QOM – Quick Ontology Mapping

Abstract

Abstract. (Semi-)automatic mapping -also called (semi-)automatic alignment -of ontologies is a core task to achieve interoperability when two agents or services use different ontologies. In the existing literature, the focus has so far been on improving the quality of mapping results. We here consider QOM, Quick Ontology Mapping, as a way to trade off between effectiveness (i.e. quality) and efficiency of the mapping generation algorithms. We show that QOM has lower run-time complexity than existing prominent approaches. Then, we show in experiments that this theoretical investigation translates into practical benefits. While QOM gives up some of the possibilities for producing high-quality results in favor of efficiency, our experiments show that this loss of quality is marginal.

A String Metric for Ontology Alignment

“…This technique is based on the fact that the same concepts are likely to be modelled using quite similar names. Many platforms that use such a methodology to compute similarities between ontologies exist in literature; some examples are, Anchor-PROMPT [3], QOM [4], Cupid [5] and many more.…”

Block Matching for Ontologies

“…Glue [6], Qom [7], Ola [8], S-Match [10], Hcone-merge [13], Prompt [14], V-Doc [20] and I-Sub [23] are such works. However, these algorithms cannot solve the block matching problem since they are targeted to find 1:1 mappings.…”

“…Despite many works (e.g., [6,7,8,10,11,13,14,20,23]) have addressed the ontology matching (also called ontology mapping or alignment) problem, there exist very few approaches raising the issue of block matching. Pbm [11] is the only work we know so far that considers the block matching problem.…”

PowerMap: Mapping the Real Semantic Web on the Fly

“…3) Time Performance is important -As already pointed out in [10], the majority of mapping approaches focus on the effectiveness (i.e., quality) of the mapping rather than on its efficiency (i.e., speed). This is a major challenge that needs to be solved in the context of run-time mappings where the speed of the response is a crucial factor.…”

“…The above mentioned paper also shows that some minor modifications of the mapping strategy can highly improve response time and have only a marginal negative effect on the quality of the mappings. Unfortunately the work presented in [10] is rather unique in the context of mapping research --although we think that such research is crucial for making mapping techniques usable during run-time. 4) Consider relation and instance mappings -much of the work in ontology mapping has focused on matching the concepts in two schemas, while other ontology entities, such as relations and instances, have largely been ignored so far (although relations and instances are taken into account as evidence to support the matching process in some approaches).…”