License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
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DOI: 10.4230/DagSemProc.08131.12
URN: urn:nbn:de:0030-drops-15063
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Alexopoulou, Dimitra ; W├Ąchter, Thomas ; Pickersgill, Laura ; Eyre, Cecilia ; Schroeder, Michael

Ontology learning with text mining: Two use cases in lipoprotein metabolism and toxicology

08131.WaechterThomas.ExtAbstract.1506.pdf (0.04 MB)


The engineering of ontologies, especially with a view to a text-mining use, is still a
new research field. There does not yet exist a well-defined theory and technology for
ontology construction. Many of the ontology design steps remain manual and are
based on personal experience and intuition. However, there exist a few efforts on
automatic construction of ontologies in the form of extracted lists of terms and
relations between them.

We share experience acquired during the manual development of a lipoprotein
metabolism ontology (LMO) to be used for text-mining. We compare the manually
created ontology terms with the automatically derived terminology from four different
automatic term recognition methods. The top 50 predicted terms contain up to
89% relevant terms. For the top 1000 terms the best method still generates 51%
relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and
38% can be generated with one of the methods.
Secondly we present a use case for ontology-based search for toxicological methods.

Given high precision, automatic methods can help decrease development time and
provide significant support for the identification of domain-specific vocabulary. The
coverage of the domain vocabulary depends strongly on the underlying documents.
Ontology development for text mining should be performed in a semi-automatic way;
taking automatic term recognition results as input.

The automatic term recognition method is available as web service, described at

BibTeX - Entry

  author =	{Alexopoulou, Dimitra and W\"{a}chter, Thomas and Pickersgill, Laura and Eyre, Cecilia and Schroeder, Michael},
  title =	{{Ontology learning with text mining: Two use cases in lipoprotein metabolism and toxicology}},
  booktitle =	{Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives},
  pages =	{1--1},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8131},
  editor =	{Michael Ashburner and Ulf Leser and Dietrich Rebholz-Schuhmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-15063},
  doi =		{10.4230/DagSemProc.08131.12},
  annote =	{Keywords: Automatic Term Recognition, Ontology Learning, Lipoprotein Metabolism}

Keywords: Automatic Term Recognition, Ontology Learning, Lipoprotein Metabolism
Collection: 08131 - Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives
Issue Date: 2008
Date of publication: 03.06.2008

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