Applying Vector Space Models to Ontology Link Type Suggestion

The identification and labeling of non-hierarchical relations are among the most challenging tasks in ontology learning. This paper describes an approach for suggesting ontology relationship types to domain experts based on implicitly learned relations from a domain corpus. The learning process extracts verb- vectors from sentences containing domain concepts. It computes centroids for known relationship types and stores them in the knowledge base. Vectors of unknown relationships are compared to the stored centroids using the cosine similarity metric. The system then suggests the relationship type of the most similar centroid. Domain experts evaluate these suggestions to refine the knowledge base and constantly improve the component's accuracy. Using four sample ontologies on "energy sources", this paper demonstrates how link type suggestion aids the ontology design process. It also provides a statistical analysis on the accuracy and average ranking performance of batch learning versus online learning.