2nd Call for Chapter - Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances
Wilson Wong
(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
========================================================================================
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
wil...@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: wil...@csse.uwa.edu.au
Up-to-date information about this call is available at
http://explorer.csse.uwa.edu.au/editedbook