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Managing data from knowledge bases =...

Zhang, Wei Emma.

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Managing data from knowledge bases = querying and extraction /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Managing data from knowledge bases/ by Wei Emma Zhang, Quan Z. Sheng.
Reminder of title:	querying and extraction /
Author:	Zhang, Wei Emma.
other author:	Sheng, Quan Z.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xiii, 139 p. :ill., digital ;24 cm.
[NT 15003449]:	1 Introduction -- 2 Cache Based Optimization for Querying Curated Knowledge Bases -- 3 Query Performance Prediction on Knowledge Base -- 4 An Efficient Knowledge Clustering Algorithm -- 5 Knowledge Extraction from Unstructured Data on the Web -- 6 Building Knowledge Bases from Unstructured Data on the Web -- 7 Conclusion.
Contained By:	Springer eBooks
Subject:	Databases. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-94935-2
ISBN:	9783319949352

Managing data from knowledge bases = querying and extraction /
Zhang, Wei Emma.

Managing data from knowledge basesquerying and extraction /[electronic resource] :by Wei Emma Zhang, Quan Z. Sheng. - Cham :Springer International Publishing :2018. - xiii, 139 p. :ill., digital ;24 cm.

1 Introduction -- 2 Cache Based Optimization for Querying Curated Knowledge Bases -- 3 Query Performance Prediction on Knowledge Base -- 4 An Efficient Knowledge Clustering Algorithm -- 5 Knowledge Extraction from Unstructured Data on the Web -- 6 Building Knowledge Bases from Unstructured Data on the Web -- 7 Conclusion.

In this book, the authors first address the research issues by providing a motivating scenario, followed by the exploration of the principles and techniques of the challenging topics. Then they solve the raised research issues by developing a series of methodologies. More specifically, the authors study the query optimization and tackle the query performance prediction for knowledge retrieval. They also handle unstructured data processing, data clustering for knowledge extraction. To optimize the queries issued through interfaces against knowledge bases, the authors propose a cache-based optimization layer between consumers and the querying interface to facilitate the querying and solve the latency issue. The cache depends on a novel learning method that considers the querying patterns from individual's historical queries without having knowledge of the backing systems of the knowledge base. To predict the query performance for appropriate query scheduling, the authors examine the queries' structural and syntactical features and apply multiple widely adopted prediction models. Their feature modelling approach eschews the knowledge requirement on both the querying languages and system. To extract knowledge from unstructured Web sources, the authors examine two kinds of Web sources containing unstructured data: the source code from Web repositories and the posts in programming question-answering communities. They use natural language processing techniques to pre-process the source codes and obtain the natural language elements. Then they apply traditional knowledge extraction techniques to extract knowledge. For the data from programming question-answering communities, the authors make the attempt towards building programming knowledge base by starting with paraphrase identification problems and develop novel features to accurately identify duplicate posts. For domain specific knowledge extraction, the authors propose to use clustering technique to separate knowledge into different groups. They focus on developing a new clustering algorithm that uses manifold constraint in the optimization task and achieves fast and accurate performance. For each model and approach presented in this dissertation, the authors have conducted extensive experiments to evaluate it using either public dataset or synthetic data they generated.

ISBN: 9783319949352

Standard No.: 10.1007/978-3-319-94935-2doiSubjects--Topical Terms:

747532
Databases.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 005.74

Managing data from knowledge bases = querying and extraction /
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100 1 $a Zhang, Wei Emma. $3 3338188
245 1 0 $a Managing data from knowledge bases $h [electronic resource] : $b querying and extraction / $c by Wei Emma Zhang, Quan Z. Sheng.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xiii, 139 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1 Introduction -- 2 Cache Based Optimization for Querying Curated Knowledge Bases -- 3 Query Performance Prediction on Knowledge Base -- 4 An Efficient Knowledge Clustering Algorithm -- 5 Knowledge Extraction from Unstructured Data on the Web -- 6 Building Knowledge Bases from Unstructured Data on the Web -- 7 Conclusion.
520 $a In this book, the authors first address the research issues by providing a motivating scenario, followed by the exploration of the principles and techniques of the challenging topics. Then they solve the raised research issues by developing a series of methodologies. More specifically, the authors study the query optimization and tackle the query performance prediction for knowledge retrieval. They also handle unstructured data processing, data clustering for knowledge extraction. To optimize the queries issued through interfaces against knowledge bases, the authors propose a cache-based optimization layer between consumers and the querying interface to facilitate the querying and solve the latency issue. The cache depends on a novel learning method that considers the querying patterns from individual's historical queries without having knowledge of the backing systems of the knowledge base. To predict the query performance for appropriate query scheduling, the authors examine the queries' structural and syntactical features and apply multiple widely adopted prediction models. Their feature modelling approach eschews the knowledge requirement on both the querying languages and system. To extract knowledge from unstructured Web sources, the authors examine two kinds of Web sources containing unstructured data: the source code from Web repositories and the posts in programming question-answering communities. They use natural language processing techniques to pre-process the source codes and obtain the natural language elements. Then they apply traditional knowledge extraction techniques to extract knowledge. For the data from programming question-answering communities, the authors make the attempt towards building programming knowledge base by starting with paraphrase identification problems and develop novel features to accurately identify duplicate posts. For domain specific knowledge extraction, the authors propose to use clustering technique to separate knowledge into different groups. They focus on developing a new clustering algorithm that uses manifold constraint in the optimization task and achieves fast and accurate performance. For each model and approach presented in this dissertation, the authors have conducted extensive experiments to evaluate it using either public dataset or synthetic data they generated.
650 0 $a Databases. $3 747532
650 0 $a Querying (Computer science) $3 625110
650 0 $a Artificial intelligence. $3 516317
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Information Storage and Retrieval. $3 761906
650 2 4 $a Information Systems Applications (incl. Internet) $3 1565452
700 1 $a Sheng, Quan Z. $3 1097306
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-94935-2
950 $a Computer Science (Springer-11645)