Towards matching of domain-specific schemas using general-purpose external background knowledge

Portisch, Jan

DOI:	https://doi.org/10.1007/978-3-030-62327-2
URL:	https://link.springer.com/chapter/10.1007/978-3-03...
Additional URL:	https://preprints.2020.eswc-conferences.org/phd_sy...
Document Type:	Conference or workshop publication
Year of publication:	2020
Book title:	The semantic web: ESWC 2020 satellite events : ESWC 2020 Satellite Events, Heraklion, Crete, Greece, May 31 - June 4, 2020 : revised selected papers
The title of a journal, publication series:	Lecture Notes in Computer Science
Volume:	12124
Page range:	270-279
Conference title:	ESWC 2020
Location of the conference venue:	Online
Date of the conference:	31.05.-04.06.2020
Publisher:	Harth, Andreas ; Presutti, Valentina ; Troncy, Raphaël ; Acosta, Maribel ; Polleres, Axel ; Fernández, Javier D. ; Xavier Parreira, Josiane ; Hartig, Olaf ; Hose, Katja ; Cochez, Michael
Place of publication:	Berlin [u.a.]
Publishing house:	Springer
ISBN:	978-3-030-62326-5 , 978-3-030-62327-2
ISSN:	0302-9743 , 1611-3349
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Data Science (Paulheim 2018-)
Subject:	004 Computer science, internet
Individual keywords (German):	Datenintegration, semantische Integration , Wissensgraphen , Hintergrundwissen , Ontologien
Keywords (English):	data integration , schema matching , ontology matching , background knowledge , knowledge graphs , financial services industry
Abstract:	Schema matching is an important and time consuming part within the data integration process. Yet, it is rarely automatized – particularly in the business world. In recent years, the amount of freely available structured knowledge has grown exponentially. Large knowledge graphs such as BabelNet, DBnary (Wiktionary in RDF format),DBpedia, or Wikidata are available. However, these knowledge bases are hardly exploited for automated matching. One exception is the biomedical domain: Here domain-specific background knowledge is broadly available and heavily used with a focus on reusing existing alignments andon exploiting larger, domain-specific mediation ontologies. Nonetheless, outside the life sciences domain such specialized structured resources are rare. In terms of general knowledge, few background knowledge sources are exploited except for WordNet. In this paper, we present our research idea towards further exploiting general-purpose background knowledge within the schema matching process. An overview of the state of the art is given and we outline how our proposed research approach fits in. Potentials and limitations are discussed and we summarize our intermediate findings.