An Efficient Approach for Semantic Relatedness Evaluation Based on Semantic Neighborhood In the context of natural language processing and information retrieval, ontologies can improve the results of the word sense disambiguation (WSD) techniques. By making explicit the semantics of the term, ontologybased semantic measures play a crucial role to determine how different ontology classes have a similar meaning. In this context, it is common to use semantic similarity as a basis for WSD. However, the measures generally consider only taxonomic relationships, which affects negatively the discrimination of two ontology classes that are related by the other relationship types. On the other hand, semantic relatedness measures consider diverse types of relationships to determine how much two classes on the ontology are related. However, these measures, especially the pathbased approaches, has as the main drawback a high computational complexity to be calculated in query execution time. Also, for both types of semantic measures, it is unpractical to store all similarity or relatedness values between all ontology classes in memory, especially for large ontologies. In this work, we propose a novel approach based on semantic neighbors that aim to improve the query time in pathbased semantic measures without losing their effectiveness in relatedness analysis. We also propose an efficient algorithm to calculate the semantic distance between two ontology classes. We evaluate our proposal in WSD using a preexistent domain ontology for wellcore description. This ontology contains 929 classes related to rock facies and a set of sentences from four different corpora about geology in the Oilu0026Gas domain. In the experiments, we compared our approach with stateoftheart semantic relatedness measures, such as pathbased, featurebased, information content, and hybrid methods regarding the Fscore, query time and the total number of classes in memory. The experimental results show that the proposed method obtains Fscore gains in WSD, as well as an improvement in the query time concerning the traditional pathbased approaches. Also, we reduce the total number of classes stored in memory for each ontology class.

Towards Association RuleBased Complex Ontology Alignment Ontology alignment has been studied for over a decade, and over that time many alignment systems have been developed by researchers in order to find simple 1to1 equivalence alignments between ontologies. However, finding complex alignments, i.e., alignments that are not simple class or property equivalences, is a topic largely unexplored but with growing significance. Currently, establishing a complex alignment requires domain experts to work together to manually generate the alignment, which is extremely timeconsuming and laborintensive. In this paper, we propose an automated method based on association rule mining to detect not only simple alignments, but also more complex alignments between ontologies. Our algorithm can also be used in a semiautomated fashion to effectively assist users in finding potential complex alignments which they can then validate or edit. In addition, we evaluate the performance of our algorithm on the complex alignment benchmark of the Ontology Alignment Evaluation Initiative (OAEI).