Enhancing Complex Question Answering over Knowledge Graphs through
  Evidence Pattern Retrieval
Wentao Ding Jinmao Li Liangchuan Luo Yuzhong Qu
Information retrieval (IR) methods for KGQA consist of two stages: subgraph
extraction and answer reasoning. We argue 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 of resource pairs. Given a question, we perform dense
retrieval to obtain atomic patterns formed by resource pairs. 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.