Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning

Ontology learning (OL) aims at the (semi-)automatic acquisition of ontologies from sources of evidence, typically domain text. Recently, there has been a trend towards the application of multiple and heterogeneous evidence sources in OL. Heterogeneous sources provide benefits, such as higher accuracy by exploiting redundancy across evidence sources, and including complementary information. When using evidence sources which are heterogeneous in quality, amount of data provided and type, then a number of questions arise, for example: How many sources are needed to see significant benefits from heterogeneity, what is an appropriate number of evidences per source, is balancing the number of evidences per source important, and to what degree can the integration of multiple sources overcome low quality input of individual sources? This research presents an extensive evaluation based on an existing OL system. It gives answers and insights on the research questions posed for the OL task of concept detection, and provides further hints from experience made. Among other things, our results suggest that a moderate number of evidences per source as well as a moderate number of sources resulting in a few thousand data instances are sufficient to exploit the benefits of heterogeneous evidence integration.