TY - CPAPER SN - 978-1-4244-1840-4 AU - Weichselbraun, Albert AU - Granitzer, Michael AU - Wohlgenannt, Gerhard AU - Neidhart, Thomas AU - Scharl, Arno AU - Juffinger, Andreas T1 - Applying Vector Space Models to Ontology Link Type Suggestion T2 - 4th International Conference on Innovations in Information Technology PY - 2007 CY - Piscataway, NJ PB - Institute of Electrical and Electronic Engineers (IEEE) UR - doi.org/10.1109/IIT.2007.4430433 SP - 566 EP - 570 AB - 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. TS - CrossRef Y3 - 22.01.2021 C7 - IIT '07 C1 - Dubai, 18. - 20. November M4 - Citavi ER -