Title :
A system for approximate tree matching
Author :
Wang, Jason Tsong-Li ; Zhang, Kaizhong ; Jeong, Karpjoo ; Shasha, Dennis
Author_Institution :
Courant Inst. of Math. Sci., New York Univ., NY, USA
fDate :
8/1/1994 12:00:00 AM
Abstract :
Ordered, labeled trees are trees in which each node has a label and the left-to-right order of its children (if it has any) is fixed. Such trees have many applications in vision, pattern recognition, molecular biology, programming compilation, and natural language processing. Many of the applications involve comparing trees or retrieving/extracting information from a repository of trees. Examples include classification of unknown patterns, analysis of newly sequenced RNA structures, semantic taxonomy for dictionary definitions, generation of interpreters for nonprocedural programming languages, and automatic error recovery and correction for programming languages. Previous systems use exact matching (or generalized regular expression matching) for tree comparison. This paper presents a system, called approximate-tree-by-example (ATBE), which allows inexact matching of trees. The ATBE system interacts with the user through a simple but powerful query language; graphical devices are provided to facilitate inputing the queries. The paper describes the architecture of ATBE, illustrates its use and describes some aspects of ATBE implementation. We also discuss the underlying algorithms and provide some sample applications
Keywords :
database theory; query languages; search problems; tree data structures; trees (mathematics); RNA structures; approximate tree matching; approximate-tree-by-example; automatic error recovery; dictionary definitions; labeled trees; molecular biology; natural language processing; pattern recognition; programming compilation; query language; semantic taxonomy; vision; Computer languages; Data mining; Information retrieval; Natural language processing; Pattern analysis; Pattern recognition; Pediatrics; RNA; Taxonomy; Tree graphs;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on