Ontology Mapping And Evaluation In Semantic Networks

The world of semantic web is progressing slowly but constantly. Ontologies, being the backbone of the semantic web, needs to be focused and new techniques are required to be developed relating to it. Ontology mapping and mapping evaluation are undoubtedly one of the most important operations we need to develop in order to make the semantic web become practical for usage. The current available techniques for ontology mapping do not consider the semantics of the ontologies and use the traditional techniques for finding similarities between the ontologies. As a result the alignments produced are not as close or not as good as they should be. Traditional techniques use multiple strategies and combine the results by assigning fixed weights to each strategy, while in actual the ontologies are semantic in nature so the weights should also be assigned according to their semantics. We propose the concept of conditional information quantity which modifies formulas for computing similarity and set the weights according to the semantic contexts of strategies. Just as mapping is important, evaluating the results of mapping algorithms is also important. There are various techniques or evaluating alignments developed at different times, but precision and recall are the most commonly used techniques. The traditional evaluation techniques, just like traditional mapping techniques, do not consider the semantics of the algorithms. Here, we propose a framework for purely semantic precision and recall. This framework will make sure that the evaluation measures are purely semantic. We will also instantiate the framework with some application oriented evaluation measures.