Zhongni Hou


2022

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HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning
Zixuan Li | Zhongni Hou | Saiping Guan | Xiaolong Jin | Weihua Peng | Long Bai | Yajuan Lyu | Wei Li | Jiafeng Guo | Xueqi Cheng
Findings of the Association for Computational Linguistics: EMNLP 2022

A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (subject, relation, object, timestamp) to describe dynamic facts. TKG reasoning has facilitated many real-world applications via answering such queries as (query entity, query relation, ?, future timestamp) about future. This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps. In addition, recent KGs provide background knowledge of all the entities, which is also helpful for the matching. Thus, in this paper, we propose the Historical Structure Matching (HiSMatch) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Besides, it adopts another encoder to integrate the background knowledge into the model. TKG reasoning experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6% performance improvement in MRR, compared to the state-of-the-art baselines.

2021

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Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning
Zhongni Hou | Xiaolong Jin | Zixuan Li | Long Bai
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021