Learning DMEs from Positive and Negative Examples

Yeting Li, Chunmei Dong, Xinyu Chu, Haiming Chen

Abstract: The presence of a schema for XML documents has numerous advantages. Unfortunately, many XML documents in practice are not accompanied by a (valid) schema. Therefore, it is essential to devise algorithms to infer schemas from XML documents. The fundamental task in XML schema inference is learning regular expressions. In this paper we consider unordered XML, where the relative order among siblings is ignored, and focus on the subclass called disjunctive multiplicity expressions (DMEs) which are proposed for unordered XML. Previous work in this direction lacks inference algorithms that support for learning DME from both positive and negative examples. In this paper, we provide an algorithm to learn DMEs from both positive and negative examples based on genetic algorithms.