arXiv:2507.22951v2 Announce Type: replace Abstract: Knowledge Graphs organize information as entity-relation-entity triples, enabling machine learning models to predict plausible missing triples in a task known as Knowledge Graph Completion (KGC). Post-hoc explainability for KGC addresses the problem of identifying which triples most influence the predictions of machine learning models. Currently, the field lacks formalization and consistent evaluations, hindering reproducibility and cross-study comparisons. This paper argues for a unified taxonomy for post-hoc explainability in KGC. First, we
The proliferation of complex AI models like Knowledge Graph Completions necessitates robust explainability to ensure trust and reliability, especially as AI integrates into critical functions.
Improved explainability in AI, particularly for KGC, enhances understanding, debugging, and auditability of AI decisions, which is crucial for adoption in sensitive domains.
The ability to formally evaluate and compare post-hoc explanations for KGC models will standardize research and development, accelerating the deployment of more transparent AI.
- · AI researchers
- · AI developers
- · Industries relying on KGC
- · Regulatory bodies
- · Opaque AI systems
- · Developers neglecting explainability
Standardized evaluation paves the way for more reproducible and comparable research in AI explainability.
Increased trust in KGC models could accelerate their adoption in areas requiring high transparency, like finance or healthcare.
Formalized explainability could eventually influence regulatory frameworks for AI, mandating specific transparency standards for deployment.
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Read at arXiv cs.AI