# Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity

arXiv: https://arxiv.org/abs/2404.02126 · HTML: https://mlsanity.com/paper/2404.02126/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering](https://mlsanity.com/paper/2104.06378/) (2021)
- [Graph Pre-training for AMR Parsing and Generation](https://mlsanity.com/paper/2203.07836/) (2022)
- [Toward Abstractive Summarization Using Semantic Representations](https://mlsanity.com/paper/1805.10399/) (2018)
- [FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations](https://mlsanity.com/paper/2204.06508/) (2022)
- [Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs](https://mlsanity.com/paper/1910.08435/) (2019)
- [Maximum Bayes Smatch Ensemble Distillation for AMR Parsing](https://mlsanity.com/paper/2112.07790/) (2021)
- [Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation](https://mlsanity.com/paper/1808.09160/) (2018)
- [AMR Similarity Metrics from Principles](https://mlsanity.com/paper/2001.10929/) (2020)
- [Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity](https://mlsanity.com/paper/2108.11949/) (2021)
- [SemBleu: A Robust Metric for AMR Parsing Evaluation](https://mlsanity.com/paper/1905.10726/) (2019)
- [DocAMR: Multi-Sentence AMR Representation and Evaluation](https://mlsanity.com/paper/2112.08513/) (2021)
- [Inducing and Using Alignments for Transition-based AMR Parsing](https://mlsanity.com/paper/2205.01464/) (2022)
- [Enumerating consistent subgraphs of directed acyclic graphs: an insight into biomedical ontologies](https://mlsanity.com/paper/1712.09679/) (2017)

## Built on by (most cited first, 1 of 1)

- [Advances and Challenges in Semantic Textual Similarity: A Comprehensive Survey](https://mlsanity.com/paper/2601.03270/) (2025)

## Abstract (arXiv)

Knowledge graphs play a pivotal role in various applications, such as question-answering and fact-checking. Abstract Meaning Representation (AMR) represents text as knowledge graphs. Evaluating the quality of these graphs involves matching them structurally to each other and semantically to the source text. Existing AMR metrics are inefficient and struggle to capture semantic similarity. We also lack a systematic evaluation benchmark for assessing structural similarity between AMR graphs. To overcome these limitations, we introduce a novel AMR similarity metric, rematch, alongside a new evaluation for structural similarity called RARE. Among state-of-the-art metrics, rematch ranks second in structural similarity; and first in semantic similarity by 1--5 percentage points on the STS-B and SICK-R benchmarks. Rematch is also five times faster than the next most efficient metric.