Capturing Expert Knowledge for Building Enterprise SME Knowledge Graphs

Mansfield, Martin, Tamma, Valentina ORCID: 0000-0002-1320-610X, Goddard, Phil and Coenen, Frans ORCID: 0000-0003-1026-6649 (2021) Capturing Expert Knowledge for Building Enterprise SME Knowledge Graphs. In: K-CAP '21: Knowledge Capture Conference, 2021-12-2 - 2021-12-3.

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Official URL: http://dx.doi.org/10.1145/3460210.3493569

Abstract

Whilst Knowledge Graphs (KGs) are increasingly used in business scenarios, the construction of enterprise ontologies and the population of KGs from existing relational data remains a significant challenge. In this paper we report our experience in supporting CSols (an SME operating in the analytical laboratory domain) in transitioning their data from legacy databases to a bespoke KG. We modelled the KG using a streamlined approach based on state of the art ontology engineering methodologies, that addresses the challenges faced by SMEs when transitioning to new technologies: lack of resources to devote to the transition, paucity of comprehensive data governance policies, and resistance within the organisation to accepting new practices and knowledge. Our approach uses a combination of UML diagrams and a controlled language glossary to support stakeholders in reaching consensus during the knowledge capture phase, thus reducing the intervention of the ontology engineer only to cases where no agreement can be found. We present a case study illustrating the generation of the KG from a UML specification of part of the analytical domain and from legacy relational data, and we discuss the benefits and challenges of the approach.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Ontology Engineering, Enterprise Knowledge Graphs, Relational Data, R2RML, UML
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	02 Nov 2021 08:19
Last Modified:	27 Nov 2023 01:12
DOI:	10.1145/3460210.3493569
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3142369