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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 __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.
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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* __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|https://doi.org/10.1145/3589334.3645325] | [WWW 2023|https://doi.org/10.1145/3543507.3583257] | [WWW 2021|https://doi.org/10.1145/3442381.3449900] ]
* __large language model and reasoning__: fusion of neural large language models with symbolic reasoning over knowledge bases for solving complex problems. [[ [ICLR 2024|https://openreview.net/pdf?id=riNuqYiD66] | [AAAI 2023|https://doi.org/10.1609/aaai.v37i11.26543] | [ACL 2022|https://doi.org/10.18653/v1/2022.acl-long.494] ]
* __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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