Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances
Editors:
Wilson Wong, Wei Liu and Mohammed Bennamoun
University of Western Australia, Australia
Call for Chapters:
Proposals Submission Deadline: December 15, 2009
Introduction
Ontologies provide formal specifications of what might exist in a domain to ensure reusability and interoperability of multiple heterogeneous systems. 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, question answering, image retrieval, agent interoperability and document annotation. In recent years, automatic ontology learning from text has provided support and relief for knowledge engineers from the labourious task of manually engineering of ontologies. Ontology learning research, an area integrating advances from information retrieval, text mining, data mining, machine learning and natural language processing, has attracted increasing interests from a wide spectrum of application domains (e.g. bioinformatics, manufacturing). Being a rapidly growing area, it is crucial to collect the recent advances in tools and technologies in ontology learning and related areas.
Objective of the Book
The main objective of this book is to provide relevant theoretical foundations, and disseminate new research findings and expert views on the remaining challenges in ontology learning. In particular, the book focuses on the following questions:
Can ontology learning continue to rely on techniques borrowed from related areas that were conceived for other purposes? Has the time arrived for us to look at certain peculiar requirements of ontology learning and develop specific techniques to meet these requirements?
Lightweight ontologies are the most common type of ontologies in a variety of existing Semantic Web applications (e.g. knowledge management, document retrieval, communities of practice, data integration). Can these lightweight ontologies be easily extended to formal ones? If so, how?
The poor coverage, rarity and maintenance cost related to manually-created resources such as semantic lexicons (e.g. WordNet, UMLS) and text corpora (e.g. BNC, GENIA corpus) have prompted an increasing number of researchers to turn to dynamic Web data for ontology learning. There is currently a lack of study concentrating on the systematic use of Web data as background knowledge for all phases of ontology learning. How do we know if we have the necessary background knowledge to carry out all our ontology learning tasks? Where do we look for more background knowledge if we know that what we have is inadequate?
More and more practitioners in the domain of biology, health care, chemistry, manufacturing, etc are looking up to ontology learning techniques for solutions to their knowledge sharing and reusability needs. How much more difficult is it to automatically learn ontologies from news articles, as compared to clinical notes or biomedical literature? To what extent can the current techniques meet the requirements of learning from texts across different domains? Is the field of automatic ontology learning from text ready for the industry?
Target Audience
This proposed book will be an invaluable resource as a library or personal reference for a wide range of audience, including, graduate students, researchers and industrial practitioners. Postgraduate students 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, it also has the potential of being an upper-level course supplement for senior undergraduate students in Artificial Intelligence, and a resource for lecturers in Knowledge Acquisition, Knowledge Representation and Reasoning, Text Mining, Information Extraction, and Ontology Learning.
Recommended topics include, but are not limited to, the following:
Area 1: Text Processing
Web data pre-processing
Noisy text analytics
Text annotation/Sentence parsing
Textual content extraction/Boilerplates removal
Automatic corpus construction
Area 2: Taxonomy Construction/Concept Formation
Named entity recognition/noun phrase chunking
Feature-based/featureless similarity and distance measures
Term recognition/term extraction/terminology mining
Cluster analysis/term clustering
Entity disambiguation
Relevance/contrastive analysis
Latent semantic analysis
Other machine learning-based techniques
Other corpus-based techniques
Area 3: Relation and Axiom Discovery/Ontology Languages
Lexico-syntactic patterns
Use of dynamic Web data (e.g. Wikipedia mining, online dictionaries)
Sub-categorisation frames
Association rules mining
Inductive logic programming
Other corpus-based techniques
Logic-based/frame-based/markup ontology languages
Area 4: Applications of Ontologies
Bioinformatics
Risk management
Manufacturing
Health care
Other relevant application areas
Submission Procedure
Researchers and practitioners are invited to submit on or before 15 DECEMBER 2009, a 2-3
page chapter proposal clearly explaining the mission and concerns together with a tentative
organisation (i.e. section titles with section summaries) of their proposed chapter. Authors
of accepted proposals will be notified by 15 JANUARY 2010 about the status of their
proposals. Authors of accepted proposals will be sent guidelines and templates to prepare
the full chapter of 8,000 - 10,000 words. Full chapters are expected to be submitted by 15
MARCH 2010. All submitted full chapters will be reviewed on a double-blind review basis. All
proposals and chapters should be typewritten in English in APA style and be submitted in
Microsoft Word® format to wilson@csse.uwa.edu.au. Unfortunately, LaTex files cannot be
accepted. Contributors may also be requested to serve as reviewers for this project. This
book is scheduled to be published by IGI Global (formerly Idea Group Inc.). For additional
information regarding the publisher, please visit
http://www.igi-global.com/requests/details.asp?ID=724. This publication is anticipated to be
released late 2010.
Important Dates
15 December 2009: Proposal Submission Deadline
15 January 2010: Notification of Acceptance
15 March 2010: Full Chapter Submission
15 July 2010: Review Results Returned
15 August 2010: Final Chapter Submission
Editorial Advisory Board Members
(Confirmed)
Prof. Sophia Ananiadou,University of Manchester,UK
Dr. Christopher Brewster, Aston University,UK
Dr Philipp Cimiano University of Bielefeld, Germany
Prof. Tharam Dillon, Curtin University of Technology, Australia
Assoc. Prof. Chunyu Kit, City University of Hong Kong,Hong Kong
Dr Venkata Subramaniam, IBM India Research, India
Inquiries and submissions can be forwarded electronically (Word document):
Wilson Wong
School of Computer Science and Software Engineering
M002 University of Western Australia
35 Stirling Highway
CRAWLEY 6009 WA
Australia
Fax: +61-8-6488-1089
E-mail: wilson@csse.uwa.edu.au
http://ontology.csse.uwa.edu.au/editedbook