Karlsruhe Use Cases - Better late then never ...

Hi all !

again, apologies for the late delivery. But travel responsibilities
resulted
in being offline for the last 3 weeks and I didn't get registered
before...

Here they are...

Best regards,

Raphael

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Karlsruhe Use Cases:

1)	Classical Ontobroker and SHOE stuff:
	Annotating web pages with additional metadata is used to
	explicitly "type" the information using an ontology. This
	approach can be (and was successfully) applied to provide
	querying on a semantic level (instead on a keyword level)
	and improve query results. Queries essentially operate on
	data instances instead of full-text
	This was used in the seminal AIFB KA2portal, and the German
	GETESS project. The annotations can also be used by
	automated agents to fulfill tasks.


2)	In the first scenario ontologies provide a single, common
vocabulary
	that acts as a communication basis between humans and machines,
	viz. as a semantic data model for integration of heterogeneous
data
	and as a common business terminology that is used.

	In the FZI research project "Ontologging" goes beyond this
single
        common vocabulary within a department. A challenging problem
        is the global knowledge access through different departments
        supporting different vocabularies, e.g.  establishing
        communication between the finance and the human resource
department,
        on the human and on the machine level. The idea behind this
approach
        is that people use their own ontololgy, but mappings between
	different ontologies (in our case to other departments) are
discovered automatically
	and proposed to the users within the use of the system.
	The user may than decide if the mapping should be explicitly
represented or neglected.
	The ontology language must then be able to express (or atleast
facilitate) such mappings
	(i.e. DAML+OIL equivalent can be used to express equivalence of
classes / properties).

3)	Additionally to this, one could also think of applications that
make their
	business rules or rules that are described in natural language
explicit
	by representing it in machine-understandable way.
	Think for example of a biological
	community that does research on "trees" - one could publish
research results
	about whether a certain type of tree grows better with a certain
soil, with
	certain amounts of light, etc.
	Here, a web ontology language would also need the ability to
represent "rules".
	Then, some researcher could use the ontology to discover the
results of other research
	agencies in goal-directed queries and augment his own
"knowledge" with the discovered rules.
	Also machine agents could use these rules in their automated
reasoning enhancing the
	problem solving capabilites. For example a machine could
determine which interval and
	quantity of water is best to be used when taking care of plants
(i.e. while the owner is on travels).

4)	In the portal for the EU-supported OntoWeb network of excellence
on Ontologies and
	Knowledge Management ontologies are used very much like in the
seminal Ka2Portal
	that is to provide semantic querying. Additionally ontologies
provide a way to
	syndicate the content published by the members. Here, classes
and relations are
	used to describe structured content types (e.g. Publication,
News (RSS), Event, ...)
	these content types are then gathered from the syndicated web
sites to form the
	content of the portal. For this application reification on a
model level, that is
	on a group of statements, is required to denote who is the
author of a certain
	piece of content. So http://foo/bar might publish some events
and these events
	are displayed within ontoweb.org. This content type is then
augmented
	with author/publisher information (we use DC). Sophisticated
publish/review
	mechanisms exist to control the published contents
	.

5)	Web ontology usage within the EU-funded On-To-Knowledge project
	(provided by York Sure (sure@aifb.uni-karlsruhe.de)):
	Case study 1: Organizational Memory

Swiss Life (Switzerland) is a large insurance company serving
customers around the world. Their vision is to build an
organizational memory with an intranet based portal that offers a
single entry point to the knowledge space of the company. The case
study explores different parts of their intranet:

1.	Skills management makes skills of employees explicit. Within the
case study existing skill databases and documents (like e.g. personal
homepages) will be integrated and expanded. Two aspects are covered
by the case study: first, explicit skills allow for an advanced
expert search within the intranet. Second, one might explore his
future career path by matching his current skill profile vs. job
profiles. The skill ontology serves as a shared conceptualisation of
relevant skills for profiles.
2.	The International Accounting Standards (IAS) document is part of
the global Swiss Life Intranet. The content of the document is highly
specialized and even trained people hardly find relevant passages,
even though there is a division into chapters and sections. Providing
sophisticated access to the IAS is the second goal of this case
study.

