© 2018

Managing Data From Knowledge Bases: Querying and Extraction


  • This book incorporates an extensive survey that overviews the main techniques and research works for the knowledge extraction and querying in knowledge bases. Two types of knowledge bases are introduced, discussed and compared. Based on the survey, key challenges of the addressed topics are discussed. A framework for data management in knowledge base are proposed and for each component in this framework, we provide description and discussion. Thus, this book can be a good reference for the readers who seek to have an overview of knowledge base data management.

  • This book covers several important research topics that are under the umbrella of querying knowledge base and knowledge extraction for the construction of knowledge base. The authors discuss the problems and provide solutions for speeding up querying process, predicting query performance, knowledge cleaning, knowledge clustering and constructing knowledge base from unstructured data. For each problem, this book provides not only technical solutions, but also design and implementation details. Therefore, this book provides both theoretical and applied computing scientific research, making it attractive to a variety set of readers from both academia and industry.

  • The book provides extensive analysis and evaluations from the real-world datasets including knowledge base queries and knowledge data obtained from different sources. Analysis results from the performance study draws a number of open research issues for knowledge extraction and querying. These open research issues and future directions are beneficial for those adopting knowledge base techniques


Table of contents

  1. Front Matter
    Pages i-xiii
  2. Wei Emma Zhang, Quan Z. Sheng
    Pages 1-18
  3. Wei Emma Zhang, Quan Z. Sheng
    Pages 19-46
  4. Wei Emma Zhang, Quan Z. Sheng
    Pages 47-67
  5. Wei Emma Zhang, Quan Z. Sheng
    Pages 69-88
  6. Wei Emma Zhang, Quan Z. Sheng
    Pages 89-102
  7. Wei Emma Zhang, Quan Z. Sheng
    Pages 103-121
  8. Wei Emma Zhang, Quan Z. Sheng
    Pages 123-126
  9. Back Matter
    Pages 127-139

About this book


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 a clustering technique to separate knowledge into different groups. They focus on developing a new clustering algorithm that uses manifold constraints 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.


Knowledge Management Semantic Web Querying Knowledge Base Query Performance Prediction Query Optimization Knowledge Extraction Open Knowledge Base Curated Knowledge Base Clustering Unstructured Data Topic Model

Authors and affiliations

  1. 1.Department of ComputingMacquarie UniversitySydneyAustralia
  2. 2.Department of ComputingMacquarie UniversitySydneyAustralia

Bibliographic information

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