Ontology Alignment and Coreference Resolution for Knowledge Graphs Portal

The vision of the Semantic Web is that computers as well as humans will be able to leverage the information on the web. One important capability that would facilitate this goal is using Knowledge Graph and Ontology. An ontology can serve as a underlying schema of Knowledge Graphs to enable logical inference and reasoning. An ontology is a representation of the concepts in a domain and how they relate to one another. Creating an ontology involves a lot of design decisions, which tend to be influenced by the designers’ backgrounds and the application they are targeting. The result is that two ontologies that represent the same domain will not necessarily be the same. Ontology alignment is considered as a key interoperability enabler for the Semantic Web, as well as a useful technique in some classical data integration tasks dealing with the semantic heterogeneity problem. It takes ontologies as input and determines as output an alignment, that is, a set of correspondences between the semantically related entities of those ontologies. These correspondences can be used for various tasks, such as ontology merging, data interlinking, query answering or process mapping. Thus, matching ontologies enables the knowledge and data expressed with the matched ontologies to interoperate. 

DaSe Lab has been working on Knowledge Graph Integration, including Ontology Alignment and Coreference Resolution for many years. Particularly, our expertise mostly includes 1) establishing and maintaining benchmarks for ontology alignment community for evaluating the performance of state of the art alignment algorithms, 2) generating and improving complex ontology alignment and coreference resolution systems, 3) reseaching and developing the evaulation matrixes to evaluate complex ontology alignment. 





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