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 paper presents a minimally supervised instance matching approach that is able to deliver competitive performance using only 2 % training data and little parameter tuning. As a first step, the classifier is trained in an ensemble setting using boosting. Iterative semi-supervised learning is used to improve the performance of the boosted classifier even further, by re-training it on the most confident samples labeled in the current iteration. Empirical evaluations on a suite of six publicly available benchmarks show that the proposed system outcompetes optimization-based minimally …

Date

2015

Authors

Mayank Kejriwal, Daniel P Miranker

Conference

The Semantic Web. Latest Advances and New Domains: 12th European Semantic Web Conference, ESWC 2015, Portoroz, Slovenia, May 31--June 4, 2015. Proceedings 12

Pages

388-402

Publisher

Springer International Publishing