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Documents authored by Alexopoulou, Dimitra

Document
DOI: 10.4230/DagSemProc.08131.6
GoPubMed: Exploring Pubmed with Ontological Background Knowledge

Authors: Heiko Dietze, Dimitra Alexopoulou, Michael R. Alvers, Bill Barrio-Alvers, Andreas Doms, Jörg Hakenberg, Jan Mönnich, Conrad Plake, Andreas Reischuck, Loic Royer, Thomas Wächter, Matthias Zschunke, and Michael Schroeder

Published in: Dagstuhl Seminar Proceedings, Volume 8131, Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives (2008)

Abstract
With the ever increasing size of scientific literature, finding relevant documents and answering questions has become even more of a challenge. Recently, ontologies - hierarchical, controlled vocabularies - have been introduced to annotate genomic data. They can also improve the question answering and the selection of relevant documents in the literature search. Search engines such as GoPubMed.org use ontological background knowledge to give an overview over large query results and to help answering questions. We review the problems and solutions underlying these next generation intelligent search engines and give examples of the power of this new search paradigm.
Cite as

Heiko Dietze, Dimitra Alexopoulou, Michael R. Alvers, Bill Barrio-Alvers, Andreas Doms, Jörg Hakenberg, Jan Mönnich, Conrad Plake, Andreas Reischuk, Loic Royer, Thomas Wächter, Matthias Zschunke, and Michael Schroeder. GoPubMed: Exploring Pubmed with Ontological Background Knowledge. In Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives. Dagstuhl Seminar Proceedings, Volume 8131, p. 1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{dietze_et_al:DagSemProc.08131.6,
  author =	{Dietze, Heiko and Alexopoulou, Dimitra and Alvers, Michael R. and Barrio-Alvers, Bill and Doms, Andreas and Hakenberg, J\"{o}rg and M\"{o}nnich, Jan and Plake, Conrad and Reischuck, Andreas and Royer, Loic and W\"{a}chter, Thomas and Zschunke, Matthias and Schroeder, Michael},
  title =	{{GoPubMed: Exploring Pubmed with Ontological Background Knowledge}},
  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 =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08131.6},
  URN =		{urn:nbn:de:0030-drops-15204},
  doi =		{10.4230/DagSemProc.08131.6},
  annote =	{Keywords: Text mining, literature search, Gene Ontology, NLP, ontology, thesaurus, PubMed}
}
Document
DOI: 10.4230/DagSemProc.08131.12
Ontology learning with text mining: Two use cases in lipoprotein metabolism and toxicology

Authors: Dimitra Alexopoulou, Thomas Wächter, Laura Pickersgill, Cecilia Eyre, and Michael Schroeder

Published in: Dagstuhl Seminar Proceedings, Volume 8131, Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives (2008)

Abstract
Background: 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. Results: 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. Conclusions: 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. Availability: The automatic term recognition method is available as web service, described at http://gopubmed4.biotec.tu- dresden.de/IdavollWebService/services/CandidateTermGeneratorService?wsdl
Cite as

Dimitra Alexopoulou, Thomas Wächter, Laura Pickersgill, Cecilia Eyre, and Michael Schroeder. Ontology learning with text mining: Two use cases in lipoprotein metabolism and toxicology. In Ontologies and Text Mining for Life Sciences : Current Status and Future Perspectives. Dagstuhl Seminar Proceedings, Volume 8131, p. 1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{alexopoulou_et_al:DagSemProc.08131.12,
  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 =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08131.12},
  URN =		{urn:nbn:de:0030-drops-15063},
  doi =		{10.4230/DagSemProc.08131.12},
  annote =	{Keywords: Automatic Term Recognition, Ontology Learning, Lipoprotein Metabolism}
}
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