Data on the Web: from relations to semistructured data and XML
Data on the Web: from relations to semistructured data and XML
On Exact Learning of Unordered Tree Patterns
Machine Learning
Discovering Structural Association of Semistructured Data
IEEE Transactions on Knowledge and Data Engineering
Optimizing Regular Path Expressions Using Graph Schemas
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Polynomial Time Inductive Inference of Regular Term Tree Languages from Positive Data
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Efficient Learning of Semi-structured Data from Queries
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Ordered Term Tree Languages which Are Polynomial Time Inductively Inferable from Positive Data
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Polynomial Time Algorithms for Finding Unordered Tree Patterns with Internal Variables
FCT '01 Proceedings of the 13th International Symposium on Fundamentals of Computation Theory
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Automatic wrapper generation for metasearch using ordered tree structured patterns
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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Tree structured data such as HTML/XML files are represented by rooted trees with ordered children and edge labels. Knowledge representations for tree structured data are quite important to discover interesting features which such tree structured data have. In order to represent tree structured patterns with rich structural features, we introduce a new type of structured variables, called height-constrained variables. An (i,j)-height-constrained variable can be replaced with any tree such that the trunk length of the tree is at least i and the height of the tree is at most j. Then, we define a term tree as a rooted tree structured pattern with ordered children and height-constrained variables. In this paper, given a term tree t and an ordered tree T, we present an $O(N\max\{nD_{\max},{\cal S}\})$ time algorithm of deciding whether or not t matches T, where D max is the maximum number of the children of an internal vertex in T, ${\cal S}$ is the sum of all trunk length constraints i of all (i,j)-height-constrained variables in t, and n and N are the numbers of vertices of t and T, respectively.