Refining Non-Taxonomic Relation Labels with External Structured Data to Support Ontology Learning

Abstract

This paper presents a method to integrate external knowledge sources such as DBpedia and OpenCyc into an ontology learning system that automatically suggests labels for unknown relations in domain ontologies based on large corpora of unstructured text. The method extracts and aggregates verb vectors from semantic relations identified in the corpus. It composes a knowledge base which consists of (i) verb centroids for known relations between domain concepts, (ii) mappings between concept pairs and the types of known relations, and (iii) ontological knowledge retrieved from external sources. Applying semantic inference and validation to this knowledge base yields a refined relation label suggestion. A formal evaluation compares the accuracy and average ranking precision of this hybrid method with the performance of methods that solely rely on corpus data and those that are only based on reasoning and external data sources.