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
Event Time Extraction and Propagation via Graph Attention Networks
TL;DR: This paper first formulates this problem based on a 4-tuple temporal representation used in entity slot filling, which allows us to represent fuzzy time spans more conveniently, and proposes a graph attention network-based approach to propagate temporal information over document-level event graphs constructed by shared entity arguments and temporal relations.
Proceedings Article
DataScope: viewing database contents in Google Maps' way
TL;DR: DataScope is developed, a Web-based data content visualization system, for people to view the desired data easily, interactively, and at multi-resolution.
Proceedings ArticleDOI
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Xin Luna Dong,Xiang He,Andrey Kan,Xian Li,Yan Liang,Jun Ma,Yifan Ethan Xu,Chenwei Zhang,Tong Zhao,Gabriel Blanco Saldana,Saurabh Deshpande,Alexandre Michetti Manduca,Jay Ren,Surender Pal Singh,Fan Xiao,Haw-Shiuan Chang,Giannis Karamanolakis,Yuning Mao,Yaqing Wang,Christos Faloutsos,Andrew McCallum,Jiawei Han +21 more
TL;DR: AutoKnow as discussed by the authors is a self-driving system that collects product knowledge for over 11k product types and collects customer behavior logs for taxonomy construction, product property identification, knowledge extraction, anomaly detection, and synonym discovery.
Proceedings ArticleDOI
Octet: Online Catalog Taxonomy Enrichment with Self-Supervision
TL;DR: In this paper, a self-supervised end-to-end framework, Octet, for Online Catalog Taxonomy EnrichmenT, leverages heterogeneous information unique to online catalog taxonomies such as user queries, items, and their relations to the taxonomy nodes.
Book ChapterDOI
AIM: Approximate Intelligent Matching for Time Series Data
TL;DR: This paper introduces a new problem, the approximate partial matching of a query sequence in a time series database and investigates an intelligent subsequence similarity matching of time series queries based on efficient graph traversal.