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US 2022/0101153 A1Published application
Processes and products for generation of completion rules of knowledge graphs
AssigneeSiemens Aktiengesellschaft
InventorThomas Hubauer
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Abstract
Provided is a computer-implemented technology which generates rules for completion of a knowledge graph by producing, with a generic machine learning model or one that is trained on the knowledge graph, inferred triples, optionally refines and filters the produced rules along predefined user settings and provides the resulting rules, along with the inferred facts covered by the rules, as candidates for completion of the knowledge graph.
Claims13
A computer-implemented method for generation of completion rules for a knowledge graph, comprising the following steps:
- using a machine learning model to produce a set of inferred triples from RDF data, wherein the model is a generic model or a model trained and/or retrained on the knowledge graph or a subset of the knowledge graph;
- generating completion rules with a same functionality as SPARQL queries of the form
- INSERT ?subject ?predicate ?object
- WHERE {
- triple_pattern_1 .
- triple_pattern_2 .
- ...
- }
- as follows:
- adding to completion rules, that would produce triples that are not part of the set, triple patterns that result in the exclusion of these triples;
- allowing to combine alternatives with a function corresponding to OR-disjunction in inductive logic programming;
- stopping the generating once a pre-defined ratio of coverage of the set is reached or a user-defined execution time timeout is met;
- providing the resulting completion rules as candidates for completion of the knowledge graph.
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