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Automatic Acquisition of Adjective Lexicalizations of Restriction Classes: a Machine Learning Approach

  • Original Article
  • Published:
Journal on Data Semantics

Abstract

There is an increasing interest in providing common Web users with access to structured knowledge bases such as DBpedia, for example by means of question answering systems. An essential task of such systems is transforming natural language questions into formal queries, e.g. expressed in SPARQL. To this end, such systems require knowledge about how the vocabulary elements used in the available ontologies and datasets are verbalized in natural language, covering different verbalization variants, possibly in multiple languages. An important part of such lexical knowledge is constituted by adjectives. In this paper, we present and evaluate a machine learning approach to extract adjective lexicalizations from DBpedia. This is a challenge that has so far not been addressed. Our approach achieves an accuracy of \(91.15 \%\) on a tenfold cross validation regime. In addition to providing a first baseline system for the task of extracting adjective lexicalizations from DBpedia, we publish the extracted adjective lexicalizations in lemon format for free use by the community.

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Notes

  1. http://www.sc.cit-ec.uni-bielefeld.de/qald/.

  2. Which can be downloaded at http://sebastianwalter.org/downloads/dblexipedia/jods_arff_files.tar.gz as .arff files.

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Acknowledgments

This work was supported by the Cluster of Excellence Cognitive Interaction Technology CITEC (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).

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Authors and Affiliations

  1. Semantic Computing Group, CITEC, Bielefeld University, Bielefeld, Germany

    Sebastian Walter, Christina Unger & Philipp Cimiano

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  1. Sebastian Walter
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  2. Christina Unger
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  3. Philipp Cimiano
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Correspondence to Sebastian Walter.

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Walter, S., Unger, C. & Cimiano, P. Automatic Acquisition of Adjective Lexicalizations of Restriction Classes: a Machine Learning Approach. J Data Semant 6, 113–123 (2017). https://doi.org/10.1007/s13740-016-0069-0

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  • DOI: https://doi.org/10.1007/s13740-016-0069-0

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