Chapter
Proposals Submission Deadline: 20/07/2010
Full
Chapters Due: 30/10/2010
Learning structure and schemas from documents
A
book edited by:
Dr
Marenglen Biba,
Dr
Fatos Xhafa,
To be published in the “Studies in
Computational Intelligence” book series, Springer (2011)
http://www.marenglenbiba.net/cfc/
The
rapidly growing volume of available digital documents of various formats and
the possibility to access these through internet-based technologies, have led
to the necessity to develop solid methods to properly organize and structure
documents in large digital libraries and repositories. Specifically, since the
extremely large volumes make it impossible to manually organize such documents
and since most of the documents exist in an unstructured form and do not follow
any schemas, most of the efforts in this direction are dedicated to automatically
infer structure and schemas that can help to better organize hue collections.
This is essential in order for these documents to be effectively and
efficiently retrieved.
Dealing
with unstructured information is a hot research. A growing body of work is
addressing the problem of recognizing structure and schemas in documents of
various types. Some areas are mainly concerned about the visual representation
of documents and increasing improvements are being made in the area of pattern
recognition and document layout analysis to classify documents according to
structure found in their layout. On the other side, extensive research is being
done in the field of machine learning to exploit attributes of documents and
relationships among different documents to infer structures in large
collections of documents. Important work is also being performed in the data
mining and knowledge discovery community which has traditionally dealt with raw
data but recently is dedicating attention to learning structure from
unstructured information. In addition, Semantic Web researchers are dedicating
important efforts to the problem of identifying structure and schemas in order
for them to achieve ontology matching or alignment. Another related area
regards the database community that has long worked with integration problems
but only recently this community has started considering automatic structure
and schema learning as a potential approach for schema and database
integration. Finally information retrieval and extraction seek to infer
structure and schemas from free text in order to build efficient information
seeking models from large corpora.
The goal of this book is to present state-of-the-art
methods for structure learning and schema inference. Most of the existing
fields and technologies have long worked mainly in an isolated fashion even
though the tasks they solve have much in common. This has led to a stall of the
overall advancement to solving the problem, even though separate fields improve
their performances independently on specific datasets. The automatic inference
of structure is central to all approaches to organizing documents, therefore it
has become important to bring together researchers from different fields and
identify common challenges in order to advance the state-of-the-art in
structure learning from documents. This will make possible the exploitation of
methods developed in one field, from researchers of related fields who might
take advantage of novelties introduced in different fields working on the same
problem of learning structure in documents.
The book appreciates that an
understanding of the interactions between various approaches is essential to
develop synergies among different research areas in order to develop more
robust methods that can attack the problem in a multi-strategic fashion. Thus
the focus of this book is on:
Although
contributions will be open from both academia and industry practitioners and
researchers, the audiences of this book are those working in or interested in
joining interdisciplinary and transdisciplinary works in the areas of data
mining, machine learning, pattern recognition, document analysis and
understanding, semantic web, databases. artificial intelligence and digital
libraries, whose mainly focus is that of learning structure and schemas from
unstructured information. The application areas are also very broad and
contributions will be open for applied works in bioinformatics, web mining,
text mining, information retrieval, real-world digital libraries, data
warehouses and ontology building. Specifically, audiences who are broadly
involved in the domains of computer science, web technologies, applied
informatics, business or management information systems are: (1) researchers or
senior graduates working in academia; (2) academics, instructors and senior
students in colleges and universities, and (3) business analysts from industries
interested in data integration, information retrieval and enterprise search.
Topics:
Chapters
should be written in a manner readable for both specialists and
non-specialists. Chapters could address issues related to past, present and
future theories, methods, and practices of learning structure from documents.
These should be focused on next generation paradigms and with a particular
focus (but not limited) to Structure Learning, Schema Integration, Schema
Inference, Document Analysis and Recognition, Document Layout Analysis,
Document Image Understanding, Data Mining, Data Annotation, Data Integration,
Mining Unstructured Data, Learning Structure from Text, Web Mining, Text
Mining, Document Databases and Digital Libraries, Database Integration, Data warehouse
Integration, Ontology mapping, Ontology merging, Ontology alignment, Ontology
Searching, Ontology Ranking, Ontology Evaluation, Information Retrieval,
Information Extraction.
Recommended topic areas include, but are not limited to:
Submission
is possible only through invitation. Academics, researchers and practitioners
are invited to submit by 20 July 2010, a 2-page manuscript
proposal detailing the background, motivations and structure of their proposed
chapter. Authors of accepted proposals will be notified by 1 August 2010 and will be given
instructions and guidelines for chapter preparation. Full chapters are due on 30 October 2010 and should be of 8,000 words
in length and/or between 25 to 30 pages long. The book is scheduled to be
published in the “Studies in
Computational Intelligence” book series, Springer. For information about the publisher and the book series,
visit http://www.springer.com/series/7092.
This publication is anticipated to be released in 2011.
Important Dates
20 July 2010: 2-page Proposal Submission Deadline
1 August 2010: Notification of
Proposal Acceptance
30 October 2010: Full Chapter
Submission (in Word or PDF)
15 December: Notification of
Full Chapter Acceptance
30 January 2011: Revised Chapter
Submission
30 February 2011: Final
Notification of Acceptance
15 March 2011: Final
Material Submission
Inquiries and
submissions can be forwarded electronically (in Word or PDF) to:
Dr Marenglen Biba
E-Mail:
marenglenbiba@unyt.edu.al
URL: http://www.marenglenbiba.net
or
Prof. Fatos Xhafa
University of London (Birkbeck), United Kingdom
E-Mail:
fatos@lsi.upc.edu