xpSHACL: Explainable SHACL Validation using Retrieval-Augmented Generation and Large Language Models

TL;DR

xpSHACL enhances SHACL validation by integrating rule-based explanations with large language models, providing detailed, multilingual, human-readable reports for non-technical users, leveraging a Violation KG for efficiency.

Contribution

It introduces xpSHACL, a novel system combining rule-based justification trees with RAG and LLMs to generate explainable, multilingual SHACL validation reports.

Findings

Produces detailed, human-readable explanations for violations.

Supports multiple languages for broader accessibility.

Uses a Violation KG to improve explanation reuse and system efficiency.

Abstract

Shapes Constraint Language (SHACL) is a powerful language for validating RDF data. Given the recent industry attention to Knowledge Graphs (KGs), more users need to validate linked data properly. However, traditional SHACL validation engines often provide terse reports in English that are difficult for non-technical users to interpret and act upon. This paper presents xpSHACL, an explainable SHACL validation system that addresses this issue by combining rule-based justification trees with retrieval-augmented generation (RAG) and large language models (LLMs) to produce detailed, multilanguage, human-readable explanations for constraint violations. A key feature of xpSHACL is its usage of a Violation KG to cache and reuse explanations, improving efficiency and consistency.