2nd Call for Chapter - Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances

SECOND CALL FOR CHAPTERS
(Proposals Submission Deadline: 15 DECEMBER 2009)

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

A book edited by Wilson Wong, Wei Liu and Mohammed Bennamoun
University of Western Australia, Australia
http://explorer.csse.uwa.edu.au/editedbook

Introduction
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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
========================================================================================
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
========================================================================================
Dr Christopher Brewster, Aston University, UK
Associate Professor Chunyu Kit, City University of Hong Kong, Hong Kong
Professor Mary-Anne Williams, University of Technology Sydney, Australia
Dr Philipp Cimiano, Technical University of Delft, Netherlands
Professor Sophia Ananiadou, University of Manchester, UK
Professor Tharam Dillon, Curtin University of Technology, Australia
Dr Venkata Subramaniam, IBM India Research, India

Inquiries and Submissions
========================================================================================
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
Up-to-date information about this call is available at
http://explorer.csse.uwa.edu.au/editedbook