Learning Recursive Patterns for Biomedical Information Extraction

  • Margherita Berardi
  • Donato Malerba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4455)


Information in text form remains a greatly unexploited source of biological information. Information Extraction (IE) techniques are necessary to map this information into structured representations that allow facts relating domain-relevant entities to be automatically recognized. In biomedical IE tasks, extracting patterns that model implicit relations among entities is particularly important since biological systems intrinsically involve interactions among several entities. In this paper, we resort to an Inductive Logic Programming (ILP) approach for the discovery of mutual recursive patterns from text. Mutual recursion allows dependencies among entities to be explored in data and extraction models to be applied in a context-sensitive mode. In particular, IE models are discovered in form of classification rules encoding the conditions to fill a pre-defined information template. An application to a real-world dataset composed by publications selected to support biologists in the task of automatic annotation of a genomic database is reported.


Information Extraction Commission Error Omission Error Target Sentence Inductive Logic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Margherita Berardi
    • 1
  • Donato Malerba
    • 1
  1. 1.Dipartimento di Informatica, Università degli Studi di Bari, via Orabona, 4 - 70126 Bari - Italy 

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