Datalignment: Ontology Schema Alignment Through Datalog Containment
Summary: Aligns ontology relations by testing bounded-depth Datalog subprogram containment/equivalence over association rules, rather than relying on names. Produces expert-verifiable candidate matches with provenance-based explanations, implemented in Datalignment. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Daniel Deutch (Tel Aviv University)
- 2. Evgeny Marants (Tel Aviv University)
- 3. Yuval Moskovitch (Tel Aviv University)
BibTeX Citation
@article{deutch_vldb19,
title = {{Datalignment: Ontology Schema Alignment Through Datalog Containment}},
author = {Deutch, Daniel and Marants, Evgeny and Moskovitch, Yuval},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {12},
pages = {1870--1873},
doi = {10.14778/3352063.3352087},
url = {https://doi.org/10.14778/3352063.3352087},
year = {2019}
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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 |
|---|---|---|---|---|
| 2,086 | PARIS: Probabilistic Alignment of Relations, Instances, and Schema | 2012 | VLDB | 9.1930537e-05 |
| 6,587 | Selective Provenance for Datalog Programs Using Top-K Queries | 2015 | VLDB | 5.8333865e-05 |
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