Integrating Structural Data into Methods for Labeling Relations in Domain Ontologies

Abstract

This paper presents a method for integrating DBpedia data into an ontology learning system that automatically suggests labels for relations in domain ontologies based on large corpora of unstructured text. The method extracts and aggregates verb vectors for semantic relations identified in the corpus. It composes a knowledge base which consists of (i) centroids for known relations between domain concepts, (ii) mappings between concept pairs and the types of known relations, and (iii) ontological knowledge retrieved from DBpedia. Refining similarities between the verb centroids of labeled and unlabeled relations by means of including domain and range constraints applying DBpedia data yields relation type suggestions. A formal evaluation compares the accuracy and average ranking performance 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.