City Research Online

Abstract

Automatic knowledge graph (KG) construction is widely used in industry for data integration and access, and there are several approaches to enable (semi-)automatic construction of knowledge graphs. One important approach is to map the raw data to a given knowledge graph schema, often a domain ontology, and construct the entities and properties according to the ontology. However, the existing approaches to construct knowledge graphs are not always efficient enough and the resulting knowledge graphs are not sufficiently application-oriented and user-friendly. The challenge arises from the trade-off: the domain ontology should be knowledge-oriented, to reflect the general domain knowledge rather than data particularities; while a knowledge graph schema should be data-oriented, to cover all data features. If the former is directly used as the knowledge graph schema, this can cause issues like blank nodes created due to classes unmapped to data and deep knowledge graph structures. To this end, we propose a system for ontology reshaping, which generates knowledge graph schemata that fully cover the data while also covers domain knowledge well. We evaluated our approach extensively with a user study and three real manufacturing datasets from Bosch against four baselines, showing promising results.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	This version of the contribution has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-19433-7_44. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms
Publisher Keywords:	Semantic data integration, Knowledge graph, Ontology reshaping, Graph algorithm, Automatic knowledge graph construction
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments:	School of Science & Technology > Computer Science

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