Matching Titles with Cross Title Web-Search Enrichment and Community Detection
Summary: Web-search enrichment makes titles referring to the same entity more similar and non-matches less so. Tokens from the titles form a social-network graph and a strength-of-ties clustering decides if they form one community (match) or separate (mismatch). (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Nikhil Londhe
- 2. Vishrawas Gopalakrishnan
- 3. Aidong Zhang
- 4. Hung Q. Ngo
- 5. Rohini Srihari
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 318 | Evaluation of entity resolution approaches on real-world match problems | 2010 | VLDB | 0.00027850417 |
| 4,956 | Mining Document Collections to Facilitate Accurate Approximate Entity Matching | 2009 | VLDB | 5.8044716e-05 |
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