Coupling Information Extraction and Data Mining for Ontology Learning in PARMENIDES

Strategic decision making, especially in the areas of business intelligence and competitive intelligence, requires the acquisition of decision-relevant information pieces like market trends, fusions and company values. This information is extracted by pre-processing and querying multiple sources, combining and condensing the findings. It is characteristic that the extraction process is resource intensive and has to be performed regularly and quite frequently. In the research project PARMENIDES, we are developing methods that establish ontologies over an application domain, annotate documents with the ontology components and identify the entities in them, so that we can decompose business into conventional queries towards entities and XML-annotated texts.

[1]  Hans-Ulrich Krieger,et al.  Integrating Shallow and Deep NLP for Information Extraction , 2003 .

[2]  Fabio Rinaldi,et al.  Breaking the Deadlock , 2003, OTM.

[3]  Fabio Rinaldi,et al.  Integrated text categorisation and Information extraction using pattern matching and linguistic processing , 1997, RIAO.

[4]  Ajith Abraham,et al.  Innovations in Knowledge Engineering. International Series on Advanced Intelligence , 2003 .

[5]  Jean-Luc Minel,et al.  Contextual Rules for Text Analysis , 2001, CICLing.

[6]  Myra Spiliopoulou,et al.  The DIAsDEM framework for converting domain-specific texts into XML documents with data mining techniques , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[7]  Myra Spiliopoulou,et al.  Structuring Domain-Specific Text Archives by Deriving a Probabilistic XML DTD , 2002, PKDD.

[8]  James A. Hendler,et al.  The semantic Web and its languages , 2000 .

[9]  Gian Piero Zarri,et al.  Representation of temporal knowledge in events: The formalism, and its potential for legal narratives , 1998 .

[10]  C. M. Sperberg-McQueen The Text Encoding Initiative , 1994 .

[11]  Philippe Martin Knowledge Representation, Sharing, and Retrieval on the Web , 2003 .

[12]  Ian Horrocks,et al.  OIL in a Nutshell , 2000, EKAW.

[13]  Fabio Rinaldi,et al.  Multilayer annotations in Parmenides , 2003 .

[14]  Linda Cantara,et al.  The text-encoding initiative: Part 1 , 2005, OCLC Syst. Serv..

[15]  Nicholas Kushmerick,et al.  Wrapper Induction for Information Extraction , 1997, IJCAI.

[16]  John F. Sowa,et al.  Knowledge representation: logical, philosophical, and computational foundations , 2000 .

[17]  Fabio Rinaldi,et al.  Parmenides: An Opportunity for ISO TC37 SC4? , 2003, ACL.

[18]  Richard A. Posner Breaking the Deadlock , 2001 .

[19]  Deborah L. McGuinness,et al.  Owl web ontology language guide , 2003 .

[20]  Francesco M. Donini,et al.  Decidable Reasoning in Terminological Knowledge Representation Systems , 1993, IJCAI.

[21]  Sophia Ananiadou,et al.  The C-value/NC-value domain-independent method for multi-word term extraction , 1999 .

[22]  Yorick Wilks,et al.  How feasible is the reuse of grammars for Named Entity Recognition? , 2002, LREC.

[23]  Adam Pease,et al.  The Suggested Upper Merged Ontology: A Large Ontology for the Semantic Web and its Applic ations , 2002 .

[24]  Stuart C. Shapiro Review of Knowledge representation: logical, philosophical, and computational foundations by John F. Sowa. Brooks/Cole 2000. , 2001 .

[25]  Nicola Guarino,et al.  WonderWeb Deliverable D17. The WonderWeb Library of Foundational Ontologies and the DOLCE ontology , 2002 .

[26]  Diana Maynard,et al.  JAPE: a Java Annotation Patterns Engine , 2000 .

[27]  Ajith Abraham,et al.  Innovations in Knowledge Engineering , 2003 .

[28]  Acknowledgments , 2006, Molecular and Cellular Endocrinology.