{"id":289,"date":"2024-08-30T11:49:17","date_gmt":"2024-08-30T11:49:17","guid":{"rendered":"https:\/\/sigkg.cn\/ccks-ijckg2024\/en\/?page_id=289"},"modified":"2024-09-06T06:21:09","modified_gmt":"2024-09-06T06:21:09","slug":"accepted-papers","status":"publish","type":"page","link":"http:\/\/sigkg.cn\/ccks-ijckg2024\/en\/accepted-papers\/","title":{"rendered":"Accepted Papers"},"content":{"rendered":"\n

Conference Papers<\/h2>\n\n\n\n
1<\/td>Yuhao Dan, Zhikai Lei, Yiyang Gu, Yong Li, Jianghao Yin, Jiaju Lin, Linhao Ye, Zhiyan Tie, Yougen Zhou, Yilei Wang, Aimin Zhou, Ze Zhou, Qin Chen, Jie Zhou, Liang He and Xipeng Qiu<\/td>EduChat: A Large Language Model Based Conversational Agent for Intelligent Education<\/td><\/tr>
2<\/td>Wei Tang, Zexin Wang, Xun Mao, Kai Lv, Xu Han, Ming Liu, Wei Wang and Bing Qin<\/td>Relation Inquiry: A Novel Synchronous Joint Extractor for Entities and Relations<\/td><\/tr>
3<\/td>Hongbin Ye, Honghao Gui, Aijia Zhang, Tong Liu and Weiqiang Jia<\/td>Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction<\/td><\/tr>
4<\/td>Zhuang Wang, Yijia Zhang, Jianyuan Yuan, Songtao Li, Mingyu Lu and Hongfei Lin<\/td>SCM-Net: Semantic-Contrastive Multimodal Framework for Enhanced Chinese NER<\/td><\/tr>
5<\/td>Jiawei Meng and Wen Zhang<\/td>KG-diffusion: an Improved Knowledge Graph Completion with Diffusion<\/td><\/tr>
6<\/td>Xinyan Guan, Hongyu Lin, Yaojie Lu, Sirui Wang, Xunliang Cai, Xianpei Han and Le Sun<\/td>Benchmarking Knowledge Graph-grounded Factual Verification<\/td><\/tr>
7<\/td>Hongbang Yuan, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu and Jun Zhao<\/td>Mitigating Multi-Hop Hallucination in Large Language Models with Non-Authoritative Knowledge Sources<\/td><\/tr>
8<\/td>Runhua Hu and Xiaohua Ke<\/td>Flexible Multi-view Subspace Clustering with Anchor Structure Alignment<\/td><\/tr>
9<\/td>Huixian Cai, Yijia Zhang, Jianyuan Yuan and Hongfei Lin<\/td>Biomedical Document Relation Extraction Via Mention-Entity Double Fusion and Contrast Enhanced Inference<\/td><\/tr>
10<\/td>Xiaoyi Liu, Shuangtao Yang, Xiaozheng Dong, Honghui Rong and Bo Fu<\/td>Manu-Eval: A Chinese Language Understanding Benchmark for Manufacturing Industry<\/td><\/tr>
11<\/td>Na Li, Jiaxin Hu, Sen Ai and Xiang Zhang<\/td>GVDExtractor: Document-level Ternary Relation Extraction of Gene-Variant-Disease from Medical Literature<\/td><\/tr>
12<\/td>Yu Song, Dezhi Kong, Bohan Yu, Kunli Zhang and Zan Hongying<\/td>LLM-AR: Large Language Model Augmented Retrieval for Few-shot Knowledge Graph Completion<\/td><\/tr>
13<\/td>Xiaotong Qin, Tong Zhou, Yubo Chen, Kang Liu and Jun Zhao<\/td>A Comprehensive Ontology Knowledge Evaluation System for Large Language Models<\/td><\/tr>
