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arXiv · 2203.13226

SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distance

Juri Opitz·Philipp Meier·Anette Frank
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Abstract

The similarity of graph structures, such as Meaning Representations (MRs), is often assessed via structural matching algorithms, such as Smatch (Cai and Knight, 2013). However, Smatch involves a combinatorial problem that suffers from NP-completeness, making large-scale applications, e.g., graph clustering or search, infeasible. To alleviate this issue, we learn SMARAGD: Semantic Match for Accurate and Rapid Approximate Graph Distance. We show the potential of neural networks to approximate Smatch scores, i) in linear time using a machine translation framework to predict alignments, or ii) in constant time using a Siamese CNN to directly predict Smatch scores. We show that the approximation error can be substantially reduced through data augmentation and graph anonymization.

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Juri Opitz, Philipp Meier, Anette Frank. 2022-03-24. SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distance. https://arxiv.org/abs/2203.13226

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