Efficient Evaluation of Partial Match Queries for XML Documents Using Information Retrieval Techniques

Whang, Kyu-Young; Lee, Byung Suk; Han, Wook-Shin

doi:10.1007/11408079_11

Cited by 9 publications

(26 citation statements)

References 11 publications

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“…Here, we define the result structure 2 of a query result qr as a (schema-level) twig pattern composed of the label path [14] from the root of the XML data to the root qr root of qr (simply, the incoming label path) and the ancestor-descendant edges from qr root to query keywords. In the result structure of a query result qr, denoted by rs(qr), the node corresponding to qr root is marked as the query result node [35] and is distinguished from other nodes by placing it in a box. Fig.…”

Section: Author(66)mentioning

confidence: 99%

“…Definition 2 [35] The node path of a node o is defined as a sequence of node identifiers n 1 , n 2 , ..., n m from the root to the node o, and is denoted as n 1 .n 2 . · · · .n m .…”

Section: Xml Data Modelmentioning

confidence: 99%

“…In the twig pattern a node, called a query node [10], represents a label (or a value), and an edge represents the parent-child or ancestordescendant relationship between two nodes. One node of the twig pattern is marked as the query result node [35] and is distinguished from other nodes by placing it in a box. A query node that has more than one child node is called a branching query node [35].…”

Section: Xpath Querymentioning

confidence: 99%

“…One node of the twig pattern is marked as the query result node [35] and is distinguished from other nodes by placing it in a box. A query node that has more than one child node is called a branching query node [35]. A leaf node of the twig pattern is called a leaf query node.…”

Section: Xpath Querymentioning

confidence: 99%

“…Inverted indexes have been used in many XML query processing methods [10,15,28,35]. We also use a table called LabelPath [35] to store all the label paths occurring in the DataGuide + . Table 1 summarizes the notation to be used for explaining the index structures.…”

Section: Index Structuresmentioning

confidence: 99%

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Structural consistency: enabling XML keyword search to eliminate spurious results consistently

et al. 2010

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XML keyword search is a user-friendly way to query XML data using only keywords. In XML keyword search, to achieve high precision without sacrificing recall, it is important to remove spurious results not intended by the user. Efforts to eliminate spurious results have enjoyed some success by using the concepts of LCA or its variants, SLCA and MLCA. However, existing methods still could find many spurious results. The fundamental cause for the occurrence of spurious results is that the existing methods try to eliminate spurious results locally without global examination of all the query results and, accordingly, some spurious results are not consistently eliminated. In this paper, we propose a novel keyword search method that removes spurious results consistently by exploiting the new concept of structural consistency. We define structural consistency as a property that is preserved if there is no query result having an ancestor-descendant relationship at the schema level with any other query results. A naive solution to obtain structural consistency would be to compute all the LCAs (or variants) and then to remove spurious results according to structural consistency. Obviously, this approach would always be slower than existing LCA-based ones. To speed up structural consistency checking, we must be able to examine the query results at the schema level without generating all the LCAs. However, this is a challenging problem since the schema-level query results do not homomorphically map to the instance-level query results, causing serious false dismissal. We present a comprehensive and practical solution to this problem and formally prove that this solution preserves structural consistency at the schema level without incurring false dismissal. We also propose a relevance-feedback based solution for the problem where our method has low recall, which occurs when it is not the user's intention to find more specific results. This solution has been prototyped in a full-fledged object-relational DBMS. Experimental results using real and synthetic data sets show that, compared with the state-of-the-art methods, our solution significantly 1) improves precision while providing comparable recall for most queries and 2) enhances the query performance by removing spurious results early.

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Section: Author(66)mentioning

confidence: 99%

“…Definition 2 [35] The node path of a node o is defined as a sequence of node identifiers n 1 , n 2 , ..., n m from the root to the node o, and is denoted as n 1 .n 2 . · · · .n m .…”

Section: Xml Data Modelmentioning

confidence: 99%