Papers by Zhongni Hou
Temporal Knowledge Graph Reasoning Based on N-tuple Modeling (2023.findings-emnlp)
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| Challenge: | Existing Temporal Knowledge Graphs (TKGs) only contain their core entities and form them as quadruples. |
| Approach: | They propose to describe a temporal fact more accurately as an n-tuple . they propose to use a neural network to learn evolutional representations of entities . |
| Outcome: | The proposed model oversimplifies and causes information loss on two datasets. |
Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning (2021.findings-acl)
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| Challenge: | Existing methods to reason missing facts on Knowledge Graphs face with serious incompleteness due to their black-box nature. |
| Approach: | They propose a multi-hop reasoning method that injects high quality symbolic rules into the model's reasoning process and employs partially random beam search. |
| Outcome: | The proposed method outperforms existing multi-hop reasoning methods in terms of Hit@1 and MRR. |
LongReward: Improving Long-context Large Language Models with AI Feedback (2025.acl-long)
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Jiajie Zhang, Zhongni Hou, Xin Lv, Shulin Cao, Zhenyu Hou, Yilin Niu, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li
| Challenge: | In recent years, significant advancements have been achieved in the development of long-context large language models (LLMs). |
| Approach: | They propose a method that utilizes an off-the-shelf LLM to provide rewards for long-context model responses from four human-valued dimensions: helpfulness, logicality, faithfulness, and completeness. |
| Outcome: | The proposed method improves models’ long-context performance and enhances their ability to follow short instructions. |
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning (2022.findings-emnlp)
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Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Temporal Knowledge Graphs (TKGs) store facts as triples in the form of subject, relation, object, timestamps. |
| Approach: | They propose a Temporal Knowledge Graph (TKG) model that extends each triple with a timestamp to describe dynamic facts. |
| Outcome: | The proposed model improves on six benchmark datasets with up to 5.6% performance improvement compared to the state-of-the-art models. |
Selective Temporal Knowledge Graph Reasoning (2024.lrec-main)
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| Challenge: | Existing models cannot abstain from uncertain predictions, which will bring risks in real-world applications. |
| Approach: | They propose to abstain from uncertain future facts by using a confidence estimator . they take both the certainty of the current prediction and the accuracy of historical predictions into account . |
| Outcome: | The proposed abstention mechanism helps existing models make selective predictions instead of indiscriminate ones. |
Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents (2026.findings-acl)
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Miao Su, Yucan Guo, Zhongni Hou, Long Bai, Zixuan Li, Yufei Zhang, Guojun Yin, Wei Lin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing methods focus on point-wise memory, losing durative information that captures persistent states and evolving patterns. |
| Approach: | They propose a memory framework that models semantic time for point-wise memory and supports the construction and utilization of durative memory. |
| Outcome: | Experiments on LongMemEval and LoCoMo show that the proposed method outperforms existing methods and achieves up to 12.2% improvement in accuracy. |
A Survey of Link Prediction in N-ary Knowledge Graphs (2025.emnlp-main)
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Jiyao Wei, Saiping Guan, Da Li, Zhongni Hou, Miao Su, Yucan Guo, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | N-ary Knowledge Graphs (NKGs) capture n-ary facts containing more than two entities. |
| Approach: | They present the first comprehensive survey of link prediction in NKGs . they provide an overview of the field and analyze their performance and application scenarios . |
| Outcome: | The proposed methods provide an overview of the field and analyze performance and application scenarios. |