LogicENN: A Neural Based Knowledge Graphs Embedding Model with Logical Rules
This work addresses a key bottleneck in AI for knowledge representation by enabling rule inclusion in embeddings, though it is incremental as it builds on existing neural-based methods.
The authors tackled the challenge of incorporating logical rules into knowledge graph embedding models, presenting LogicENN, a neural-based model that can learn all ground truths of encoded rules and outperforms state-of-the-art models in link prediction tasks.
Knowledge graph embedding models have gained significant attention in AI research. Recent works have shown that the inclusion of background knowledge, such as logical rules, can improve the performance of embeddings in downstream machine learning tasks. However, so far, most existing models do not allow the inclusion of rules. We address the challenge of including rules and present a new neural based embedding model (LogicENN). We prove that LogicENN can learn every ground truth of encoded rules in a knowledge graph. To the best of our knowledge, this has not been proved so far for the neural based family of embedding models. Moreover, we derive formulae for the inclusion of various rules, including (anti-)symmetric, inverse, irreflexive and transitive, implication, composition, equivalence and negation. Our formulation allows to avoid grounding for implication and equivalence relations. Our experiments show that LogicENN outperforms the state-of-the-art models in link prediction.