Relational Learning for Spatial Relation Extraction from Natural Language

  • Parisa Kordjamshidi
  • Paolo Frasconi
  • Martijn Van Otterlo
  • Marie-Francine Moens
  • Luc De Raedt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7207)


Automatically extracting spatial information is a challenging novel task with many applications. We formalize it as an information extraction step required for a mapping from natural language to a formal spatial representation. Sentences may give rise to multiple spatial relations between words representing landmarks, trajectors and spatial indicators. Our contribution is to formulate the extraction task as a relational learning problem, for which we employ the recently introduced kLog framework. We discuss representational and modeling aspects, kLog’s flexibility in our task and we present current experimental results.


Spatial Relation Parse Tree Candidate Selection Inductive Logic Programming Relational Learn 
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 2012

Authors and Affiliations

  • Parisa Kordjamshidi
    • 1
  • Paolo Frasconi
    • 2
  • Martijn Van Otterlo
    • 3
  • Marie-Francine Moens
    • 1
  • Luc De Raedt
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenBelgium
  2. 2.Università degli Studi di FirenzeItaly
  3. 3.Radboud University NijmegenThe Netherlands

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