Evidence-Carrying Validation for Knowledge Graphs

2026-08-16

AI Summary

This study addresses the lack of concrete evidence and diagnostic difficulty for missing information in knowledge graph validation by proposing an evidence-carrying validation interface. We introduce mutually recursive evidence objects that preserve constraints, paths, and triples to generate either satisfaction traces or failure witnesses, thereby enabling precise diagnosis and repair. Evaluations using the Shifty engine on real-world corpora demonstrate that this approach incurs only a 1.54× to 2.07× overhead in evidence generation. Consequently, the method effectively resolves the interpretability challenge of validation results while maintaining high efficiency, significantly enhancing quality assurance capabilities for knowledge graphs.

Abstract

Programs that consume a knowledge graph they do not maintain, such as applications, authoring platforms, and LLM agents, need to know whether the graph contains the information their task requires. Validating the graph against a schema can answer this question, but existing validation interfaces usually return a conformance bit or failure-oriented report without identifying why checks pass or the partial matches behind failures. We present an evidence-carrying validation interface: every selected node-shape check returns either a satisfaction trace or failure witness. These are mutually recursive objects that retain constraints, cardinality decisions, paths, and supporting triples. We implement this interface in Shifty, an experimental SHACL validator. Against two real-world shape graph corpora, materializing all-pair evidence costs a median 1.54-2.07X conformance-only validation. A case study then shows how programs combine passing and failing evidence to diagnose missing information and guide repair.

Problem

• Knowledge Graph Validation
• SHACL
• Evidence-Carrying Validation
• Schema Conformance

Innovation

• Evidence-Carrying Validation
• SHACL
• Satisfaction Trace
• Failure Witness
• Knowledge Graph Repair