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.
@article{arxiv.2203.13226,
title = {SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph Distance},
author = {Juri Opitz and Philipp Meier and Anette Frank},
journal= {arXiv preprint arXiv:2203.13226},
year = {2023}
}
Comments
to appear at 15th International Conference on Computational Semantics (IWCS 2023)