Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)


Three common approaches for deriving or predicting instantiated relations are information extraction, deductive reasoning and machine learning. Information extraction uses subsymbolic unstructured sensory information, e.g. in form of texts or images, and extracts statements using various methods ranging from simple classifiers to the most sophisticated NLP approaches. Deductive reasoning is based on a symbolic representation and derives new statements from logical axioms. Finally, machine learning can both support information extraction by deriving symbolic representations from sensory data, e.g., via classification, and can support deductive reasoning by exploiting regularities in structured data. In this paper we combine all three methods to exploit the available information in a modular way, by which we mean that each approach, i.e., information extraction, deductive reasoning, machine learning, can be optimized independently to be combined in an overall system. We validate our model using data from the YAGO2 ontology, and from Linked Life Data and Bio2RDF, all of which are part of the Linked Open Data (LOD) cloud.


Sensory Information Information Extraction Link Prediction Deductive Reasoning Link Open Data 
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

  1. 1.Siemens AG, Corporate TechnologyMunichGermany
  2. 2.Ludwig Maximilian University of MunichMunichGermany

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