Mapping Text Mining Taxonomies

Katja Pfeifer, Eric Peukert

2013

Abstract

Huge amounts of textual information relevant for market analysis, trending or product monitoring can be found on the Web. To make use of that information a number of text mining services were proposed that extract and categorize entities from given text. Such services have individual strengths and weaknesses so that merging results from multiple services can improve quality. To merge results, mappings between service taxonomies are needed since different taxonomies are used for categorizing extracted information. The mappings can potentially be computed by using ontology matching systems. However, the available meta data within most taxonomies is weak so that ontology matching systems currently return insufficient results. In this paper we propose a novel approach to enrich service taxonomies with instance information which is crucial for finding mappings. Based on the found instances we present a novel instance-based matching technique and metric that allows us to automatically identify equal, hierarchical and associative mappings. These mappings can be used for merging results of multiple extraction services. We broadly evaluate our matching approach on real world service taxonomies and compare to state-of-the-art approaches.

Paper Citation

in Harvard Style

Pfeifer K. and Peukert E. (2013). Mapping Text Mining Taxonomies . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013) ISBN 978-989-8565-75-4, pages 5-16. DOI: 10.5220/0004500400050016

in Bibtex Style

@conference{kdir13,
author={Katja Pfeifer and Eric Peukert},
title={Mapping Text Mining Taxonomies},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013)},
year={2013},
pages={5-16},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004500400050016},
isbn={978-989-8565-75-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013)
TI - Mapping Text Mining Taxonomies
SN - 978-989-8565-75-4
AU - Pfeifer K.
AU - Peukert E.
PY - 2013
SP - 5
EP - 16
DO - 10.5220/0004500400050016