& Workshop Description
Ontologies serve as a means for establishing a conceptually concise basis for
communicating knowledge for many purposes. Previous, successful workshops on ontology
engineering and problem-solving methods have shown that there is a huge interest
in the area of engineering ontologies for a very wide range of interesting applications
and, in fact, the community in that field is steadily growing. Also in recent
years, we have seen a surge of interest in other fields than ontology engineering
that tackle the discovery and automatic
creation of complex,
multirelational knowledge structures. For example, the natural
language community tries to acquire word semantics from natural language
texts, database researchers tackle the problem
of schema induction, and people building intelligent
information agents research the learning of complex structures from
semi-structured input (HTML, XML). All the
while, efforts in the machine learning community pursue the induction of more
concise and more expressive knowledge structures (e.g., relational learning) in
general. Traditionally, there has been only very few interactions between these
groups in spite of the fact that they all try to learn conceptual structures,
which are termed ``schemata'', ``concept
hierarchies'' or ``heterarchies'',
``conceptual patterns'', or ``ontologies''
-- depending on which community you talk to. We aim at furthering, or even establishing,
communication between these communities through our workshop on ontology learning.
Ontology modeling and maintenance is a time consuming task. Human expert modeling by hand is biased, error prone, and expensive.
It is very difficult and cumbersome to manually derive ontologies from data. This appears to be true even regardless of the
type of data one might consider. In the workshop we plan to attract researchers that try to overcome the problem through learning
ontologies from natural language text, semi-structured data (e.g., HTML or XML) or structured data such as found
Natural language texts exhibit morphological, syntactic, semantic, pragmatic and conceptual constraints that interact in
order to convey a particular meaning to the reader. Thus, the text transports information to the reader and the reader
embeds this information into his background knowledge. Through the understanding of the text
data is associated with conceptual structures and new conceptual structures are learned from the
interacting constraints given through language.
Tools that learn ontologies from natural language exploit the interacting constraints on the various language levels (from
morphology to pragmatics and background knowledge) in order to discover new concepts and stipulate relationships between concepts.
We solicit submissions that investigate the combination of natural language processing techniques and machine learning
methods for the learning task.
With the success of new standards for document publishing on the web
there will be a proliferation of semi-structured data and
formal descriptions of semi-structured data freely and widely available.
HTML data, XML data, XML Document Type Definitions (DTDs), XML-Schemata (cf. http://w3c.org),
and their likes add -- more or less expressive -- semantic information
to documents. A number of approaches understand ontologies as a common generalizing level that may
communicate between the various data types and data descriptions. Ontologies play a major role for
allowing semantic access to these vast resources of semi-structured data.
few approaches do yet exist we belief that learning of ontologies from these data and data descriptions
may considerably leverage the application of ontologies and, thus, facilitate the access to these data.
Ontologies have been firmly established as a means for mediating between different
databases. Nevertheless, the manual creation of a mediating ontology is again a tedious, often extremely difficult, task
that may be facilitated through learning methods. The negotiation of a common ontology
from a set of data and the evolution of ontologies through the observation of data is a hot topic these days.
The same applies to the learning of ontologies from metadata, such as database schemata, in order to
derive a common high-level abstraction of underlying data descriptions - an important precondition
for data warehousing or intelligent information agents.
The exchange of experience that comes from these different, newly emerging, subareas
of ontology learning has been neglected so far. We want to stimulate interaction
across these disciplines and initiate a dialogue with research in learning complex
structures in general. In particular, we are also interested in maintenance (revision,
incrementality) and integration (from various sources) aspects of learning ontologies.
We want to further, or even establish, the exchange of ideas between these communities
--- and maybe even others that we have not thought of. Hence, we solicit papers
that present innovative approaches to ontology learning that are to be discussed
in the workshop, system demonstrations and applications or position statements.
|Deadline for paper submission
||1 April 2000
|Notification of acceptance
||1 May 2000
|Deadline final contributions
||1 June 2000
All accepted papers will be published in the workshop proceedings. In addition,
a few selected best papers will be considered for publication in a special
issue about Ontology Learning of Elsevier's
Journal "Data and Knowledge Engineering".
We invite contributions that advance the state-of-the-art in topics related
to the purpose of the workshop. Persons interested in participating should submit
either a technical paper (less than 6000 words) or a position paper (less than
1500 words) addressing new research issues. In addition, we solicit proposals
for panel discussions and break-out groups that work towards visions for ontology
learning. Submit before April 1, 2000 in electronic form (strongly preferred!)
in postscript or pdf in the final ECAI
style format to:
or send three hard copies of your submission to:
© Steffen Staab, firstname.lastname@example.org,
Last change: April 14th, 2000.
Steffen Staab and Alexander Mädche