Retrieving meaningful relaxed tightest fragments for XML keyword search

Abstract

Adapting keyword search to XML data has been attractive recently, generalized as XML keyword search (XKS). One of its key tasks is to return the meaningful fragments as the result.[1] is the latest work following this trend, and it focuses on returning the fragments rooted at SLCA (Smallest LCA -Lowest Common Ancestor) nodes. To guarantee that the fragments only contain interesting nodes, [1] proposes a contributor-based filtering mechanism in its MaxMatch algorithm. However, the filtering mechanism is not sufficient. It will commit the false positive problem (discarding interesting nodes) and the redundancy problem (keeping uninteresting nodes).In this paper, our interest is to propose a framework of retrieving meaningful fragments rooted at not only the SLCA nodes, but all LCA nodes. We begin by introducing the concept of Relaxed Tightest Fragment (RTF) as the basic result type. Then we propose a new filtering mechanism to overcome those two problems in MaxMatch. Its kernel is the concept of valid contributor, which helps to distinguish the interesting children of a node. The new filtering mechanism is then to prune the nodes in a RTF which are not valid contributors to their parents. Based on the valid contributor concept, our ValidRTF algorithm not only overcomes those two problems in MaxMatch, but also satisfies the axiomatic properties deduced in [1] that an XKS technique should satisfy. We compare ValidRTF with MaxMatch on real and synthetic XML data. The result verifies our claims, and shows the effectiveness of our validcontributor-based filtering mechanism.

Fast Result Enumeration for Keyword Queries on XML Data

“…In Fig. 1 Based on the set of matched SLCA nodes, there are three kinds of subtree results: 1) CSubtree [2,4]; 2) PSubtree [13]; and 3) MSubtree, which is a subtree rooted at v satisfying the constraints of monotonicity and consistency [7,8], which can be further interpreted by the changing of data and query, respectively. Data monotonicity means that if we add a new node to the data, the number of query results should be (non-strictly) monotonically increasing.…”

“…Over the past few years, keyword search on extensible markup language (XML) data has been a hot research issue, along with the steady increase of XML-based applications [1][2][3][4][5][6][7][8][9][10][11][12]. As with the importance of effectiveness in keyword search, efficiency is also a key factor in the success of keyword search.…”

“…Based on the set of qualified root nodes, there are three kinds of subtree results: 1) complete subtree (CSubtree), which is a subtree rooted at a node v that is excerpted from the original XML tree without pruning any informat v C tion [2,4]; 2) path subtree (PSubtree), which is a subtree that consists of paths from v to all of its descendants, each of which contains at least one input keyword [13]; and 3) matched subtree (MSubtree), which is a subtree rooted at v satisfying the constraints of monotonicity and consistency [7,8]. Let S v' = {k 1 ,k 2 ,...k m' } be the set of distinct keywords contained in the subtree rooted at a node v', S v' Q.…”

“…In fact, constructing subtree results is not a trivial task. Existing methods [7,8] need to firstly scan all node labels to compute qualified SLCA/ELCA results, and then rescan all node labels to construct the initial subtrees. After that, they need to buffer these subtrees in memory, and apply the constraint of keywords subsumption on each node of these subtrees, to prune nodes with keyword sets subsumed by that of their sibling nodes, which is inefficient in time and space.…”

“…As illustrated by [7], an MSubtree could still contain redundant information; e.g., the four conference nodes, i.e., 1. Fig.…”