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71 26-三月-2024 10:14 3 KB 程龚 前一版本 | 最新版本
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*__Office__: Room 309, Building of Computer Science Technology, Nanjing University Xianlin Campus, 163 Xianlin Avenue, Nanjing 210023, China
*__Office__: Room 309, Building of Computer Science Technology, Nanjing University Xianlin Campus, 163 Xianlin Avenue, 210023 Nanjing, China
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Gong Cheng is a professor at the Department of Computer Science and Technology, Nanjing University. He received his PhD from Southeast University. His research interests include __knowledge graph__, __big data search__, and __neuro-symbolic computation__ with applications to __knowledge-based hybrid intelligent systems__ such as data search engines, decision support systems, and AIOps platforms. His research has been published in journals and at conferences including TKDE, WWW, SIGIR, AAAI, IJCAI, ACL, ISWC, and has received or been nominated for six best paper awards. He was a Posters & Demos co-chair of ISWC 2019.
Gong Cheng is a professor at the Department of Computer Science and Technology, Nanjing University. His research interests include __big data search__, __knowledge graph__, and __neuro-symbolic reasoning__ with applications to data search engines, knowledge computing platforms, question answering systems, etc. His research has been published at conferences and in journals including WWW and TKDE, and has received or been nominated for seven best paper awards from ISWC, ESWC, and COLING. He served as PC co-chairs of ISWC 2024 and CCKS 2023.
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* __data search engine__: a better data search engine, featuring data retrieval, ranking, summarization, browsing, recommendation, etc.;
* __knowledge graph search__: search algorithms for large knowledge graphs, e.g., efficient approximation algorithms for Steiner Tree problems;
* __complex question answering__: neuro-symbolic methods for question answering with applications to complex tasks such as high-school exams.
* __data search engines__: more effective data search engines, featuring data fusion, retrieval, ranking, summarization, recommendation, etc.; [[ [SIGIR 2024|https://doi.org/10.1145/3626772.3657837] | [ISWC 2023|https://doi.org/10.1007/978-3-031-47240-4_2] | [TKDE 2023|https://doi.org/10.1109/TKDE.2021.3095309] ]
* __knowledge graph search__: search algorithms for large knowledge graphs, e.g., efficient approximation algorithms for Steiner Tree problems; [[ WWW2024 | [WWW 2023|https://doi.org/10.1145/3543507.3583257] | [WWW 2021|https://doi.org/10.1145/3442381.3449900] | [WWW 2020|https://doi.org/10.1145/3366423.3380110] ]
* __large language model and reasoning__: fusion of neural large language models with symbolic reasoning over knowledge bases for solving complex problems. [[ [AAAI 2023|https://doi.org/10.1609/aaai.v37i11.26543] | [ACL 2022|https://doi.org/10.18653/v1/2022.acl-long.494] | [AAAI 2021|https://doi.org/10.1609/aaai.v35i15.17570]]
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*ISWC 2023 Best Research Paper Nominee
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*In-Use Track co-chair of ISWC 2024
*Proceedings & Metadata co-chair of ISWC 2023
*Publicity co-chair of CCF BigData 2023
*PC co-chair of CCKS 2023
*Top Conference Review chair of CCKS 2020
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*Top Conference Review co-chair of CCKS 2017
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*Top conference review (co-)chair of CCKS 2017, 2020
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*ISWC 2022
*CIKM 2020-2022
*ISWC 2022-2024
*ESWC 2023
*ACL 2023
*CIKM 2020-2023
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*WWW 2017-2023
*AAAI 2021-2023
*IJCAI 2022
*WWW 2017-2024
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*ESWC 2013-2014, 2017, 2019-2021
*ESWC 2013-2014, 2017, 2019-2022, 2024
*SIGIR 2023-2024
*AAAI 2021-2024
*IJCAI 2022
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*COLING 2022
*COLING 2022-2024
*DASFAA 2023-2024
*IEEE BigData 2024
*ADMA 2023-2024
!Associate editor of
*[Transactions on Graph Data and Knowledge|https://tgdk.org/]
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*Journal of Web Semantics (Special Issue on Knowledge Graphs and Information Retrieval)
*[Journal of Web Semantics (Special Issue on Knowledge Graphs and Information Retrieval)|https://www.sciencedirect.com/journal/journal-of-web-semantics/special-issue/10DWQV75Z4T]
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*ACM Transactions on the Web
!!Hobbies
*[Traveling|https://mapsengine.google.com/map/edit?mid=zhNYIVHdeTCM.kUtDkqu0baRc]
*Photography
*Basketball
*[ACM Transactions on the Web|https://dl.acm.org/journal/tweb]