基于压缩不相交子图的非融合NLC文法推理
Non-Confluent NLC Graph Grammar Inference by Compressing Disjoint Subgraphs
摘要：
语法推理涉及确定（最好是简单的）与一组观察一致的模型/语法。在形式语言理论中有大量关于语法推理的研究。然而，令人惊讶的是，很少有人知道的文法推理的图文法。在本文中，我们在这个方向上采取了进一步的措施，并在节点标签控制（NLC）图语法的框架内工作。具体地说，我们的特点，给定一个图$G$的一组不相交和同构的子图，是否有一个NLC图的语法规则，可以产生这些子图，以获得$G$。通过假设同构子图的集合是不相交的而不是不接触的，这推广了以前的结果。这自然导致考虑更多涉及的“非融合”图形语法规则。
英文摘要：
Grammar inference deals with determining (preferable simple) models/grammars consistent with a set of observations. There is a large body of research on grammar inference within the theory of formal languages. However, there is surprisingly little known on grammar inference for graph grammars. In this paper we take a further step in this direction and work within the framework of node label controlled (NLC) graph grammars. Specifically, we characterize, given a set of disjoint and isomorphic subgraphs of a graph $G$, whether or not there is a NLC graph grammar rule which can generate these subgraphs to obtain $G$. This generalizes previous results by assuming that the set of isomorphic subgraphs is disjoint instead of non-touching. This leads naturally to consider the more involved ``non-confluent'' graph grammar rules.
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