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Gong Cheng
Gong Cheng

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Gong Cheng is a professor at the Department of Computer Science and Technology, Nanjing University. His research interests include __knowledge graph__, __information retrieval__, and __neuro-symbolic reasoning__ with applications to knowledge computing platforms, data search engines, question answering systems, etc. His research has been published in journals and at conferences including TKDE and WWW, 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 engines__: more effective data search engines, featuring data fusion, retrieval, ranking, summarization, recommendation, etc.; [[ [TKDE 2023|https://doi.org/10.1109/TKDE.2021.3095309] | [SIGIR 2022|https://doi.org/10.1145/3477495.3531729] | [ISWC 2021|https://doi.org/10.1007/978-3-030-88361-4_1] ]
* __knowledge graph search__: search algorithms for large knowledge graphs, e.g., efficient approximation algorithms for Steiner Tree problems; [[ [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] ]
* __complex question answering__: neuro-symbolic methods for question answering with applications to complex tasks such as problem solving. [[ [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]]
* __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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*Posters & Demos co-chair of ISWC 2019
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*Tutorial co-chair of JIST 2016
*PC co-chair of CCKS 2023
*Top Conference Review (co-)chair of CCKS 2017, 2020
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*PC co-chair of CCKS 2023
*Top Conference Review chair of CCKS 2020
*Posters & Demos co-chair of ISWC 2019
*Top Conference Review co-chair of CCKS 2017
*Tutorial co-chair of JIST 2016
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*ISWC 2022-2023
*ISWC 2022-2024
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*SIGIR 2023
*SIGIR 2023-2024
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*ADMA 2023
*IEEE BigData 2024
*ADMA 2023-2024
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