TMG framework for mining ordered subtrees

Abstract

In this chapter, we will elaborate on the overall TMG framework for mining ordered subtrees as described in Chapter 4 (Tan 2008). In an ordered tree, for each internal node, the order of its children is fixed. Such trees have found many useful applications in areas such as vision, natural language processing, molecular biology, programming compilation etc. (Wang, Zhang, Jeong & Shasha 1994). In the research on automatic natural language processing, the dictionary definitions are represented syntactically as trees. Computational linguists extract semantic information about these definitions and in the process construct semantic taxonomies (Chodorow & Klavans, 1990; Neff, Roy & Omneya 1998). In the molecular biology field, large amounts of analyzed RNA structures are collected and stored in the form of ordered labeled trees. When the researchers want to acquire information about a new RNA structure, it is compared against those already in the database in order to detect structural similarities and thereby relate different RNA structures (Shapiro & Zhang 1990). Since the researchers will maintain the RNA-related information in the same order, a comparison of ordered subtrees is sufficient. A general observation about applications where ordered subtree mining is suitable is that the left-to-right order among sibling nodes is commonly fixed and known beforehand. Ordered subtree mining is useful for querying a single database where the order restriction can be placed on the query subtree because it is known beforehand. © 2011 Springer-Verlag Berlin Heidelberg.