J
Jiawei Han
Researcher at University of Illinois at Urbana–Champaign
Publications - 1302
Citations - 155054
Jiawei Han is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Cluster analysis & Knowledge extraction. The author has an hindex of 168, co-authored 1233 publications receiving 143427 citations. Previous affiliations of Jiawei Han include Georgia Institute of Technology & United States Army Research Laboratory.
Papers
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Proceedings ArticleDOI
Normalization of linear recursions in deductive databases
Jiawei Han,K. Zeng,T. Lu +2 more
TL;DR: A graph-matrix expansion-based compilation technique that transforms complex linear recursions into highly regular linear normal forms (LNFs) is introduced, which facilitates the development of powerful query analysis and evaluation techniques for complexlinear recursions in deductive databases.
Proceedings ArticleDOI
Assured Information Sharing Life Cycle
Tim Finin,Anupam Joshi,Hillol Kargupta,Yelena Yesha,Joel Sachs,Elisa Bertino,Ninghui Li,Chris Clifton,Gene Spafford,Bhavani Thuraisingham,Murat Kantarcioglu,Alain Bensoussan,Nathan Berg,Latifur Khan,Jiawei Han,ChengXiang Zhai,Ravi Sandhu,Shouhuai Xu,Jim Massaro,Lada A. Adamic +19 more
TL;DR: The main objective of the project is to define, design and develop an Assured Information Sharing Lifecycle (AISL) that realizes the DoD's information sharing value chain.
Posted ContentDOI
Deep functional synthesis: a machine learning approach to gene functional enrichment
Sheng Wang,Jianzhu Ma,Samson Fong,Stefano E. Rensi,Jiawei Han,Jian Peng,Dexter Pratt,Russ B. Altman,Trey Ideker +8 more
TL;DR: This work presents an alternative machine learning approach, Deep Functional Synthesis (DeepSyn), which moves beyond gene function databases to dynamically infer the functions of a gene set from its associated network of literature and data, conditioned on the disease and drug context of the current experiment.
Proceedings ArticleDOI
Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach
TL;DR: Data-driven methods to recognize typed entities of interest in massive, domain-specific text corpora are introduced and demonstrated on real datasets including news articles and tweets how these typed entities aid in knowledge discovery and management.
Posted Content
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training
TL;DR: This paper propose a self-training method that uses contextualized augmentations created by pre-trained language models to improve the generalization ability of the NER model, which achieves superior performance on three benchmark datasets.