Tree Languages Generated by Context-Free Graph Grammars

Author(s):  
Joost Engelfriet ◽  
Sebastian Maneth
1978 ◽  
Vol 37 (2) ◽  
pp. 207-233 ◽  
Author(s):  
Pierluigi Della Vigna ◽  
Carlo Ghezzi

2004 ◽  
Vol 69 (3) ◽  
pp. 617-640 ◽  
Author(s):  
E. Fischer ◽  
J. A. Makowsky

Abstract.We show that the spectrum of a sentence ϕ in Counting Monadic Second Order Logic (CMSOL) using one binary relation symbol and finitely many unary relation symbols, is ultimately periodic, provided all the models of ϕ are of clique width at most k, for some fixed k. We prove a similar statement for arbitrary finite relational vocabularies τ and a variant of clique width for τ-structures. This includes the cases where the models of ϕ are of tree width at most k. For the case of bounded tree-width, the ultimate periodicity is even proved for Guarded Second Order Logic GSOL. We also generalize this result to many-sorted spectra, which can be viewed as an analogue of Parikh's Theorem on context-free languages, and its analogues for context-free graph grammars due to Habel and Courcelle.Our work was inspired by Gurevich and Shelah (2003), who showed ultimate periodicity of the spectrum for sentences of Monadic Second Order Logic where only finitely many unary predicates and one unary function are allowed. This restriction implies that the models are all of tree width at most 2, and hence it follows from our result.


1994 ◽  
Vol 31 (4) ◽  
pp. 341-378 ◽  
Author(s):  
Joost Engelfriet ◽  
Linda Heyker ◽  
George Leih

1997 ◽  
Vol 34 (10) ◽  
pp. 773-803 ◽  
Author(s):  
Joost Engelfriet ◽  
Jan Joris Vereijken

2004 ◽  
Vol 13 (01) ◽  
pp. 65-79 ◽  
Author(s):  
ISTVAN JONYER ◽  
LAWRENCE B. HOLDER ◽  
DIANE J. COOK

We present an algorithm for the inference of context-free graph grammars from examples. The algorithm builds on an earlier system for frequent substructure discovery, and is biased toward grammars that minimize description length. Grammar features include recursion, variables and relationships. We present an illustrative example, demonstrate the algorithm's ability to learn in the presence of noise, and show real-world examples.


1972 ◽  
Vol 19 (1) ◽  
pp. 11-22 ◽  
Author(s):  
T. Pavlidis

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