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| Conversational Question Answering Based on Knowledge Graph and Coreference Resolution |
| WANG Jiahui1,2, ZHAO Linchao1,2, YIN Zhaorui1,2, YUE Kun1,2, CHEN Xingtong1,2, DUAN Liang1,2 |
1. Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming 650500;
2. School of Information Science and Engineering, Yunnan University, Kunming 650500 |
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Abstract There are two urgent challenges in conversational question answering to be addressed at present. One is how coreference and long range dependencies can be resolved to effectively utilize dependency information. The other is how contextual query subgraphs can be effectively maintained to avoid the risk of excessive expansion, thereby enabling more precise answer retrieval within them. In this paper, a model of conversational question answering based on knowledge graph and coreference resolution is proposed. First, coreference resolution is employed to obtain coreference clusters and an index replacement algorithm is introduced to enhance the semantic information of questions. Additionally, two types of dependency information, word coreference structure and character semantics, are proposed to guide the expansion of contextual query subgraph and answer retrieval. The contextual query subgraph is effectively expanded based on dependency information to obtain accurate query subgraph while avoiding overgrowth. Then, a reward-and-punishment mechanism is designed based on the number of dialogue rounds and the size of the query subgraph to effectively prevent the subgraph from overgrowing. Finally, dependency information is utilized to effectively improve the accuracy of answer retrieval. Experiments on the ConvQuestions dataset verify the effectiveness of the proposed method.
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Received: 03 November 2025
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| Fund:National Natural Science Foundation of China(No.U23A20298), Key Project of Yunnan Fundamental Research Special Fund(No.202501AS070102,202401AS070138), Open Project of Yunnan Key Laboratory of Intelligent Control and Application(No.2025ICA01) |
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Corresponding Authors:
DUAN Liang, Ph.D., associate professor. His research interests include graph analysis, structural information theory and Bayesian deep learning.
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| About author:: WANG Jiahui, Ph.D., assistant profe-ssor. His research interests include data and knowledge engineering, domain knowledge mining and uncertain knowledge reasoning. ZHAO Linchao, Master student. His research interests include data and knowledge engineering. YIN Zhaorui, Master student. His research interests include data and knowledge enginee?ring. YUE Kun, Ph.D., professor. His research interests include graph data analysis, big data knowledge engineering, neural symbolic computation and Bayesian deep learning. CHEN Xingtong, Master, assistant engineer. His research interests include data and knowledge engineering. |
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2026, Vol. 39 
