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Documents authored by Hakenberg, Jörg

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.08131.15
Services for annotation of biomedical text

Authors: Jörg Hakenberg

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

Abstract
Motivation: Text mining in the biomedical domain in recent years has focused on the development of tools for recognizing named entities and extracting relations. Such research resulted from the need for such tools as basic components for more advanced solutions. Named entity recognition, entity mention normalization, and relationship extraction now have reached a stage where they perform comparably to human annotators (considering inter--annotator agreement, measured in many studies to be around 90\%). Many tools have been made available, through web--interfaces or as downloadable software using non--standardized formats for in-- and output. To advance progress in text mining, solutions are needed to both provide and combine the results of 'basic' information retrieval and extraction tools. Results: Our groups at Technical University Dresden, Humboldt--Universit"{a}t zu Berlin, and Arizona State University developed systems for named entity recognition, normalization, and relationship extraction. As evaluated during and after the BioCreative 2 challenge, recognition of proteins achieves 86\% f--measure, normalization of gene mentions 85\%, and extraction of protein--protein interactions including mapping to UniProt 25\%. Conclusions: We consider the BioCreative meta-service an ideal framework to make available information extraction tools to a variety of users: researchers from the biomedical domain, database curators, and researchers in text mining who can use the services as input for subsequent analyses. At the time of writing this abstract, twelve groups provide their tools as services to the BCMS server. We currently participates with tools for recognizing names of genes/proteins and species, normalization of gene mentions to EntrezGene, protein mentions to UniProt, mentions of species to NCBI Taxonomy, as well as classifying abstracts for protein-protein interactions. Availability: For more information, please refer to http://alibaba.informatik.hu-berlin.de/bcms/. BCMS is available at http://bcms.bioinfo.cnio.es/.
Cite as

Jörg Hakenberg. Services for annotation of biomedical text. 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{hakenberg:DagSemProc.08131.15,
  author =	{Hakenberg, J\"{o}rg},
  title =	{{Services for annotation of biomedical text}},
  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.08131.15},
  URN =		{urn:nbn:de:0030-drops-15101},
  doi =		{10.4230/DagSemProc.08131.15},
  annote =	{Keywords: BioCreative, NER, EMN, GN, information extraction, web-service, AliBaba}
}
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