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Extended Semantic Web Conference

ESWC 2012: The Semantic Web: Research and Applications pp 164–178Cite as

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

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

  • Xueyan Jiang22,
  • Yi Huang21,22,
  • Maximilian Nickel22 &
  • …
  • Volker Tresp21,22 
  • Conference paper
  • 2939 Accesses

  • 4 Citations

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7295)

Abstract

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.

Keywords

  • 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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Author information

Authors and Affiliations

  1. Siemens AG, Corporate Technology, Munich, Germany

    Yi Huang & Volker Tresp

  2. Ludwig Maximilian University of Munich, Munich, Germany

    Xueyan Jiang, Yi Huang, Maximilian Nickel & Volker Tresp

Authors
  1. Xueyan Jiang
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  2. Yi Huang
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  3. Maximilian Nickel
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  4. Volker Tresp
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Editor information

Editors and Affiliations

  1. Institute AIFB, Karlsruhe Institute of Technology, Englerstrasse 11, 76131, Karlsruhe, Germany

    Elena Simperl

  2. CITEC, University of Bielefeld, Morgenbreede 39, 33615, Bielefeld, Germany

    Philipp Cimiano

  3. Siemens AG Österreich, Siemensstrasse 90, 1210, Vienna, Austria

    Axel Polleres

  4. Technical University of Madrid, C/ Severo Ochoa, 13, 28660, Boadilla del Monte, Madrid, Spain

    Oscar Corcho

  5. STLab, ISTC-CNR, Via Nomentana 56, 00161, Rome, Italy

    Valentina Presutti

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© 2012 Springer-Verlag Berlin Heidelberg

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Jiang, X., Huang, Y., Nickel, M., Tresp, V. (2012). Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds) The Semantic Web: Research and Applications. ESWC 2012. Lecture Notes in Computer Science, vol 7295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30284-8_18

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  • DOI: https://doi.org/10.1007/978-3-642-30284-8_18

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  • Print ISBN: 978-3-642-30283-1

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