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Linking Temporal Records for Profiling Entities

Summary: Temporal record linkage for profiling evolving entities. A novel transition model of attribute values, together with source freshness, enables a source-aware temporal matcher that links records to the correct time period, yielding richer profiles. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5065
Venue
SIGMOD
Year
2015
Pagerank
5.6605087e-05
Overall Rank
7,265 | 50.16%
DOI
10.1145/2723372.2737789

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{li_sigmod15,
        title = {{Linking Temporal Records for Profiling Entities}},
        author = {Li, Furong and Lee, Mong Li and Hsu, Wynne and Tan, Wang-Chiew},
        series = {{SIGMOD} '15},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2723372.2737789},
        url = {https://dl.acm.org/doi/10.1145/2723372.2737789},
        year = {2015}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
4,598 Temporal Rules Discovery for Web Data Cleaning 2016 VLDB 6.6127851e-05
8,105 Online Topic-Aware Entity Resolution Over Incomplete Data Streams 2021 SIGMOD 5.4860105e-05
11,434 Learning and Deducing Temporal Orders 2023 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

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

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