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Ontology matching is a solution to the semantic heterogeneity problem between different ontologies or knowledge graphs. 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. Its applications range from enabling interoperability between ontologies or data and systems based thereon, to knowledge and data integration. It has firm roots in the field of knowledge representation and reasoning, but bridges into software engineering, information retrieval, machine learning, natural language processing, data visualization, and human-computer interaction.
Despite decades of research, interest in ontology alignment has not waned, as ontologies have gained increasing traction as vehicles for knowledge sharing, especially in data-intensive domains, such as the life sciences. The endless stream of matching problems to solve and the open-ended nature of challenges such as scalability and user interaction means that this field is in constant evolution, often through incremental improvements but sometimes also through paradigm shifts. Furthermore, specific challenges, such as interactive matching or expressive alignments, pose technical problems not only to enfranchised tools and algorithms, but also with respect to the evaluation of those tools and algorithms.
The ontology matching community rallies annually for the Ontology Alignment Evaluation Initiative (OAEI), a friendly competition for assessing ontology alignment tools and algorithms which became one of the main drivers of innovation in this field as well as the de facto testing ground for new challenges and ideas. The OAEI results are presented and discussed during the Ontology Matching workshops (OM), collocated with the International Semantic Web Conference, which serve as a forum not only for this purpose, but also for discussing theoretical and practical experiences made with applying diverse ontology matching approaches.
This special issue gathers selected papers that describe new challenges, benchmarks, and tools, most of which participated or were presented in the OAEI and OM editions of 2018.
Pavel Shvaiko - Trentino Digitale SpA, Italy
Daniel Faria - INESC-ID, Lisbon, Portugal
Ernesto Jiménez-Ruiz - City, University of London, UK and University of Oslo, Norway