14<\/td>Yu Song, Yongqi Zhu, Kunli Zhang, Yingjie Han, Chenkang Zhu and Bohan Yu<\/td>Cardiovascular Disease Knowledge Graph Reasoning Method Based on ConvKB Link Predication<\/td><\/tr>
15<\/td>Ziyou He, Zhoushuai Xu, Wenbo Zheng and Yuanming Zhang<\/td>Hierarchical Knowledge Graph Attention Network for Recommendation Systems<\/td><\/tr>
16<\/td>Lijie Li, Yongyi Wang, Lingfu Wang, Jiahang Li, Ye Wang, Qilong Han and Tao Ren<\/td>Evolutionary Graph Network with Time-aware Attention for Temporal Knowledge Graph Reasoning<\/td><\/tr>
17<\/td>Zhiwei Huang, Juan Li, Long Jin, Junjie Wang, Mingchen Tu, Yin Hua, Zhiqiang Liu, Jiawei Meng and Wen Zhang<\/td>Reliable Academic Conference Question Answering: A Study Based on Large Language Model<\/td><\/tr>
18<\/td>\u8dc3\u7fd4 \u6768, \u96e8\u6770 \u9648 and \u71d5\u6e05 \u5218<\/td>The Framework Design of a Semantic Role-Based Knowledge Graph for Natural Disaster Emergency Response<\/td><\/tr>
19<\/td>Wenling Liu, Yuexiang Yang, Xinyu Tu and Wan Wang<\/td>Research on Automatic Extraction of Emergency Response Standards Concept Hierarchy Based on LDA<\/td><\/tr>
20<\/td>Jiayin Lan, Zexin Wang and Ming Liu<\/td>Few-shot Fine-grained Ship Detection<\/td><\/tr>
21<\/td>Zhi Yang, Yi Luo and Xin Xin<\/td>Alias Extraction Enhanced by Automatically Generated Long-Tail Instances<\/td><\/tr>
22<\/td>Chengfeng Zhao, Shizhu He, Kang Liu and Jun Zhao<\/td>Adaptive Factual Decoding for Hallucination Mitigation with Part-Of-Speech based Critics<\/td><\/tr>
23<\/td>Jiaye Li, Yuan Meng, Lijun Wang, Tianhao Qian, Songlin Zhai and Guilin Qi<\/td>Taxonomy Induction Using LLMs An Enhanced Framework by Integrating Doubly-Checked Mechanism and Self-Evaluation Strategy<\/td><\/tr>
24<\/td>Muhammad Salman, Haoting Chen, Sergio Jos\u00e9 Rodr\u00edguez M\u00e9ndez and Armin Haller<\/td>An LLM-SPARQL Hybrid Framework for Named Entity Linking and Disambiguation to Wikidata<\/td><\/tr>
25<\/td>Zhishu Jiang, Wei Chen, Weijie Zhang, Shiqi Zhang, Jiruo Chen and Huaiyu Wan<\/td>\u57fa\u4e8e\u793e\u4ea4\u65f6\u5e8f\u77e5\u8bc6\u56fe\u8c31\u7684\u63a8\u7279\u673a\u5668\u4eba\u68c0\u6d4b\u65b9\u6cd5<\/td><\/tr>
26<\/td>Hongyan Zhao and Yonghao Li<\/td>\u57fa\u4e8e\u591a\u5c3a\u5ea6\u9605\u8bfb\u7406\u89e3\u7684\u6587\u6863\u7ea7\u4e8b\u4ef6\u62bd\u53d6<\/td><\/tr>
27<\/td>Yiyan Wang, Hai-Rong Wang, Tong Wang, Yimeng Wang and Boxiang Jing<\/td>\u591a\u6a21\u6001\u5b9e\u4f53\u5bf9\u9f50\u65b9\u6cd5\u7814\u7a76\u8fdb\u5c55<\/td><\/tr>
28<\/td>Li Jianhong, Bai Yu and Li Jun<\/td>\u57fa\u4e8e\u63d0\u793a\u8bcd\u4e0e\u4ea4\u4e92\u4fe1\u606f\u589e\u5f3a\u7684\u6570\u503c\u4fe1\u606f\u62bd\u53d6\u6a21\u578b<\/td><\/tr>
29<\/td>Yongjie Wang, Wei Luo, Minghao Hu, Xiantao Xu and Fang Wang<\/td>\u9762\u5411\u56fd\u9632\u79d1\u6280\u9886\u57df\u7684\u77e5\u8bc6\u6269\u5c55\u589e\u5f3a\u5c11\u6837\u672c\u534f\u540c\u5206\u7c7b<\/td><\/tr>
