Towards SHACL learning from knowledge graphs

Date

2020

Authors

Ghiasnezhad Omran, Pouya
Taylor, Kerry
Rodríguez Méndez, Sergio
Haller, Armin

Journal Title

Journal ISSN

Volume Title

Publisher

CEUR Workshop Proceedings

Abstract

Knowledge Graphs (KGs) are typically large data-first knowl- edge bases with weak inference rules and weakly-constraining data schemes. The SHACL Shapes Constraint Language is a W3C recommendation for the expression of shapes as constraints on graph data. SHACL shapes serve to validate a KG and can give informative insight into the structure of data. Here, we introduce Inverse Open Path (IOP) rules, a logical for- malism which acts as a building block for a restricted fragment of SHACL that can be used for schema-driven structural knowledge graph validation and completion. We define quality measures for IOP rules and propose a novel method to learn them, SHACLearner. SHACLearner adapts a state-of-the-art embedding-based open path rule learner (oprl) by modifying the efficient matrix-based evaluation module. We demonstrate SHACLearner performance on real-world massive KGs, YAGO2s (4M facts), DBpedia 3.8 (11M facts), and Wikidata (8M facts), finding that it can efficiently learn hundreds of high-quality rules.

Description

Keywords

SHACL Learning, Open Path Rule, Knowledge Graph, Rule Learning, Knowledge Graph

Citation

Source

Proceedings of the 19th International Semantic Web Conference on Demos and Industry Tracks (ISWC)

Type

Conference paper

Book Title

Entity type

Access Statement

Open Access

License Rights

Creative Commons Attribution 4.0 International License

DOI

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