Capturing Symbolic Knowledge of Constraints and Incompleteness to Guide Inductive Learning in Neuro-Symbolic Knowledge Graph Completion

Abstract

Knowledge Graphs (KGs) are widely used to represent structured knowledge. However, their incompleteness under the Open World Assumption (OWA) limits their effectiveness for reasoning and inference. Neural link prediction models can recover missing links. Yet, these models often overlook the distinction between semantically valid and invalid inferences and lack mechanisms to validate predictions against domain-specific constraints. In sensitive domains such as healthcare, predicting a plausible but contraindicated relation can have harmful consequences. This work addresses semantically grounded KG completion by extending the Partial Completeness Assumption (PCA) with two metrics—PCAvalid and PCAinvalid. These metrics distinguish constraint-compliant from constraint-violating predictions using SHACL validation. They guide the selection of symbolic rules and the generation of labeled training data for neural models. As a result, link prediction systems can assess both plausibility and semantic validity. Experiments on 315 testbeds demonstrate that incorporating constraint-aware symbolic knowledge enhances MRR and Hits@K across multiple KG embedding models, including TransE and TransH. Thus, this approach supports interpretable and trustworthy KG completion.