The determination of threshold is a complex and a time consuming task. Existing threshold value recommendation approaches are either not generalizable or requires further improvement in accuracy. In this paper, we propose an approach that computes two properties namely, symmetric and transitive, on the con dence values computed by an ontology matching algorithm in order to recommend the threshold. We demonstrate the e ectiveness of our solution through experiments by comparing our solution with the hierarchical agglomerative clustering.
Martinez-Gil and Aldana-Montes have highlighted the determination of
threshold as a complex and time consuming task [
Convert the set of correspondences which is in many-to-many form into oneto-one form using the linear optimization.
Select the correspondences (of step 2) and lter them with respect to symmetric Copyright c for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
K. Vidhani et al.
Ontology Matching alignment and transitive properties.
Distribute the ltered correspondences (of step3) into a set of -length intervals.
2 [0; 1] is a value chosen by a user.
Choose the top interval's correspondences to determine a threshold value.
We have conducted experiments on the OAEI 2019 conference dataset to compare threshold values recommended by ThValRec with the hierarchical agglomerative clustering (HAC) [2] viz-a-viz three ontology matching algorithms: fastText (v0.9.1), WuPalmer (nltk v3.4.5) and NGram (strsim v0.0.3 of python).
As shown in the table 1, HAC mostly recommends three threshold values, 0.5, 0.8 and 0.9, for the fastText and NGram algorithms across all ontology pairs. In case of WuPalmer, HAC recommends low threshold values viz-a-viz fastText and NGram, and, performs very poorly in comparison to ThValRec approaches. This demonstrates that HAC may not recommend consistent values for di erent ontology matching algorithms.