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Abstract

Text mining services can be used to extract and categorize entities from textual information on the web. Merging results from multiple services could improve extraction quality. This requires to have an integrated extraction taxonomy and corresponding mappings between individual taxonomies that are used for categorizing extracted information. However, current ontology matching approaches cannot be applied since the available meta data within most taxonomies is weak.

In this article we propose a novel taxonomy alignment process that allows us to automatically identify equal, hierarchical and associative mappings and integrate those mappings in a global taxonomy. We broadly evaluate our matching approach on real world service taxonomies and compare to state-of-the-art approaches.

Keywords

Instance-based matching Text mining Taxonomy alignment 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.SAP AGDresdenGermany

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