Mining Latent Entity Structures from Massive Unstructured and Interconnected Data
Summary: Mining latent entity structures from massive unstructured data to reveal semantic relations and groupings around entities. The tutorial blends DB, data mining, NLP, and IR to extract conceptual, topical, and relational structures, with case studies on papers, news, and social networks. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jiawei Han
- 2. Chi Wang
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 62 | Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge | 2008 | SIGMOD | 0.0006429466 |
| 364 | Annotating and Searching Web Tables Using Entities, Types and Relationships | 2010 | VLDB | 0.00025637562 |
| 420 | InfoGather: Entity Augmentation and Attribute Discovery By Holistic Matching with Web Tables | 2012 | SIGMOD | 0.00023719065 |
| 1,001 | Recovering Semantics of Tables on the Web | 2011 | VLDB | 0.00014706505 |
| 1,066 | Probase: A Probabilistic Taxonomy for Text Understanding | 2012 | SIGMOD | 0.0001433416 |
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