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Learning SHACL shapes from knowledge graphs
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DOI：10.3233/SW-223063.png)
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Abstract
En 中文
Knowledge Graphs (KGs) have proliferated on the Web since the introduction of knowledge panels to Google search in 2012. KGs are large data-first graph databases with weak inference rules and weakly-constraining data schemes. SHACL, the Shapes Constraint Language, is a W3C recommendation for expressing constraints on graph data as shapes. SHACL shapes serve to validate a KG, to underpin manual KG editing tasks, and to offer insight into KG structure. Often in practice, large KGs have no available shape constraints and so cannot obtain these benefits for ongoing maintenance and extension. We introduce Inverse Open Path (TOP) rules, a predicate logic formalism which presents specific shapes in the form of paths over connected entities that are present in a KG. IOP rules express simple shape patterns that can be augmented with minimum cardinality constraints and also used as a building block for more complex shapes, such as trees and other rule patterns. We define formal quality measures for IOP rules and propose a novel method to learn high-quality rules from KGs. We show how to build high-quality tree shapes from the IOP rules. Our learning method, SHACLEARNER, is adapted from a state-of-the-art embedding-based open path rule learner (OPRL). We evaluate SHACLEARNER on some real-world massive KGs, including YAGO2s (4M facts), DBpedia 3.8 (11M facts), and Wikidata (8M facts). The experiments show that our SHACLEARNER can effectively learn informative and intuitive shapes from massive KGs. The shapes are diverse in structural features such as depth and width, and also in quality measures that indicate confidence and generality.
Keywords:
SHACL shape learning
shapes constraint language
knowledge graph
inverse open path rule