Abstract
Contextual hyperedge replacement (CHR) strengthens the generative power of hyperedge replacement (HR) significantly, thus increasing its usefulness for practical modeling. We define top-down parsing for CHR grammars by graph transformation, and prove that it is correct as long as the generation and use of context nodes in productions does not create cyclic dependencies. An efficient predictive version of this algorithm can be obtained as in the case of HR grammars.
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Notes
- 1.
Note that, while t is a derivation tree over the borrowing HR grammar \({\hat{\varGamma }}\), the ordering of right-hand sides of productions in \({\hat{\varGamma }}\) ignores edges that are labeled with
and \(\circledcirc \), so that it is the same as in \(\varGamma \) and provides the required edge ordering. - 2.
The mentioned CHR grammars can be downloaded at www.unibw.de/inf2/grappa. The graph parser distiller grappa developed by Mark Minas, generates PTD parsers that run in quadratic time, and often even in linear time [6]. The website also contains specifications of the PTD parser for linked trees with the AGG system [11] along the lines of Example 5.
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Acknowledgments
We thank Annegret Habel, Verone Stillger, and the anonymous reviewers for their advice.
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Drewes, F., Hoffmann, B., Minas, M. (2021). Rule-Based Top-Down Parsing for Acyclic Contextual Hyperedge Replacement Grammars. In: Gadducci, F., Kehrer, T. (eds) Graph Transformation. ICGT 2021. Lecture Notes in Computer Science(), vol 12741. Springer, Cham. https://doi.org/10.1007/978-3-030-78946-6_9
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