Enhancing Complex Question Answering over Knowledge Graphs through Evidence Pattern Retrieval
Wentao Ding, Jinmao Li, Liangchuan Luo, Yuzhong Qu
Abstract
Information retrieval (IR) methods for KGQA consist of two stages: subgraph extraction and answer reasoning. We argue that current subgraph extraction methods underestimate the importance of structural dependencies among evidence facts. We propose Evidence Pattern Retrieval (EPR) to explicitly model the structural dependencies during subgraph extraction. We implement EPR by indexing the atomic adjacency pattern formed by resource pairs. Given a question, we perform dense retrieval to obtain atomic patterns. We then enumerate their combinations to construct candidate evidence patterns. These evidence patterns are scored using a neural model, and the best one is selected to extract a subgraph for downstream answer reasoning. Experimental results demonstrate that the EPR-based approach has significantly improved the F1 scores of IR-KGQA methods by over 10 points on ComplexWebQuestions and achieves competitive performance on WebQuestionsSP.