Integrating keyword search into XML query processing

Florescu, Daniela; Kossmann, Donald; Manolescu, Ioana

doi:10.1016/s1389-1286(00)00069-4

Cited by 148 publications

(84 citation statements)

References 4 publications

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“…Keyword search over XML databases has also attracted interest recently [7,12,9]. Florescu et al [7] extend XML query languages to enable keyword search at the granularity of XML elements, which helps novice users formulate queries.…”

Section: Related Workmentioning

confidence: 99%

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Efficient IR-Style Keyword Search over Relational Databases

Hristidis

Gravano

Papakonstantinou

2003

Proceedings 2003 VLDB Conference

469

693

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Applications in which plain text coexists with structured data are pervasive. Commercial relational database management systems (RDBMSs) generally provide querying capabilities for text attributes that incorporate state-of-the-art information retrieval (IR) relevance ranking strategies, but this search functionality requires that queries specify the exact column or columns against which a given list of keywords is to be matched. This requirement can be cumbersome and inflexible from a user perspective: good answers to a keyword query might need to be "assembled" -in perhaps unforeseen ways-by joining tuples from multiple relations. This observation has motivated recent research on free-form keyword search over RDBMSs. In this paper, we adapt IR-style document-relevance ranking strategies to the problem of processing free-form keyword queries over RDBMSs. Our query model can handle queries with both AND and OR semantics, and exploits the sophisticated single-column text-search functionality often available in commercial RDBMSs. We develop query-processing strategies that build on a crucial characteristic of IR-style keyword search: only the few most relevant matches -according to some definition of "relevance"-are generally of interest. Consequently, rather than computing all matches for a keyword query, which leads to inefficient executions, our techniques focus on the top-k matches for the query, for moderate values of k. A thorough experimental evaluation over real data shows the performance advantages of our approach. *

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Section: Related Workmentioning

confidence: 99%

“…Florescu et al [7] extend XML query languages to enable keyword search at the granularity of XML elements, which helps novice users formulate queries. This work does not consider keyword proximity.…”

Section: Related Workmentioning

confidence: 99%

Efficient IR-Style Keyword Search over Relational Databases

Hristidis

Gravano

Papakonstantinou

2003

Proceedings 2003 VLDB Conference

469

693

View full text Add to dashboard Cite

show abstract

“…Keyword search can be used to query XML data, and there is a lot of works focusing on this problem (e.g., [3] [4] [5] [6] [7]). …”

Section: Introductionmentioning

confidence: 99%

Top-k Keyword Search Over Graphs Based On Backward Search

2017

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Abstract:Keyword search is one of the most friendly and intuitive information retrieval methods. Using the keyword search to get the connected subgraph has a lot of application in the graph-based cognitive computation, and it is a basic technology. This paper focuses on the top-k keyword searching over graphs. We implemented a keyword search algorithm which applies the backward search idea. The algorithm locates the keyword vertices firstly, and then applies backward search to find rooted trees that contain query keywords. The experiment shows that query time is affected by the iteration number of the algorithm. IntroductionGraphs are applied in many areas. For example, RDF (Resource Description Framework) data model is a graph-shaped data model. Keyword search is a useful tool when searching large graph data. It is a userfriendly way to retrieve graphs because it does not require users to know the structure of the graph and the syntax of the query language. In this paper, we focus on top-k ranked keyword searching on vertex-labelled graphs. Each vertex in the graph can contain multiple keywords. Given a query list with multiple keywords, we want to find out the subgraph that contains all query keywords. There may be multiple subgraphs that meet the requirement in the graph. It is time-consuming to find all such subgraphs. Moreover, users usually want to obtain the most relevant results with the query. So an evaluation function is needed to assess and sort the results. The evaluation function will be discussed in section 2.For its convenience, keyword search has been adopted in many situations. Keyword search can be used to query XML data, and there is a lot of works focusing on this problem (e.g., [3]

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“…Several studies have been done in the literature to provide effective IR techniques [10,9,6]. A standard containment keywordbased query, which retrieves textual identities that contain a set of keywords, is generally supported by a full-text index.…”

Section: Introductionmentioning

confidence: 99%

“…A standard containment keywordbased query, which retrieves textual identities that contain a set of keywords, is generally supported by a full-text index. Inverted index is considered as one of the most useful full-text indexing techniques for very large textual collections [10], supported by many relational DBMSs, and recently extended toward semistructured [9] and unstructured data [6] to support keyword-based queries.…”

Section: Introductionmentioning

confidence: 99%

SemIndex: Semantic-Aware Inverted Index

Chbeir

Luo

Tekli

et al. 2014

Advances in Databases and Information Systems

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Abstract. This paper focuses on the important problem of semanticaware search in textual (structured, semi-structured, NoSQL) databases. This problem has emerged as a required extension of the standard containment keyword based query to meet user needs in textual databases and IR applications. We provide here a new approach, called SemIndex, that extends the standard inverted index by constructing a tight coupling inverted index graph that combines two main resources: a general purpose semantic network, and a standard inverted index on a collection of textual data. We also provide an extended query model and related processing algorithms with the help of SemIndex. To investigate its effectiveness, we set up experiments to test the performance of SemIndex. Preliminary results have demonstrated the effectiveness, scalability and optimality of our approach.

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Integrating keyword search into XML query processing

Cited by 148 publications

References 4 publications

Efficient IR-Style Keyword Search over Relational Databases

Efficient IR-Style Keyword Search over Relational Databases

Top-k Keyword Search Over Graphs Based On Backward Search

SemIndex: Semantic-Aware Inverted Index

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