Keyword Search over Relational Databases: Issues, Approaches and Open Challenges

Empowering the user to access databases using simple keyword search can be a relief for the user to learn SQL and to understand the schema. Keyword Search has also received a lot of attention in database as it is an effective technique to retrieve the query without knowing the underlying schema. Researches done in this area mostly deal with keyword search in single database then what for when the user is dealing with more than one database, to overcome this problem we are introducing an algorithm of SIL (Searching in Linked Databases). The other challenge in keyword processing is storing the keyword in the table i.e. via Inverted Index, so we introduce a novel technique to DeINIX (Density INverted IndeX) which reduce the memory storage space henceforth the pre-processing time is also reduced and the answer displayed will be First-10 answers. Our empirical result shows that this method answers queries more precisely, takes lesser time and memory space to retrieve a quality answer.

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“…In paper [4] the existing approaches is divided into two major categories. The first approach is the Tuple Based approach.…”

Section: Related Workmentioning

confidence: 99%

Supporting Linked Databases in Keyword Query Searching Using Density Inverted Indexes

Bilimoria

Patel

Rajpoot

2015

Emerging Research in Computing, Information, Communication and Applications

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“…We can obtain the intrasimilarity between any two keywords in databases by formula (1). Example 2 will further show the calculation process above.…”

Section: Construction Of Term Similaritymentioning

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%

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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A Novel Ranking Model for a Large-Scale Scientific Publication

Sohn

Jung

2014

Mobile Netw Appl

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Keyword Search over Relational Databases: Issues, Approaches and Open Challenges

Cited by 8 publications

References 28 publications

Supporting Linked Databases in Keyword Query Searching Using Density Inverted Indexes

Supporting Linked Databases in Keyword Query Searching Using Density Inverted Indexes

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

A Novel Ranking Model for a Large-Scale Scientific Publication

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