Summarization of Massive RDF Graphs Using Identifier Classification

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

dos Santos, AF (Author) Other; José Paulo Leal (Author) FCUP

Conference proceedings International

Title: Computational Science - ICCS 2023 - 23rd International Conference, Prague, Czech Republic, July 3-5, 2023, Proceedings, Part I

Pages: 89-103

23rd International Conference on Computational Science, ICCS 2023

Prague, 3 July 2023 through 5 July 2023

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00Y-W08

DOI: 10.1007/978-3-031-40960-8_8

Abstract (EN): The size of massive knowledge graphs (KGs) and the lack of prior information regarding the schemas, ontologies and vocabularies they use frequently makes them hard to understand and visualize. Graph summarization techniques can help by abstracting details of the original graph to produce a reduced summary that can more easily be explored. Identifiers often carry latent information which could be used for classification of the entities they represent. Particularly, IRI namespaces can be used to classify RDF resources. Namespaces, used in some RDF serialization formats as a shortening mechanism for resource IRIs, have no role in the semantics of RDF. Nevertheless, there is often a hidden meaning behind the decision of grouping resources under a common prefix and assigning an alias to it. We improved on previous work on a namespace-based approach to KG summarization that classifies resources using their namespaces. Producing the summary graph is fast, light on computing resources and requires no previous domain knowledge. The summary graph can be used to analyze the namespace inter-dependencies of the original graph. We also present chilon, a tool for calculating namespace-based KG summaries. Namespaces are gathered from explicit declarations in the graph serialization, community contributions or resource IRI prefix analysis. We applied chilon to publicly available KGs, used it to generate interactive visualizations of the summaries, and discuss the results obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14