Extracting Semantic Subgraphs to Capture the Real Meanings of Ontology Elements

An element may have heterogeneous semantic interpretations in different ontologies. Therefore, understanding the real local meanings of elements is very useful for ontology operations such as querying and reasoning, which are the foundations for many applications including semantic searching, ontology matching, and linked data analysis. However, since different ontologies have different preferences to describe their elements, obtaining the semantic context of an element is an open problem. A semantic subgraph was proposed to capture the real meanings of ontology elements. To extract the semantic subgraphs, a hybrid ontology graph is used to represent the semantic relations between elements. An extracting algorithm based on an electrical circuit model is then used with new conductivity calculation rules to improve the quality of the semantic subgraphs. The evaluation results show that the semantic subgraphs properly capture the local meanings of elements. Ontology matching based on semantic subgraphs also demonstrates that the semantic subgraph is a promising technique for ontology applications.

GeoLink Data Set:A Complex Alignment Benchmark from Real-world Ontology

Ontology alignment has been studied for over a decade,and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies.However,very few alignment systems focus on finding complex correspondences.One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships.In this paper,we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark.The data set consists of two ontologies,the GeoLink Base Ontology(GBO)and the GeoLink Modular Ontology(GMO),as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions.The alignment includes 1:1,1:n,and m:n equivalence and subsumption correspondences,and is available in both Expressive and Declarative Ontology Alignment Language(EDOAL)and rule syntax.The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies.This allows it to be used by extensional alignment systems or those that require training data.This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative(OAEI)complex track to help researchers test their automated alignment systems and algorithms.This paper also analyzes the challenges inherent in effectively generating,detecting,and evaluating complex ontology alignments and provides a road map for future work on this topic.

Application of Improved Compact Particle Swarm Optimization to Large Ontology Alignment Task

Ontology occupies an important position in artificial intelligence,computer linguistics and knowledge management.However,when different ontologies are constructed to represent the same information in a domain,the so-called heterogeneity problem arises.In order to address this problem,a key task is to discover the semantic relationship of entities between given two ontologies,called ontology alignment.Recently,the meta-heuristic algorithms have already been regarded as an effective approach for solving ontology alignment problem.However,firstly,as the ontologies become increasingly large,meta-heuristic algorithms may be easier to find local optimal alignment in large search spaces.Secondly,many existing approaches exploit the population-based meta-heuristic algorithms so that the massive calculation is required.In this paper,an improved compact particle swarm algorithm by using a local search strategy is proposed,called LSCPSOA,to improve the performance of finding more correct correspondences.In LSCPSOA,two update strategies with local search capability are employed to avoid falling into a local optimal alignment.The proposed algorithm has been evaluated on several large ontology data sets and compared with existing ontology alignment methods.The experimental results show that the proposed algorithm can find more correct correspondences and improves the time performance compared with other meta-heuristic algorithms.