Result of ontology alignment with RiMOM at OAEI'06
Yi Li, Juanzi Li, Duo Zhang, Jie Tang · 2006
Abstract. In this report, we give a brief explanation of how RiMOM obtains the ontology alignment results at OAEI’07 contest. RiMOM integrates different alignment strategies: edit-distance based strategy, vector-similarity based strategy, path-similarity based strategy, background-knowledge based strategy, and three similarity-propagation based strategies. Each strategy is defined based on one specific ontological-information. In this contest, we, in particular, study how the different strategies (or strategy combination) perform for different alignment tasks. We found that: 1) on the directory data set, the path-similarity based strategy seems to outperform the others and 2) on the anatomy and food data sets, the background-knowledge based strategy has several distinct advantages. This report presents our results based on the evaluation. We also share our thoughts on the experiment design, showing specific strengths and weaknesses of our approach. 1. PRESENTATION OF THE SYSTEM Ontology alignment is the key point to reach interoperability over ontologies. In