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)

Paper ID: 10964
Venue: VLDB
Year: 2014
Pagerank: 5.093636e-05
Overall Rank: 12,180 | 16.44%
DOI: 10.14778/2732977.2732990

Incoming Non-self Citations Over Time

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Authors

1. Nikhil Londhe (State University of New York at Buffalo)
2. Vishrawas Gopalakrishnan (State University of New York at Buffalo)
3. Aidong Zhang (State University of New York at Buffalo)
4. Hung Q. Ngo (State University of New York at Buffalo)
5. Rohini Srihari (State University of New York at Buffalo)

BibTeX Citation

@article{londhe_vldb14,
        title = {{Matching Titles with Cross Title Web-Search Enrichment and Community Detection}},
        author = {Londhe, Nikhil and Gopalakrishnan, Vishrawas and Zhang, Aidong and Ngo, Hung Q. and Srihari, Rohini},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {12},
        pages = {1167--1180},
        doi = {10.14778/2732977.2732990},
        url = {https://doi.org/10.14778/2732977.2732990},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
248	Evaluation of entity resolution approaches on real-world match problems	2010	VLDB	0.00023278354
5,279	Mining Document Collections to Facilitate Accurate Approximate Entity Matching	2009	VLDB	6.2849137e-05

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