Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances

Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances

Indexed In: SCOPUS
Release Date: May, 2011|Copyright: © 2011 |Pages: 358
DOI: 10.4018/978-1-60960-625-1
ISBN13: 9781609606251|ISBN10: 1609606256|EISBN13: 9781609606268
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Description & Coverage
Description:

Ontologies form an indispensable part of the Semantic Web standard stack. While the Semantic Web is still our vision into the future, ontologies have already found a myriad of applications such as document retrieval, image retrieval, agent interoperability and document annotation.

Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances provides relevant theoretical foundations, and disseminates new research findings and expert views on the remaining challenges in ontology learning. This book is invaluable resource as a library or personal reference for graduate students, researchers, and industrial practitioners. Readers who are in the process of looking for future research directions, and carving out their own niche area will find this book particularly useful due to the detailed scope and wide coverage of the book, which informs any discussion of artificial intelligence, knowledge acquisition, knowledge representation and reasoning, text mining, information extraction, and ontology learning.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Applications of ontologies
  • Artificial Intelligence
  • Concept Formation
  • Information Extraction
  • Knowledge Acquisition
  • Knowledge Representation and Reasoning
  • Ontology Learning
  • Taxonomy Construction
  • Text Mining
  • Text Processing
Reviews & Statements

The time is ripe for this book. Techniques of ontology learning and knowledge discovery are beginning to converge. Prototypes are becoming stronger. Industry practitioners are beginning to realize the need for ontology learning. Wilson, Wei, and Mohammed bring together recent work in the construction and application of ontologies and knowledge bases. They introduce a wide range of techniques that utilize unstructured and semi-structured Web data for learning and discovery. [...] The interdisciplinary nature of ontology learning and knowledge discovery is reflected in this book. It will appeal to advanced undergraduates, postgraduate students, academic researchers and practitioners. I hope that it will lead to a world in which we can all live more effectively, a world in which the ready availability of information is balanced by our enhanced ability to process it.

– Ian H. Witten

Converting today's onslaught of information into usable knowledge is one of the main challenges facing us in the modern era. This volume covers a wide range of topics, from ontology learning to data mining of comparable patents and is edited by three faculty members at the University of Western Australia.

– Book News, Reference - Research Book News - August 2011
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Editor/Author Biographies
Wilson Wong is a Postdoctoral Research Associate at the University of Western Australia (UWA) working on the application of text mining and natural language processing across different domains such as healthcare. Wilson was an Endeavour IPRS Scholar for his PhD study at UWA. His doctoral dissertation investigates the use of Web data for automatically acquiring knowledge from natural language texts across different domains. Wilson also has a BIT (First Class Honours) (Data Communications) degree, and an MSc (Information and Communication Technology) by research degree in the field of natural language processing from Malaysia. Wilson has close to 30 publications in book chapters, reputable conferences (e.g. IJCNLP, IJCAI, PACLING), and high-impact journals (e.g. DMKD, IDA). His areas of interest include text mining, natural language processing, Web technologies, and health informatics.
Wei Liu is an Assistant Professor at the University of Western Australia, and currently the Lab Coordinator for Adaptive System Group. She obtained her PhD from the University of Newcastle on Multi-Agent Belief Revision, Australia in 2003. Her current research interest is on ontology learning to bootstrap agent knowledge base. Dr. Wei Liu’s research strength lies in ontology learning and data-driven ontology change. She leads the work on developing automatic and semi-automatic ontology learning system from 2004, which addresses the cold-start issues (labour intensive and time consuming) of manual ontology engineering. The research contributes significantly to the investigation of emergent semantics through text mining. The first paper reporting the techniques and the system modules won one of the best papers in the conference and was invited for journal publication. The techniques developed including co-occurrence analysis to identify taxonomic and non-taxonomic relations, and spreading activation to identify the core, extended, and peripheral concepts. Information network analysis and clustering algorithms are also developed to measure the evolution of an ontology in both temporal and spatial scope.
Mohammed Bennamoun received his PhD from Queen's University, Canada/ Queensland University of Technology (QUT), Australia in the area of Computer Vision. He has been a full Professor and the Head of the School of Computer Science and Software Engineering (CSSE) at the University of Western Australia (UWA) since 2007. Prior to this, he was an Associate Professor at CSSE, a Senior Lecturer at QUT, and a Lecturer at Queen’s. He was an Erasmus Mundus Scholar at the University of Edinburgh in 2006. He was also a Visiting Professor at several other institutions including CNRS (Centre National de la Recherche Scientifique), Telecom Lille1, Helsinki University of Technology, University of Bourgogne and University of Paris 13. He is the co-author of the book “Object Recognition: Fundamentals and Case Studies” published by Springer-Verlag. He published over 140 journal and conference publications, and served as a guest editor for several special issues in international journals. His areas of interest include control theory, robotics, obstacle avoidance, object recognition, artificial neural networks, signal/image processing, and computer vision, and lately, in the development of tools for combining text and image analysis.
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Editorial Advisory Board
  • Christopher Brewster, Aston University, UK
  • Chunyu Kit, City University of Hong Kong, China
  • Philipp Cimiano, University of Bielefeld, Germany
  • Sophia Ananiadou, University of Manchester, UK
  • Tharam Dillon, Curtin University of Technology, Australia
  • Venkata Subramaniam, IBM India Research, India