30<\/td>Yunyang Cao, Yanjun Li, Silong Dai, Bo Jin, Yuanbin Wu and Jun Zhu<\/td>\u9762\u5411\u5de5\u4e1a\u573a\u666f\u7684\u590d\u6742\u5408\u540c\u4fe1\u606f\u62bd\u53d6\u5927\u6a21\u578b<\/td><\/tr>
31<\/td>\u9f9a\u5929\u9686, \u9ad8\u5cf0\uff0c\u987e\u8fdb\u5e7f<\/td>\u57fa\u4e8e\u5173\u7cfb\u5c42\u7ea7\u5206\u7c7b\u7684\u5927\u5c0f\u6a21\u578b\u533b\u5b66\u5173\u7cfb\u62bd\u53d6<\/td><\/tr>
32<\/td>\u65bd\u5723\u6cfd, \u79e6\u5229, \u9c81\u6676\u6676\uff0c\u80e1\u519b<\/td>\u57fa\u4e8e\u89e3\u8026\u4e0e\u7ec4\u5408\u7684\u5927\u6a21\u578b\u77e5\u8bc6\u95ee\u7b54\u65b9\u6cd5<\/td><\/tr>
33<\/td>\u5c24\u73af\u79d1, \u624d\u8ba9\u534e\u5374\uff0c\u8d75\u6d77\u5174<\/td>\u683c\u8428\u5c14\u53f2\u8bd7\u547d\u540d\u5b9e\u4f53\u548c\u5b9e\u4f53\u5173\u7cfb\u6807\u6ce8\u8bed\u6599\u5e93\u6784\u5efa<\/td><\/tr>
34<\/td>\u718a\u73e0\u73e0, \u5218\u4ed5\u946b\uff0c\u66fe\u96ea\u5f3a<\/td>\u878d\u5408\u60c5\u611f\u5206\u5e03\u7684\u591a\u6807\u7b7e
emoji\u9884\u6d4b\u6a21\u578b<\/td><\/tr>
35<\/td>\u5218\u6d77\u5764, \u90ed\u5c11\u8339, \u674e\u4fca\u6750, \u95eb\u667a\u8d85, \u82cf\u96ea\u5cf0\uff0c\u674e\u8339<\/td>CFSP\uff1a\u6c49\u8bed\u6846\u67b6\u8bed\u4e49\u81ea\u52a8\u89e3\u6790\u5de5\u5177\u96c6<\/td><\/tr>
36<\/td>\u5f20\u96bd\u677e and Turdy Tohti<\/td>\u57fa\u4e8e\u81ea\u84b8\u998f\u7684\u4e2d\u6587\u533b\u7597\u547d\u540d\u5b9e\u4f53\u8bc6\u522b<\/td><\/tr>
37<\/td>Shuchun Wu, Huanran Zheng, Xiaoling Wang and Junyu Niu<\/td>\u57fa\u4e8e\u68c0\u7d22\u589e\u5f3a\u7684\u5e02\u6c11\u4fe1\u7bb1\u667a\u80fd\u6d3e\u53d1\u7b97\u6cd5<\/td><\/tr>
38<\/td>\u9a6c\u548f\u6885, \u803f\u751f\u73b2, \u8d75\u7ef4\u7eb3, \u9a6c\u9f99\u9f99\uff0c\u5b89\u6ce2<\/td>\u57fa\u4e8eLoRA\u5fae\u8c03\u7684\u6492\u62c9\u65cf\u5efa\u7b51\u77e5\u8bc6\u56fe\u8c31\u6784\u5efa<\/td><\/tr>
39<\/td>\u738b\u660c\u9f99, \u9ad8\u6e90\uff0c\u6851\u79c0\u5a1f<\/td>\u57fa\u4e8e\u672c\u4f53\u7684\u7b80\u724d\u6570\u636e\u8bbf\u95ee\u65b9\u6cd5\u7814\u7a76<\/td><\/tr>
40<\/td>\u674e\u821f, \u6b27\u9633\u7eaf\u840d, \u5218\u6c38\u5f6c, \u4f59\u9896, \u949f\u8fdb\uff0c\u6c5f\u7490<\/td>\u57fa\u4e8e\u591a\u6a21\u6001\u4ea4\u53c9\u6ce8\u610f\u529b\u7684\u963f\u5c14\u8328\u6d77\u9ed8\u75c7\u8f85\u52a9\u8bca\u65ad\u7814\u7a76<\/td><\/tr>
41<\/td>Jiajing Li, Yuanyuan Dai, Wang Jia, Renyi Guo and Xuliang Chen<\/td>\u57fa\u4e8e\u5c40\u90e8\u7279\u5f81\u7b5b\u9009\u548c\u80f6\u56ca\u7f51\u7edc\u7684\u6848\u60c5\u8981\u7d20\u8bc6\u522b\u65b9\u6cd5<\/td><\/tr>
42<\/td>Shuyang Wang, Changhua Lu, Jianwei Zhang and Xu Chen<\/td>\u57fa\u4e8e\u81ea\u9002\u5e94\u63d0\u5347\u5c0f\u6ce2\u53d8\u6362\u7684\u957f\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b<\/td><\/tr>
