswc2007.semanticweb.org/papers/837.pdf

Abstract

Business Intelligence BI requires the acquisition and aggregation of key pieces of knowledge from multiple sources in order to provide valuable information to customers or feed statistical BI models and tools. The massive amount of information available to business analysts makes information extraction and other natural language processing tools key enablers for the acquisition and use of that semantic information. We describe the application of ontology-based extraction and merging in the context of a practical e-business application for the EU MUSING Project where the goal is to gather international company intelligence and country/region information. The results of our experiments so far are very promising and we are now in the process of bulding a complete end-to-end solution.

BibTeX record

@inproceedings{Saggion/2007/Ontology-based,
  address = {Berlin, Heidelberg},
  author = {Horacio Saggion and Kalina Bontcheva and Adam Funk and Diana Maynard},
  booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
  crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
  editor = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
  interHash = {a7d7a1cfa4bcacea805966f0667944a4},
  intraHash = {837c26926a5e2c025e16c8e5c36af752},
  pages = {837--850},
  publisher = {Springer Verlag},
  series = {LNCS},
  title = {Ontology-based Information Extraction for Business Intelligence},
  url = {http://iswc2007.semanticweb.org/papers/837.pdf},
  volume = {4825},
  year = {2007},
  month = {November},
  abstract = {Business Intelligence (BI) requires the acquisition and aggregation of key pieces of knowledge from multiple sources in order to provide valuable information to customers or feed statistical BI models and tools. The massive amount of information available to business analysts makes information extraction and other natural language processing tools key enablers  for the acquisition and use of that semantic information. We describe the application of ontology-based extraction and merging in the context of a practical e-business application for the EU MUSING Project where the goal is to gather international company intelligence and country/region information. The results of our experiments so far are very promising and we are now in the process of bulding a complete end-to-end solution.}
}

Endnote record

%0 Conference Paper
%1 Saggion/2007/Ontology-based
%A Horacio Saggion
%A Kalina Bontcheva
%A Adam Funk
%A Diana Maynard
%B Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea
%C Berlin, Heidelberg
%D 2007
%E Karl Aberer
%E Key-Sun Choi
%E Natasha Noy
%E Dean Allemang
%E Kyung-Il Lee
%E Lyndon J B Nixon
%E Jennifer Golbeck
%E Peter Mika
%E Diana Maynard
%E Guus Schreiber
%E Philippe Cudré-Mauroux
%I Springer Verlag
%K 
%P 837--850
%T Ontology-based Information Extraction for Business Intelligence
%U http://iswc2007.semanticweb.org/papers/837.pdf
%V 4825
%X Business Intelligence (BI) requires the acquisition and aggregation of key pieces of knowledge from multiple sources in order to provide valuable information to customers or feed statistical BI models and tools. The massive amount of information available to business analysts makes information extraction and other natural language processing tools key enablers  for the acquisition and use of that semantic information. We describe the application of ontology-based extraction and merging in the context of a practical e-business application for the EU MUSING Project where the goal is to gather international company intelligence and country/region information. The results of our experiments so far are very promising and we are now in the process of bulding a complete end-to-end solution.