Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference
This work addresses the problem of enhancing explainability and semantics in knowledge graph inference for applications like question answering, representing an incremental improvement over existing path-based methods.
The paper tackles the challenge of improving knowledge graph inference by addressing the lack of explainability and poor semantics capture in existing path-based methods, proposing a joint semantics and data-driven path representation that achieves significant performance gains over state-of-the-art baselines in link prediction and path query answering tasks.
Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering. The path-based reasoning models can leverage much information over paths other than pure triples in the KG, which face several challenges: all the existing path-based methods are data-driven, lacking explainability for path representation. Besides, some methods either consider only relational paths or ignore the heterogeneity between entities and relations both contained in paths, which cannot capture the rich semantics of paths well. To address the above challenges, in this work, we propose a novel joint semantics and data-driven path representation that balances explainability and generalization in the framework of KG embedding. More specifically, we inject horn rules to obtain the condensed paths by the transparent and explainable path composition procedure. The entity converter is designed to transform the entities along paths into the representations in the semantic level similar to relations for reducing the heterogeneity between entities and relations, in which the KGs both with and without type information are considered. Our proposed model is evaluated on two classes of tasks: link prediction and path query answering task. The experimental results show that it has a significant performance gain over several different state-of-the-art baselines.