An Effective Object-Level XML Keyword Search

Bao, Zhifeng; Lu, Jiaheng; Ling, Tok Wang; Xu, Liang; Wu, Hong

doi:10.1007/978-3-642-12026-8_10

Cited by 10 publications

(6 citation statements)

References 16 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…XKeyword [9] presented a method to optimized the query evaluation by making use of the graph's schema in XML. [3] proposed an object-level matching semantics on XML graph based on the assumption that the object information is given. XML Tree Keyword Search.…”

Section: Related Workmentioning

confidence: 99%

Efficient XML Keyword Search: From Graph Model to Tree Model

Zeng

Bao

Ling

et al. 2013

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Keyword search, as opposed to traditional structured query, has been becoming more and more popular on querying XML data in recent years. XML documents usually contain some ID nodes and IDREF nodes to represent reference relationships among the data. An XML document with ID/IDREF is modeled as a graph by existing works, where the keyword query results are computed by graph traversal. As a comparison, if ID/IDREF is not considered, an XML document can be modeled as a tree. Keyword search on XML tree can be much more efficient using tree-based labeling techniques. A nature question is whether we need to abandon the efficient XML tree search methods and invent new, but less efficient search methods for XML graph. To address this problem, we propose a novel method to transform an XML graph to a tree model such that we can exploit existing XML tree search methods. The experimental results show that our solution can outperform the traditional XML graph search methods by orders of magnitude in efficiency while generating a similar set of results as existing XML graph search methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Efficient XML Keyword Search: From Graph Model to Tree Model

Zeng

Bao

Ling

et al. 2013

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, ranking of target objects is restricted to the distance between elements which leads to missing objects that are more related to the keywords, although they do not contain them. Recently, Bao [7] presented an object-level to retrieve effective results which are based on schema document. The result is either a single object or a set of interconnecting objects.…”

Section: Related Workmentioning

confidence: 99%

“…To define temporal objects in XML document, we combine the definitions of temporal object in [23,24] and an object tree in [7] as follows. where "OID" is the object identifier.…”

Section: Background Notions and Definitionsmentioning

confidence: 99%

“…We compared our approach to two state-of-art approaches: SLCA [2] as an example of XML tree model and ISO_IRO [7] as an example of XML graph model. We analyzed our results according to the three metrics that are used to analyze the results of any keyword search experiments; effectiveness, efficiency, and scalability.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Another drawback is that it does not consider ID/IDREF relationship between nodes, which may result in missing some relevant results. Recent approaches [4,7,8] preferred to model XML document as a set of interrelated objects rather than nodes. Each object is represented as a sub tree rooted by a representative node with its set of attributes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

TX-Kw: An Effective Temporal XML Keyword Search

Bin-Thalab¹,

El-Tazi²,

El-Sharkawi³

2013

IJACSA

View full text Add to dashboard Cite

Abstract-Inspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. Existing methods cannot resolve challenges addressed by using keyword search in Temporal XML documents. We propose a way to evaluate temporal keyword search queries over Temporal XML documents. Moreover, we propose a new ranking method based on the time-aware IR ranking methods to rank temporal keyword search queries results. Extensive experiments have been conducted to show the effectiveness of our approach.

show abstract