A Learning Algorithm for Top-Down XML Transformations
Summary: Generalizes regular-language learnability to deterministic top-down tree transducers (dtops): proves a Myhill–Nerode theorem for dtops and gives a polynomial-size sample learning algorithm that handles copying, deletion, and permutation. Presents a DTD-based encoding of unranked XML trees so dtops can realize many practical XSLT-like transformations beyond first-child/next-sibling encodings. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 207 | Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach | 2001 | SIGMOD | 0.00034571837 |
| 480 | Translating Web Data | 2002 | VLDB | 0.00022166928 |
| 3,814 | XML Type Checking with Macro Tree Transducers | 2005 | PODS | 6.7315557e-05 |
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