An Original Semantics to Keyword Queries for XML Using Structural Patterns

Theodoratos, Dimitri; Wu, Xiaoying

doi:10.1007/978-3-540-71703-4_61

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Pushing a partial LCA into a stack (lines [22][23][24][25][26][27] can be done only if this partial LCA is a child or self of the stack's top node. To this end, entries from the stack are popped until an ancestor of the partial LCA is reached.…”

Section: Algorithm Lcaszmentioning

confidence: 99%

“…We refer to these approaches as filtering approaches as they filter out part of the LCAs that they consider irrelevant. Some filtering approaches, take also into account semantic information (node labels and label paths in the XML tree) in order to filter out irrelevant LCAs [14,6,12,10,15,24]. Although, filtering approaches are intuitively reasonable, they are sufficiently ad-hoc and they are frequently violated in practice resulting in low precision and/or recall [23].…”

Section: Introductionmentioning

confidence: 99%

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Efficient keyword search on large tree structured datasets

Dimitriou¹,

Theodoratos²

2012

Proceedings of the Third International Workshop on Keyword Search on Structured Data

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Keyword search is the most popular paradigm for querying XML data on the web. In this context, three challenging problems are (a) to avoid missing useful results in the answer set, (b) to rank the results with respect to some relevance criterion and (c) to design algorithms that can efficiently compute the results on large datasets.In this paper, we present a novel multi-stack based algorithm that returns as an answer to a keyword query all the results ranked on their size. Our algorithm exploits a lattice of stacks each corresponding to a partition of the keyword set of the query. This feature empowers a linear time performance on the size of the input data for a given number of query keywords. As a result, our algorithm can run efficiently on large input data for several keywords. We also present a variation of our algorithm which accounts for infrequent keywords in the query and show that it can significantly improve the execution time. An extensive experimental evaluation of our approach confirms the theoretical analysis, and shows that it scales smoothly when the size of the input data and the number of input keywords increases.

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Section: Algorithm Lcaszmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient keyword search on large tree structured datasets

Dimitriou¹,

Theodoratos²

2012

Proceedings of the Third International Workshop on Keyword Search on Structured Data

Self Cite

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“…The first is the extension of the keyword query so as to incorporate more information in the keywords, including [5,26,27,28]. The second is about integrating keyword proximity search in a structural query language, such as [29,30,32,31].…”

Section: Related Workmentioning

confidence: 99%

Retrieving meaningful relaxed tightest fragments for XML keyword search

Kong

Gilleron

Mostrare

2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

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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.

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