Learning to Match Heterogeneous Structures using Partially Labeled Data

Abstract

This paper addresses the problem of matching between highly heterogeneous structures. The problem is modeled as a classification task where training examples are used to learn the matching between structures. In our approach, training is performed using partially labeled data. We propose a Greedy Mapping approach to generate training examples from partially labeled data. Different types of structures may have different types of attributes that can be exploited to enhance the matching problem. We utilize three types of attributes, namely, text content, structure name and path correspondence, in the matching problem. Experiments are performed on two types of structures: semantic domain and semantic role. We evaluate the effectiveness of the Greedy Mapping as well as the performance on different types of attributes. Finally, the results are presented and discussed.