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Ontology-based Information Extraction for Business Intelligence

by: Horacio Saggion, Kalina Bontcheva, 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. 4825 Berlin, Heidelberg: Springer Verlag (November 2007) , p. 837--850.

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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,
  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.},
  added-at = {2007-11-07T19:13:58.000+0100},
  address = {Berlin, Heidelberg},
  author = {Saggion, Horacio and Bontcheva, Kalina and Funk, Adam and Maynard, Diana},
  biburl = {http://www.bibsonomy.org/bibtex/2837c26926a5e2c025e16c8e5c36af752/iswc2007},
  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 = {Aberer, Karl and Choi, Key-Sun and Noy, Natasha and Allemang, Dean and Lee, Kyung-Il and Nixon, Lyndon J B and Golbeck, Jennifer and Mika, Peter and Maynard, Diana and Schreiber, Guus and Cudré-Mauroux, Philippe},
  interhash = {a7d7a1cfa4bcacea805966f0667944a4},
  intrahash = {837c26926a5e2c025e16c8e5c36af752},
  keywords = {2007 application_software business extraction finance in_use_3 information intelligence iswc ontology ontology_(computer_science) semantic_web tool},
  month = {November},
  pages = {837--850},
  publisher = {Springer Verlag},
  series = {LNCS},
  timestamp = {2007-11-07T19:13:58.000+0100},
  title = {Ontology-based Information Extraction for Business Intelligence},
  url = {http://iswc2007.semanticweb.org/papers/837.pdf},
  volume = 4825,
  year = 2007
}

Endnote record

%0 Conference Paper
%1 Saggion/2007/Ontology-based
%A Saggion, Horacio
%A Bontcheva, Kalina
%A Funk, Adam
%A Maynard, Diana
%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 Aberer, Karl
%E Choi, Key-Sun
%E Noy, Natasha
%E Allemang, Dean
%E Lee, Kyung-Il
%E Nixon, Lyndon J B
%E Golbeck, Jennifer
%E Mika, Peter
%E Maynard, Diana
%E Schreiber, Guus
%E Cudré-Mauroux, Philippe
%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.