  Knowledge Graph Question Answering (KGQA) aims to answer user-questions from
a knowledge graph (KG) by identifying the reasoning relations between topic
entity and answer. As a complex branch task of KGQA, multi-hop KGQA requires
reasoning over the multi-hop relational chain preserved in KG to arrive at the
right answer. Despite recent successes, the existing works on answering
multi-hop complex questions still face the following challenges: i) The absence
of an explicit relational chain order reflected in user-question stems from a
misunderstanding of a user's intentions. ii) Incorrectly capturing relational
types on weak supervision of which dataset lacks intermediate reasoning chain
annotations due to expensive labeling cost. iii) Failing to consider implicit
relations between the topic entity and the answer implied in structured KG
because of limited neighborhoods size constraint in subgraph retrieval-based
algorithms.To address these issues in multi-hop KGQA, we propose a novel model
herein, namely Relational Chain based Embedded KGQA (Rce-KGQA), which
simultaneously utilizes the explicit relational chain revealed in natural
language question and the implicit relational chain stored in structured KG.
Our extensive empirical study on three open-domain benchmarks proves that our
method significantly outperforms the state-of-the-art counterparts like
GraftNet, PullNet and EmbedKGQA. Comprehensive ablation experiments also verify
the effectiveness of our method on the multi-hop KGQA task. We have made our
model's source code available at github:
https://github.com/albert-jin/Rce-KGQA.
