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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 Department of Computer Science and Technology, Nanjing University. He received his PhD from Southeast University. His research interests include __knowledge graph__, __data search__, and __neuro-symbolic reasoning__ with applications to __knowledge-based hybrid intelligent systems__ such as open data portals, question answering 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 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.
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* __open data portal__: a better open data portal, featuring data fusion, retrieval, ranking, summarization, 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 problem solving.
* __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.
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*[My DBLP page|https://dblp.org/pid/69/1215-1.html]
*[My Google Scholar profile|https://scholar.google.com/citations?user=_ncKAiwAAAAJ]
*[DBLP|https://dblp.org/pid/69/1215-1.html]
*[Google Scholar|https://scholar.google.com/citations?user=_ncKAiwAAAAJ]
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*CIKM 2021 Best Reviewer Award
*ISWC 2023 Best Research Paper Nominee
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*Posters & Demos co-chair of ISWC 2019
*In-Use Track co-chair of ISWC 2024
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*Publicity co-chair of CCF BigData 2023
*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
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*Top conference review (co-)chair of CCKS 2017, 2020
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*ISWC 2022
*ISWC 2022-2024
*CIKM 2020-2023
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*CIKM 2020-2022
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*ACL 2023
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*WWW 2017-2023
*WWW 2017-2025
*SIGIR 2023-2024
*WSDM 2025
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*ESWC 2013-2014, 2017, 2019-2022
*AAAI 2021-2023
*ESWC 2013-2014, 2017, 2019-2022, 2024
*AAAI 2021-2025
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*ACL 2023
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*COLING 2022
*SIGIR 2023
*DASFAA 2023
*COLING 2022-2025
*ICLR 2025
*KDD 2025
*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
*[ACM Transactions on the Web|https://dl.acm.org/journal/tweb]
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