JHU/APL onto-mapology results for OAEI 2006
Wayne L. Bethea, Clay Fink, John S. Beecher-Deighan · 2006
Abstract. Numerous techniques for ontology alignment and mapping have appeared in the literature, but there has been little discussion on the use of formal semantics for the task. Typical solutions apply multiple techniques to produce their results. We demonstrate that a hybrid solution that brings together a number of matching techniques yields the best results. An essential component of any ontology mapping solution is the ability for users to interact with the system and manipulate intermediate and final results. We introduce Onto-Mapology; an approach to ontology mapping that integrates techniques based on string/text matching, structure/graph matching, and semantic (rulebased/logic-based) matching. After the initial design, development, testing and evaluation we applied Onto-Mapology to the OAEI 2006 test cases with encouraging results. 1 Onto-Mapology: The Mapping Process Ontology mapping techniques have been discussed in the literature that describe string and text matching techniques [1], schema matching techniques [2], categorical information mapping techniques [3], and machine learning techniques [4], but very little has been discussed that describes formal semantic matching techniques. Onto-Mapology is the Johns Hopkins University Applied Physics Lab (JHU/APL) ontology mapping software solution that was designed and developed with strong consideration for human participation in the mapping process. It integrates techniques based on string/text matching, structure/graph matching, and semantic (rule-based/logic-based) matching. It allows users to apply different combinations of these techniques, or a hybrid algorithm that produces solid results in our testing. This paper discusses Onto-Mapology, our approach to the ontology mapping process, and our results for OAEI