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[{Image src='gcheng.jpeg' height='200px'}]
[{Image src='gcheng.jpg' height='200px'}]
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Gong Cheng is a professor at the School of Computer Science, Nanjing University. His research interests include __big data search__, __knowledge graph__, and __LLM inference__ with applications to search engines, decision support systems, question answering systems, etc. His research has been published at academic conferences and in journals including WWW, SIGIR, 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.
Gong Cheng is a professor at the School of Computer Science, Nanjing University. His research interests include __big data retrieval__, __knowledge graph__, and __LLM inference__ with applications to search engines, decision support systems, question answering systems, etc. His research has been published at academic conferences and in journals including WWW, SIGIR, 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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* __dataset search__: search engines for open datasets, featuring dataset fusion, retrieval, ranking, summarization, recommendation, etc.;\\[[ [SIGIR 2025|https://doi.org/10.1145/3726302.3730120] | [SIGIR 2024|https://doi.org/10.1145/3626772.3657837] | [SIGIR 2024|https://doi.org/10.1145/3626772.3657866] | [TKDE 2023|https://doi.org/10.1109/TKDE.2021.3095309] ]
* __graph search algorithms__: search algorithms for large graph data, e.g., efficient approximation algorithms for Group Steiner Tree problems;\\[[ [WWW 2024|https://doi.org/10.1145/3589334.3645325] | [WWW 2023|https://doi.org/10.1145/3543507.3583257] | [WWW 2023|https://doi.org/10.1145/3543507.3583429] ]
* __LLM inference__: optimization of LLM inference for specific tasks and solving complex problems in critical domains;\\[[ [NeurIPS 2024|http://papers.nips.cc/paper_files/paper/2024/hash/3bf80b34f731313b8292f4578e820c90-Abstract-Conference.html] | [ICLR 2024|https://openreview.net/pdf?id=riNuqYiD66] | [AAAI 2023|https://doi.org/10.1609/aaai.v37i11.26543] ]
* __knowledge-based RAG__: retrieval of multi-source heterogeneous knowledge, e.g., knowledge graphs and tables, for enhancing the generation of LLMs.\\[[ ISWC 2025 ]
* __dataset retrieval__: search engines for open datasets, featuring dataset fusion, retrieval, ranking, summarization, recommendation, etc.;\\[[ [EMNLP 2025|https://doi.org/10.18653/v1/2025.emnlp-main.353] | [SIGIR 2025|https://doi.org/10.1145/3726302.3730120] | [SIGIR 2024|https://doi.org/10.1145/3626772.3657837] | [SIGIR 2024|https://doi.org/10.1145/3626772.3657866] ]
* __graph search algorithms__: search algorithms for large graph data, e.g., efficient approximation algorithms for Group Steiner Tree problems;\\[[ [WWW 2026|https://doi.org/10.1145/3774904.3792210] | [WWW 2024|https://doi.org/10.1145/3589334.3645325] | [WWW 2023|https://doi.org/10.1145/3543507.3583257] | [WWW 2023|https://doi.org/10.1145/3543507.3583429] ]
* __LLM inference__: optimization of LLM inference for specific tasks and solving complex problems in critical domains;\\[[ ICLR 2026 | [KAIS 2025|https://doi.org/10.1007/s10115-024-02216-1] | [NeurIPS 2024|http://papers.nips.cc/paper_files/paper/2024/hash/3bf80b34f731313b8292f4578e820c90-Abstract-Conference.html] | [ICLR 2024|https://openreview.net/pdf?id=riNuqYiD66] ]
* __knowledge-based RAG__: retrieval of multi-source heterogeneous knowledge, e.g., knowledge graphs and tables, for enhancing the generation of LLMs.\\[[ [ISWC 2025|https://doi.org/10.1007/978-3-032-09530-5_1] ]
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*Associate PC chair of CIKM 2025
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*CIKM 2020-2024
*ESWC 2023, 2025
*CIKM 2020-2025
*ESWC 2023, 2025-2026
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*AAAI 2026
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*AAAI 2026
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*SIGIR 2023-2025
*WSDM 2025
*SIGIR 2023-2026
*WSDM 2025-2026
*ECIR 2026
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*AAAI 2021-2025
*IJCAI 2022, 2025
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*AAAI 2021-2025
*IJCAI 2022, 2025
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*ICLR 2025
*KDD 2025
*ICDM 2025
*DASFAA 2023-2025
*IEEE BigData 2024-2025
*ICLR 2025-2026