The document explores the concept of combining multiple ontology matchers using anomaly detection as an unsupervised method for aggregation. It discusses the rationale behind identifying outliers among matching scores, highlights a full pipeline approach that includes running different matchers, dimensionality reduction, and outlier detection, and presents performance results on various datasets. The authors conclude that anomaly detection is effective for matcher aggregation while also noting future work needed to address scalability issues.