Publication and maintenance of RDB2RDF views externally materialized in enterprise knowledge graphs

Abstract

Purpose Enterprise knowledge graphs (EKG) in resource description framework (RDF) consolidate and semantically integrate heterogeneous data sources into a comprehensive dataspace. However, to make an external relational data source accessible through an EKG, an RDF view of the underlying relational database, called an RDB2RDF view, must be created. The RDB2RDF view should be materialized in situations where live access to the data source is not possible, or the data source imposes restrictions on the type of query forms and the number of results. In this case, a mechanism for maintaining the materialized view data up-to-date is also required. The purpose of this paper is to address the problem of the efficient maintenance of externally materialized RDB2RDF views. Design/methodology/approach This paper proposes a formal framework for the incremental maintenance of externally materialized RDB2RDF views, in which the server computes and publishes changesets, indicating the difference between the two states of the view. The EKG system can then download the changesets and synchronize the externally materialized view. The changesets are computed based solely on the update and the source database state and require no access to the content of the view. Findings The central result of this paper shows that changesets computed according to the formal framework correctly maintain the externally materialized RDB2RDF view. The experiments indicate that the proposed strategy supports live synchronization of large RDB2RDF views and that the time taken to compute the changesets with the proposed approach was almost three orders of magnitude smaller than partial rematerialization and three orders of magnitude smaller than full rematerialization. Originality/value The main idea that differentiates the proposed approach from previous work on incremental view maintenance is to explore the object-preserving property of typical RDB2RDF views so that the solution can deal with views with duplicates. The algorithms for the incremental maintenance of relational views with duplicates published in the literature require querying the materialized view data to precisely compute the changesets. By contrast, the approach proposed in this paper requires no access to view data. This is important when the view is maintained externally, because accessing a remote data source may be too slow.

Guest editorial: Special issue on “current topics of knowledge graphs and semantic web”

“…The selected papers are on many different topics, such as process mining application on patient waiting time (Dogan, 2022); maintenance of RDB2RDF in enterprise knowledge graphs (Vidal et al , 2022); deep neural network-based approach for fake news detection (Katariya et al , 2022); ranking community detection algorithms for complex social networks (Rani and Kumar, 2022); agglomerative clustering enhanced GA for optimal seed selection (Mehta, 2022); CNN-BERT for measuring agreement (Harly and Girsang, 2022); hotel room personalization via ontology and rule-based reasoning (Ojino et al , 2022); fake news detection on Twitter [1]; semiautomated process for generating knowledge graphs (Keshan et al , 2022); From ontology to knowledge graph with agile methods (DeBellis and Dutta, 2022); keyword-based faceted search interface for Knowledge Graph construction and exploration (Sellami and Zarour, 2022); and finally, applied personal profile ontology for personnel appraisals (Usip et al , 2022).…”