Pfeifer, K. ; Peukert, E.

Mapping Text Mining Taxonomies

KDIR 2013

2013 / 09



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.