Abstract

Instance matching concerns identifying pairs of instances that refer to the same underlying entity. Current state-of-the-art instance matchers use machine learning methods. Supervised learning systems achieve good performance by training on significant amounts of manually labeled samples. To alleviate the labeling effort, this poster (The work presented herein is also being published as a full conference paper at ESWC 2015. This poster provides a more high-level overview and discusses supplemental experimental findings beyond the scope of the material in the full paper.) presents a minimally supervised instance matching approach that is able to deliver competitive performance using only 2 % training data. As a first step, a committee of base classifiers is trained in an ensemble setting using boosting. Iterative semi-supervised learning is used to improve the performance of the ensemble classifier …

Date

March 15, 2026

Authors

Mayank Kejriwal, Daniel P Miranker

Conference

The Semantic Web: ESWC 2015 Satellite Events: ESWC 2015 Satellite Events, Portorož, Slovenia, May 31–June 4, 2015, Revised Selected Papers 12

Pages

72-76

Publisher

Springer International Publishing