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Document Spanners for Extracting Incomplete Information: Expressiveness and Complexity

Summary: Extends document spanners with mappings to extract incomplete or optional information, simplifying regex-formula and rule semantics. Compares their expressive power and analyzes enumeration, satisfiability, and containment complexity. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
1747
Venue
PODS
Year
2018
Pagerank
8.7135317e-05
Overall Rank
2,348 | 83.90%
DOI
10.1145/3196959.3196968

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{maturana_pods18,
        address = {New York, NY, USA},
        series = {{PODS} '18},
        title = {{Document Spanners for Extracting Incomplete Information: Expressiveness and Complexity}},
        url = {https://dl.acm.org/doi/10.1145/3196959.3196968},
        doi = {10.1145/3196959.3196968},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Maturana, Francisco and Riveros, Cristian and Vrgoč, Domagoj},
        year = {2018}
}

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