Tree pattern relaxation. Sihem Amer-Yahia, SungRan Cho and Divesh Srivastava. Tree patterns are fundamental to querying tree-structured data like XML. Because of the heterogeneity of XML data, it is often more appropriate to permit approximate query matching and return ranked answers, in the spirit of Information Retrieval, than to return only exact answers. In this paper, we study the problem of approximate XML query matching, based on tree pattern relaxations, and devise efficient algorithms to evaluate relaxed tree patterns. We consider weighted tree patterns, where exact and relaxed weights, associated with nodes and edges of the tree pattern, are used to compute the scores of query answers. We are interested in the problem of finding answers whose scores are at least as large as a given threshold. We design data pruning algorithms where intermediate query results are filtered dynamically during the evaluation process. We develop an optimization that exploits scores of intermediate results to improve query evaluation efficiency. Finally, we show experimentally that our techniques outperform rewriting-based and post-pruning strategies.