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 Many
on-line documents, such as HTML, XML, Latex, SGML and those found in
digital libraries, are semistructured. With the continuous growth in
semistructured data sources, the ability to manage collections of
these documents for improving the quality of information exchange
becomes increasingly important. Employing the power of automated
reasoning to guide access to these resources requires
machine-processable representations of the semantics of these
resources.
Ontology mining and
knowledge discovery techniques are required to explicitly represent
semantics of semi-structured information. The lack of formal
semantics makes it difficult to make the best use of the massive
amount of stored semi-structured data. Mining of structure along with
semantics provides new insights and means into the process of
accessing these semistructured resources. Due to the inherent
flexibility of the semi-structured documents, in both structure and
semantics, mining for ontology and knowledge from them is faced with
new challenges as well as benefits.
Recognising the
increasing interest and need to manage semistructured resources, this
workshop aims to provide a stimulating forum for researchers to
discuss new and interesting algorithms, applications and issues of
ontology mining and knowledge discovery from semistructured
documents.
Moreover, this
workshop intends to bring together researchers from knowledge
discovery as well as ontology mining community in order to initiate a
discussion on how to integrate insights from both communities. As
data mining research has already recognised the value of knowledge
discovery from semi-structured resources in the process of building
ontology, links in the opposite direction are rarer.
We solicit papers
with important new insights and experiences of ontology mining and
knowledge discovery from semistructured data. Topics of interest
include, but are not limited to:
a) XML or
semistructured data mining algorithms and techniques
 classification
clustering
association
change
detection
tree
and graph mining
schema
matching/discovery
approximating
queries
b) Ontology mining
algorithms and techniques
ontology
learning
ontology
maintenance
c) semistructured
data mining applications
mining
for bioinformatics
mining
for sensor and networking data
mining
in e-commerce, Web services and others
Convergence
of semi-structured mining with information retrieval
Convergence
of semi-structured mining with Semantic Web and Ontology
d) semistructured
data mining emerging issues and challenges
Security
and privacy
Distributed
mining
e) Benchmarks and mining performance using
semi-structured databases
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