=Paper=
{{Paper
|id=Vol-3063/om2021_poster6
|storemode=property
|title=ThValRec: threshold value recommendation approach for ontology matching
|pdfUrl=https://ceur-ws.org/Vol-3063/om2021_poster6.pdf
|volume=Vol-3063
|authors=Kumar Vidhani,Gurpriya Bhatia,Mangesh Gharote,Sachin Lodha
|dblpUrl=https://dblp.org/rec/conf/semweb/VidhaniBGL21
}}
==ThValRec: threshold value recommendation approach for ontology matching==
https://ceur-ws.org/Vol-3063/om2021_poster6.pdf
ThValRec: Threshold Value Recommendation
Approach for Ontology Matching∗
Kumar Vidhani[0000−0002−2412−6391] , Gurpriya Bhatia[0000−0002−7511−8543] ,
Mangesh Gharote[0000−0002−4942−2429] , and Sachin Lodha[0000−0001−5771−4977]
54B, TRDDC, Tata Consultancy Services Ltd., Hadapsar, Pune, Maharashtra
-411013 {kumar.vidhani, gurpriya.bhatia, mangesh.g, sachin.lodha}@tcs.com
Abstract. The determination of threshold is a complex and a time con-
suming 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 confidence values computed by
an ontology matching algorithm in order to recommend the threshold.
We demonstrate the effectiveness of our solution through experiments by
comparing our solution with the hierarchical agglomerative clustering.
Keywords: Threshold Value Recommendation · Symmetric and Tran-
sitive Properties · Machine Set · Ontology Matching.
1 Introduction
Martinez-Gil and Aldana-Montes have highlighted the determination of thresh-
old as a complex and time consuming task [1]. After producing an ontology
alignment, a threshold value is specified to produce final alignment. In this pa-
per, we propose a Threshold Val ue Recommendation (ThValRec) approach
that defines two properties namely, symmetric and transitive on the confidence
values computed by an ontology matching algorithm. Through these properties,
ThValRec captures whether ontology matching algorithm computes a confidence
value for a pair of concepts consistently or not and hence only use consistent pairs
to compute final threshold.
2 Approach
As shown in the figure 1, ThValRec consists of the following steps.
Run the ontology matching algorithm on a pair of ontologies and generate a set
of correspondences.
Convert the set of correspondences which is in many-to-many form into one-
to-one form using the linear optimization.
Select the correspondences (of step 2) and filter 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).
2 K. Vidhani et al.
O1, O2 Ontology
Linear Symmetric and Threshold
Matching
many-to-many Optimization one-to-one Transitive properties
Algorithm S: Step
alignment alignment
S1:Run S2:Convert S3:Select S4:Distribute S5:Choose
Fig. 1. ThValRec approach to compute threshold value
Table 1. Comparison between ThValRec (Thvr) approach and HAC. δ = 0.1
Threshold Value F-measure
OntologyPair fastText WuPalmer NGram fastText WuPalmer NGram
TThvr Thac TThvr Thac TThvr Thac FThvr Fhac FThvr Fhac FThvr Fhac
cmt Conference 0.915 0.9 1 0.3 1 0.8 0.417 0.4 0.462 0.136 0.435 0.429
cmt confOf 1 0.9 1 0.3 1 0.8 0.417 0.417 0.5 0.175 0.417 0.417
cmt edas 1 0.9 0.941 0.3 1 0.8 0.609 0.609 0.615 0.174 0.667 0.667
cmt ekaw 1 0.9 1 0.3 1 0.8 0.556 0.556 0.5 0.192 0.526 0.5
cmt iasted 1 0.9 1 0.2 1 0.8 0.889 0.889 0.6 0.082 0.889 0.727
cmt sigkdd 1 0.9 1 0.2 1 0.8 0.727 0.782 0.667 0.235 0.696 0.696
Conference confOf 1 0.9 1 0.2 1 0.8 0.667 0.667 0.519 0.227 0.667 0.643
Conference edas 1 0.9 1 0.1 1 0.8 0.581 0.581 0.5 0.159 0.581 0.514
Conference ekaw 1 0.9 0.938 0.2 0.917 0.8 0.41 0.41 0.375 0.274 0.439 0.444
Conference iasted 1 0.9 0.933 0.2 1 0.8 0.4 0.4 0.333 0.088 0.4 0.4
Conference sigkdd 1 0.9 1 0.2 1 0.8 0.583 0.56 0.538 0.205 0.56 0.519
confOf edas 1 0.9 0.952 0.1 1 0.8 0.564 0.564 0.524 0.283 0.564 0.55
confOf ekaw 1 0.9 1 0.2 1 0.8 0.606 0.606 0.629 0.374 0.606 0.611
confOf iasted 1 0.9 1 0.2 1 0.5 0.615 0.714 0.471 0.148 0.615 0.363
confOf sigkdd 1 0.9 1 0.2 1 0.5 0.727 0.727 0.667 0.111 0.667 0.444
edas ekaw 1 0.9 0.929 0.3 1 0.8 0.474 0.474 0.4 0.124 0.462 0.537
edas iasted 1 0.9 0.933 0.3 1 0.8 0.519 0.519 0.457 0.1 0.519 0.551
edas sigkdd 1 0.9 0.967 0.2 1 0.8 0.609 0.609 0.56 0.228 0.583 0.56
ekaw iasted 1 0.9 0.967 0.2 1 0.8 0.706 0.706 0.476 0.104 0.706 0.632
ekaw sigkdd 1 0.9 1 0.2 1 0.8 0.667 0.667 0.7 0.214 0.632 0.6
iasted sigkdd 1 0.9 1 0.2 1 0.8 0.733 0.774 0.595 0.273 0.71 0.765
and transitive properties.
Distribute the filtered correspondences (of step3) into a set of δ-length intervals.
δ ∈ [0, 1] is a value chosen by a user.
Choose the top interval’s correspondences to determine a threshold value.
3 Experiments
We have conducted experiments on the OAEI 2019 conference dataset to com-
pare threshold values recommended by ThValRec with the hierarchical agglomer-
ative 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 different
ontology matching algorithms.
References
1. Martinez-Gil, J., Aldana-Montes, J.F.: An overview of current ontology meta-
matching solutions. The Knowledge Engineering Review 27(4), 393–412 (2012)
2. dos Santos, J.B., Heuser, C.A., Moreira, V.P., Wives, L.K.: Automatic threshold
estimation for data matching applications. Information Sciences 181(13), 2685–2699
(2011)