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Gong Cheng
Gong Cheng

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*__Twitter__: [Gong Cheng|https://twitter.com/gong_cheng]
*__Twitter__: [gong_cheng|https://twitter.com/gong_cheng]
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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 __neuro-symbolic reasoning__ with applications to data search engines, question answering systems, etc. His research has been published at 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 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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* __dataset search__: search engines for open datasets, featuring dataset fusion, retrieval, ranking, summarization, recommendation, etc.;\\[[ [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 | [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.
* __data search engines__: more effective data search engines, featuring data fusion, retrieval, ranking, summarization, recommendation, etc.; [[ [ISWC 2023|https://doi.org/10.1007/978-3-031-47240-4_2] | [TKDE 2023|https://doi.org/10.1109/TKDE.2021.3095309] | [SIGIR 2022|https://doi.org/10.1145/3477495.3531729] ]
* __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] ]
* __large language model and question answering__: combination 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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*[DBLP|https://dblp.org/pid/69/1215-1.html]
*[Google Scholar|https://scholar.google.com/citations?user=_ncKAiwAAAAJ]
*[My DBLP page|https://dblp.org/pid/69/1215-1.html]
*[My Google Scholar profile|https://scholar.google.com/citations?user=_ncKAiwAAAAJ]
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*CIKM 2021 Best Reviewer Award
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*ISWC 2022-2024
*CIKM 2020-2023
*ISWC 2022-2023
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*ACL 2023
*CIKM 2020-2023
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*ACL 2023
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*WWW 2017-2025
*SIGIR 2023-2024
*WSDM 2025
*WWW 2017-2024
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*AAAI 2021-2025
*SIGIR 2023-2024
*AAAI 2021-2024
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*COLING 2022-2025
*ICLR 2025
*KDD 2025
*COLING 2022-2024
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*ADMA 2023-2024
*ADMA 2023
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