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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.
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 eight 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 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;\\[[ ICML 2026 | ACL 2026 | ICLR 2026 | [NeurIPS 2024|https://doi.org/10.52202/079017-1076] | [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] ]
* __data retrieval and RAG__: multi-source data retrieval, including dataset retrieval, data selection, knowledge graph RAG, etc.;\\[[ [SIGIR 2026|https://doi.org/10.1145/3805712.3808588] | [EMNLP 2025|https://doi.org/10.18653/v1/2025.emnlp-main.353] | [SIGIR 2025|https://doi.org/10.1145/3726302.3730120] ]
* __graph algorithm design and analysis__: search algorithms for large-scale graph data, e.g., Group Steiner Tree algorithms;\\[[ VLDB 2026 | [WWW 2026|https://doi.org/10.1145/3774904.3792210] | [WWW 2024|https://doi.org/10.1145/3589334.3645325] ]
* __multimodal knowledge reasoning__: multimodal data governance, knowledge extraction and fusion, trustworthy reasoning and decision support.\\[[ ICML 2026 | ACL 2026 | ICLR 2026 ]
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*ESWC 2026 Best Resource Paper Nominee
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*ACL 2023
*ARR 2025-2026
*AAAI 2026
*ACL/ARR 2023, 2025-2026
*AAAI 2026-2027