Rematch: Robust and Efficient Matching of Local Knowledge Graphs to
  Improve Structural and Semantic Similarity
Zoher Kachwala Jisun An Haewoon Kwak Filippo Menczer
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.