Keyword search in relational databases

Park, Jaehui; Lee, Sang-goo

doi:10.1007/s10115-010-0284-1

Cited by 42 publications

(24 citation statements)

References 25 publications

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“…Recently, keyword query methods in relational databases have been studied extensively [2,[13][14][15]. According to the different modeling methods of databases, the existing query methods can be divided into two main categories: schema graph-based methods and data graph-based methods.…”

Section: Keyword Query In Relational Databasesmentioning

confidence: 99%

“…In recent years, keyword query in relational databases has been widely applied due to its simplicity and ease of use [1,2]. Although this method does not require users to be knowledgeable about the underlying structure of databases and structured query language (e.g., SQL), its semantic fuzziness and its expressive power are limited due to the lack of structure [3].…”

Section: Introductionmentioning

confidence: 99%

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Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational Databases

Wang

Zhou

2017

Scientific Programming

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Due to the ambiguity and impreciseness of keyword query in relational databases, the research on keyword query expansion has attracted wide attention. Existing query expansion methods expose users' query intention to a certain extent, but most of them cannot balance the precision and recall. To address this problem, a novel two-step query expansion approach is proposed based on query recommendation and query interpretation. First, a probabilistic recommendation algorithm is put forward by constructing a term similarity matrix and Viterbi model. Second, by using the translation algorithm of triples and construction algorithm of query subgraphs, query keywords are translated to query subgraphs with structural and semantic information. Finally, experimental results on a real-world dataset demonstrate the effectiveness and rationality of the proposed method.

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Section: Keyword Query In Relational Databasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational Databases

Wang

Zhou

2017

Scientific Programming

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“…The earlier survey in [8] and [9] overviewed systems such as BANKS [10], DBXplorer [3], DISCOVER [4], ObjectRank [2], and EASE [11] and briefly summarized the key techniques from several aspects.…”

Section: Related Workmentioning

confidence: 99%

A Metadata Search Approach to Keyword Query in Relational Databases

Saelee¹,

Boonjing²

2013

IJCA

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This paper proposes an effective approach to keyword query in relational databases. It uses a semantic graph model consisting of database metadata, database values, user terms, and their semantic connections. Keywords of a query determine all possible connected subgraphs of the semantic model. A query answer is a subgraph with the minimum connections. In addition, the approach proposes to rank result tuples of the answer subgraph using the IR-style ranking function. Our experiment results show that queries with metadata terms give more precise answers than queries without them. General Terms

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“…This task bears some resemblance to keyword search in relational databases [89] and to XML retrieval [50]. The former has no direct relevance as methods developed for structured databases are not directly applicable to RDF data.…”

Section: Related Workmentioning

confidence: 99%

Semantic and distributed entity search in the web of data

Neumayer

2012

SIGIR Forum

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Both the growth and ubiquitious character of the Internet have had a profound effect on how we access and consume ata and information. More recently, the Semantic Web, an extension of the current Web has come increasingly relevant due to its widespread adoption.The Web of Data (WoD) is an extension of the current web, where not only documents are interlinked by means of hyperlinks but also data in terms of predicates. Specifically, it describes objects, entities or "things" in terms of their attributes and their relationships, using RDF data (and often is used equivalently to Linked Data). Given its growth, there is a strong need for making this wealth of knowledge accessible by keyword search (the de-facto standard paradigm for accessing information online).The overall goal of this thesis is to provide new techniques for accessing this data, i.e., to leverage its full potential to end users. We therefore address the following four main issues: a) how can the Web of Data be searched by means of keyword search?, b) what sets apart search in the WoD from traditional web search?, c) how can these elements be used in a theoretically sound and effective way?, and d) How can the techniques be adapted to a distributed environment?To this end, we develop techniques for effectively searching WoD sources. We build upon and formalise existing entity modelling approaches within a generative language modelling framework, and compare them experimentally using standard test collections. We show that these models outperform the current state-of-the-art in terms of retrieval effectiveness, however, this is done at the cost of abandoning a large part of the semantics behind the data. We propose a novel entity model capable of preserving the semantics associated with entities, without sacrificing retrieval effectiveness. We further show how these approaches can be applied in the distributed context, both with low (federated search) and high numbers (Peerto-peer or P2P) of independent repositories, collections, or nodes.The main contributions are as follows:• We develop a hybrid approach to search in the Web of Data, using elements from traditional information retrieval and structured retrieval alike.• We formalise our approaches in a language model setting.• Our extensions are successfully evaluated with respect to their applicability in different distributed environments such as federated search and P2P.• We discuss and analyse based on our empirical evaluation and provide insights into the entity search problem. Preface

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Keyword search in relational databases

Cited by 42 publications

References 25 publications

Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational Databases

Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational Databases

A Metadata Search Approach to Keyword Query in Relational Databases

Semantic and distributed entity search in the web of data

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