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Similarity Evaluation on Tree-structured Data

Summary: Tree edit distance is costly; encode trees as approximate numeric vectors; L1 distance lower-bounds tree distance in O(|T1|+|T2|). A filter-and-refine framework uses the embedding to speed up similarity search on large tree-structured datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3744
Venue
SIGMOD
Year
2005
Pagerank
7.8155543e-05
Overall Rank
3,047 | 79.10%
DOI
10.1145/1066157.1066243

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{yang_sigmod05,
        title = {{Similarity Evaluation on Tree-structured Data}},
        author = {Yang, Rui and Kalnis, Panos and Tung, Anthony K. H.},
        series = {{SIGMOD} '05},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1066157.1066243},
        url = {https://dl.acm.org/doi/10.1145/1066157.1066243},
        year = {2005}
}

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