43<\/td>Chao Zhang, Tiexin Wang, Jiaqi Song and Hengxin Zhang<\/td>\u5b9e\u4f53\u591a\u6a21\u6001\u7279\u5f81\u589e\u5f3a\u7684\u65e0\u76d1\u7763\u5b9e\u4f53\u5bf9\u9f50\u7814\u7a76<\/td><\/tr>
44<\/td>\u5218\u6c6a\u6d0b, \u5f20\u7acb\u707f, \u5f20\u4f73\u5e05, \u5ed6\u4f1f, \u8c2d\u654f\uff0c\u6c88\u5b9c<\/td>\u57fa\u4e8e\u56fd\u4ea7\u56fe\u6570\u636e\u5e93\u7684\u4ebf\u7ea7\u6570\u636e\u9ad8\u6548\u5bfc\u5165\u4e0e\u7d22\u5f15\u5173\u8054\u6280\u672f<\/td><\/tr>
45<\/td>Yuan Wang and Wuying Liu<\/td>\u96be\u8d1f\u6837\u672c\u589e\u5f3a\u7684\u5bf9\u6bd4\u77e5\u8bc6\u56fe\u8c31\u8865\u5168<\/td><\/tr>
46<\/td>\u6768\u632f\u4e1a, \u738b\u6d77\u8363, \u738b\u827a\u7131\uff0c\u6613\u4e4b\u822a<\/td>\u57fa\u4e8e\u90e8\u5206\u54c8\u5e0c\u7684\u591a\u6a21\u6001\u77e5\u8bc6\u68c0\u7d22\u65b9\u6cd5\u7814\u7a76\u8fdb\u5c55<\/td><\/tr>
47<\/td>\u9648\u89c2\u6797, \u5b54\u632f\u534e, \u6c6a\u4fca\u9716\uff0c\u7fc1\u6587\u52c7<\/td>\u5371\u9669\u5316\u5b66\u54c1\u4e8b\u6545\u7684\u4e8b\u7406\u56fe\u8c31\u6784\u5efa\u65b9\u6cd5\u7814\u7a76<\/td><\/tr>
48<\/td>\u5411\u541b, \u59da\u6d2a\u78ca, \u53f8\u7fa4, \u6768\u8f76\u6770\uff0c\u96f7\u4e1d\u8426<\/td>\u57fa\u4e8e\u77e5\u8bc6\u56fe\u8c31\u7684\u7f51\u7edc\u5b89\u5168\u5e94\u6025\u5904\u7f6e\u6784\u5efa\u53ca\u5e94\u7528\u7814\u7a76<\/td><\/tr>
49<\/td>\u738b\u5f64 , \u738b\u6d77\u8363 , \u738b\u827a\u7131 and \u9648\u82b3\u840d<\/td>\u591a\u6a21\u6001\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\u65b9\u6cd5\u7814\u7a76\u8fdb\u5c55<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Poster Papers<\/h2>\n\n\n\n
1<\/td>Chen Jingyao, Xia Shuqi, Li Jinghua and Yu Tong<\/td>Enhancing TCM Information Extraction using Instruction-Tuned Large Models<\/td><\/tr>
2<\/td>Zhao Zhuo, Rongzhen Li, Kai Liu, Huhai Zou, Kaimao Li, Jie Yu, Tianhao Sun and Qingbo Wu<\/td>KAOS: Large Model Multi-Agent Operating System<\/td><\/tr>
3<\/td>Heyi Zhang, Xin Wang, Zhaopeng Meng, Zhe Chen, Pengwei Zhuang, Dawei Xu, Yongzhe Jia and Wenbin Guo<\/td>Integrating Large Language Models with Knowledge Graphs in Traditional Chinese Medicine Consultation: A Case Study<\/td><\/tr>
4<\/td>Kozaki Kouji, Shusaku Egami and Ken Fukuda<\/td>Local Index File-based Tool for Extracting Class Hierarchies from Wikidata<\/td><\/tr>
5<\/td>Mengru Wang, Ziwen Xu and Ningyu Zhang<\/td>\u57fa\u4e8e\u77e5\u8bc6\u7f16\u8f91\u7684\u5927\u6a21\u578b\u5b89\u5168\u9632\u5fa1<\/td><\/tr>
6<\/td>Xiang Chen, Chenxi Wang and Ningyu Zhang<\/td>\u9762\u5411\u591a\u6a21\u6001\u5927\u8bed\u8a00\u6a21\u578b\u7684\u7edf\u4e00\u5e7b\u89c9\u68c0\u6d4b<\/td><\/tr>
7<\/td>Lei Zhang and Chunyan An<\/td>\u5976\u4e1a\u91d1\u878d\u98ce\u9669\u9884\u8b66\u4e8b\u4ef6\u56fe\u8c31\u6784\u5efa\u65b9\u6cd5\u7814\u7a76<\/td><\/tr>