Case study 2: Call Centre Desktop Support

British Telecom (United Kingdom) is a leading company on the telecom
market. An important segment of their service portfolio are call
centres. High quality information is needed to be able to answer
complex questions of customers. Every transaction should emphasize
the uniqueness of both the customer and the customer service person,
i.e. call centre agents. Therefore agents need to share their
knowledge effectively with colleagues so that existing solutions from
other agents are available to all agents. In the case study an
ontology serves to structure the knowledge space by providing a
shared and agreed upon model. The goal of the case study is to enable
a shared knowledge space with sophisticated access to existing
solutions.

Case study 3: Virtual Organisation

EnerSearch (Sweden) is a virtual organization founded by a consortium
of major energy providers researching new IT-based business
strategies and customer services in deregulated markets [10]. Its
research affiliates and shareholders are spread over many countries
(e.g. US, Sweden, Germany ^Å). Essentially EnerSearch creates
knowledge which is then transferred to shareholders and other
interested parties. Goal of the case study is to enhance the
knowledge transfer to researchers in different disciplines and
countries, and to specialists from shareholding companies interested
in getting up-to-date information about R&D results on IT in Energy.
Ontologies will help to enable a content based search on research
topics. An important aspect of the case study is an initial
experiment where end-users compare different search methods including
traditional keyword-based search, ontology-based querying and
ontolgoy-based brwosing.

6)	Ontology usage for text / data mining
	(Hotho et al. -
http://www.aifb.uni-karlsruhe.de/WBS/Publ/2001/hothoetal.pdf)
	Ontologies can be used to bring background into machine learning
and explain
	the results of learning algorithms. For example, text clustering
	typically involves clustering in a high dimensional space, which
appears difficult with regard to virtually all practical settings. In
addition, given a particular
       clustering result it is typically very hard to come up with a
good explanation of why the text clusters have been constructed the way
they are.
       We developed views on the high dimensional data basing our
selection of features on a heterarchy of classes.
       The results can be distinguished and explained by the
corresponding selection of classes in the ontology.

7)	Combination of ontologies and web mining -> Semantic Web mining
	(provided by Gerd Stumme (stumme@aifb.uni-karlsruhe.de))

The Semantic Web and Web Mining are two fast-developing
research areas which have a lot of points of contact. Results of Web
Mining can be improved by exploiting the new semantic structures in
the web; on the other hand, Web Mining can be used for building the
Semantic Web. Ontologies play an important role in this integration
task.  For Web Mining, the
levels from XML and RDF to ontologies and logics are of particular
interest. Web Mining applies data mining techniques on the web.
Three areas can be distinguished: Web usage mining analyzes
user behavior, web structure mining utilizes the hyperlink
structure, and web content mining exploits the contents of the
documents on the web.
Semantic Web Mining aims to brings together
research on the different represenation levels of the Semantic Web,
and on the different Web Mining approaches. First results on this
trend have been presented at the Semantic Web Mining Workshop at the
12th European Conference on Machine Learning (ECML'01)/5th European
Conference on Principles and Practice of Knowledge Discovery in
Databases (PKDD'01) in September 3-7, 2001, at Freiburg, Germany
<http://semwebmine2001.aifb.uni-karlsruhe.de/>

8)	Issues in Semantic Annotation
	(provided by Siegried Handschuh
(handschuh@aifb.uni-karlsruhe.de))
	Richly interlinked, machine-understandable data constitutes the
basis for the Semantic Web. Annotating web documents is one of the
major techniques for creating metadata on the Web. However,
annotation tools so far are restricted in their capabilities of
providing richly interlinked and truely machine-understandable
data. They basically allow the user to annotate with plain text
according to a template structure, such as Dublin Core. We here
present CREAM (Creating RElational, Annotation-based Metadata), a
framework for an annotation environment that allows to construct
\emph{relational metadata}, i.e.\ metadata that comprises class
instances and relationship instances. These instances are not
based on a fix structure, but on a domain ontology. We discuss
some of the requirements one has to meet when developing such a
framework, e.g.\ the integration of a metadata crawler, inference
services, document management and information extraction, and
describe its implementation, Ontomat  a component-based,
ontology-driven tool.