A novel community answer matching approach based on phrase fusion heterogeneous information network

Yongliang Wu, Shuliang Zhao, Ruiqiang Guo

Abstract: Highlights•To the best of our knowledge, it is the first work to propose the phrase information network and employ it to construct a fusion heterogeneous information network (HIN) to represent complex entity relationships in community question answering (CQA).•We define the distance of entities with the same or different types in HIN and propose a novel Type-constrained Top-k similarity entity finding algorithm (TTSEF) for answer selection, which innovatively combines entity attributes and semantic features to achieve answer selection in CQA.•Abundant experimental demonstrate that proposed algorithm precedes the state-of-the-art similar entity matching methods in CQA.•A meta-path analysis of the optimal matching answers proves that phrase can serve as a bridge to connect different types of entities in CQA effectively.