8<\/td>Rui Yang, Yi Zhou, Jiahao Zhu, Jianping Man, Li Fang and Hongze Liu<\/td>\u57fa\u4e8e\u5927\u8bed\u8a00\u6a21\u578b\u7684\u901a\u7528\u751f\u6210\u5f0f\u77e5\u8bc6\u56fe\u8c31\u8865\u5168\u7814\u7a76<\/td><\/tr>
9<\/td>Hanming Li, Jifan Yu, Ruimiao Li, Zhanxin Hao, Xuan Yan, Jiaxin Yuan, Bin Xu, Juanzi Li and Zhiyuan Liu<\/td>LM-Interview\uff1a\u901a\u8fc7\u77e5\u8bc6\u5f15\u5bfc\u7684\u8bed\u8a00\u6a21\u578b\u5f00\u53d1\u7684\u667a\u80fd\u8bbf\u8c08\u7cfb\u7edf<\/td><\/tr>
10<\/td>Jiaye Li, Yuan Meng, Lijun Wang, Tianhao Qian, Songlin Zhai and Guilin Qi<\/td>Taxonomy Induction Using LLMs An Enhanced Framework by Integrating Doubly-Checked Mechanism and Self-Evaluation Strategy<\/td><\/tr>
11<\/td>Bohan Yu, Dezhi Kong, Tao Liu, Aoze Zheng, Kunli Zhang and Hongying Zan<\/td>\u57fa\u4e8e\u5927\u8bed\u8a00\u6a21\u578b\u589e\u5f3a\u7684\u4e2d\u533b\u77e5\u8bc6\u7406\u89e3\u4e0e\u63a8\u7406\u7814\u7a76<\/td><\/tr>
12<\/td>\u5f20\u4eae, \u6768\u5fd7\u6770, \u7ae5\u82e5\u73c8, \u9ec4\u5a77, \u5f20\u9e4f\u4e3e, \u9648\u7aef\u5175 and \u9648\u7691<\/td>\u57fa\u4e8e\u591a\u7ef4\u878d\u5408\u5927\u6a21\u578b\u9a71\u52a8\u7684\u77e5\u8bc6\u56fe\u8c31\u95ee\u7b54\u89e3\u51b3\u65b9\u6848<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Demo Papers<\/h2>\n\n\n\n
1<\/td>Yihan Xu, Tong Yu and Jinghua Li<\/td>Development of an Intelligent Chinese Medicine Q&A System Based on Traditional Chinese Medicine Knowledge Graph and Large Language Models<\/td><\/tr>
2<\/td>Yu Qin and Enbo Jiang<\/td>A study on the Metadata System and the Construction of Knowledge Graph of the Classic of Mountains and River — Taking the Classic of the Southern Mountains as an Example<\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"

Conference Papers 1 Yuhao Dan, Zhikai Lei, Yiyang Gu, Yong Li, Jianghao Yin, Jiaju Lin, Linhao Ye, Zhiyan Tie, Yougen Zhou, Yilei Wang, Aimin Zhou, Ze Zhou, Qin Chen, Jie Zhou, Liang He and Xipeng Qiu EduChat: A Large Language Model Based Conversational Agent for Intelligent Education 2 Wei Tang, Zexin Wang, Xun Mao, Kai […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"full-width-page-template.php","meta":[],"_links":{"self":[{"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/en\/wp-json\/wp\/v2\/pages\/289"}],"collection":[{"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/en\/wp-json\/wp\/v2\/comments?post=289"}],"version-history":[{"count":4,"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/en\/wp-json\/wp\/v2\/pages\/289\/revisions"}],"predecessor-version":[{"id":309,"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/en\/wp-json\/wp\/v2\/pages\/289\/revisions\/309"}],"wp:attachment":[{"href":"https:\/\/sigkg.cn\/ccks-ijckg2024\/en\/wp-json\/wp\/v2\/media?parent=289"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}