Efficient Subgraph Pruning & Embedding for Multi-Relation QA over Knowledge Graph

The intelligent question answering over the knowledge graph aims to automatically answer natural language questions via locating the correct entities in the knowledge graph. Aside from the former progresses, it is still challenging to answer the multi-relation questions because of the variety and complexity of the natural language, as well as the combinatorial explosion on possible candidates. In this paper, we propose a novel embedding-based approach named SPE-QA to address these issues. It answers a question by identifying its most semantic like question-answer path from the candidate topic-entity-centric subgraph, and locating this path's tail entity as the final answer. In order to limit the scale of the candidate sub graph and thus reduce the neural network's training complexity, it is essential to filter the explicit noises as much as possible. Therefore, we employ a sub graph separation method to decide the scale of the sub graph, and remove the false question-answer paths with two different pruning policies. The experimental results on two widely used benchmarks approve that, our mechanism can not only reduce the subgraph's scale dramatically, but also obtain better performance on the multi-relation questions, compare to the stat-of-the-art approaches.