  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.
