Towards SHACL learning from knowledge graphs
Date
2020
Authors
Ghiasnezhad Omran, Pouya
Taylor, Kerry
Rodríguez Méndez, Sergio
Haller, Armin
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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.
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Keywords
SHACL Learning, Open Path Rule, Knowledge Graph, Rule Learning, Knowledge Graph
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Source
Proceedings of the 19th International Semantic Web Conference on Demos and Industry Tracks (ISWC)
Type
Conference paper
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Open Access
License Rights
Creative Commons Attribution 4.0 International License
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Restricted until
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