Abstract

Instance matching has emerged as an important problem in the Semantic Web, with machine learning methods proving especially effective. To enhance performance, task-specific knowledge is typically used to introduce bias in the model selection problem. Such biases tend to be exploited by practitioners in a piecemeal fashion. This paper introduces a framework where the model selection design process is represented as a factor graph. Nodes in this bipartite graphical model represent opportunities for explicitly introducing bias. The graph is first used to unify and visualize common biases in the design of existing instance matchers. As a direct application, we then use the graph to hypothesize about potential unexploited biases. The hypotheses are evaluated by training 1032 neural networks on three instance matching tasks on Microsoft Azure’s cloud-based platform. An analysis over 25 GB of …

Date

2015

Authors

Mayank Kejriwal, Daniel P Miranker

Conference

The Semantic Web-ISWC 2015: 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015, Proceedings, Part I 14

Pages

392-407

Publisher

Springer International Publishing