Minimally Supervised Instance Matching: An Alternate Approach

  • Mayank Kejriwal
  • Daniel P. Miranker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9341)


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 even further, by self-training it on the most confident samples labeled in the current iteration. Empirical evaluations on real-world data show that, using a multilayer perceptron as base classifier, the system is able to achieve an average F-Measure that is within 2.5 % of that of state-of-the-art supervised systems.


Instance Matching Boosting Semi-supervision Self-training 


  1. 1.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995) CrossRefGoogle Scholar
  2. 2.
    Ngomo, A.-C.N., Lyko, K.: Unsupervised learning of link specifications: deterministic vs. non-deterministic. In: OM, pp. 25–36 (2013)Google Scholar
  3. 3.
    Nikolov, A., d’Aquin, M., Motta, E.: Unsupervised learning of link discovery configuration. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 119–133. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  4. 4.
    Scharffe, F., Ferrara, A., Nikolov, A., et al.: Data linking for the semantic web. Int. J. Seman. Web Inf. Syst. 7(3), 46–76 (2011)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.University of Texas at AustinAustinUSA

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