Ontology-Based Software Testing

wafflebazaarInternet and Web Development

Oct 21, 2013 (3 years and 7 months ago)

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Ontology
-
based Knowledge
Management
Samad
Paydar
Dept. of Computer Engineering
Email:
samad.paydar@stu
-
mail.um.ac.ir
September
2009
2
2
Ontology

An ontology is an explicit and formal
specification of a conceptualization of a
domain of interest (Gruber
1993
)

An ontology defined the
basic terms
and
relations
comprising the
vocabulary
of a
topic area
as well as the
rules
for
combining terms and relations to define
extensions to the vocabulary
3
Ontology

Ontologies consist of concepts, relations,
instances and axioms.

OWL (Web Ontology Language) a W
3
C
Standard
3
4
4
Ontology

Ontologies are formal

possibility of
transferring domain knowledge in a
machine
-
readable format

knowledge
reuse by different applications, software
systems and human resources

Intelligent systems

Improve inter
-
operability between systems
5
5
Ontology

Tsunami of information


need to machine processable
information

metadata, ontology
My Reference

Semantic Knowledge Management, An
Ontology
-
Based Framework

2009
6
KIWI

Chapter
1
: KIWI: A Framework for
Enabling Semantic Knowledge
Management

KIWI (Knowledge
-
based Innovation for the
Web Infrastructure) project is focused on the
strategies for the current Web evolution in the
more powerful Semantic Web, where formal
semantic representation of resources enables
a more effective knowledge sharing.
7
KIWI

The first pillar of the KIWI framework
concerns development of ontologies as a
metadata layer. Resources can be
formally and semantically annotated with
these metadata, while search engines or
software agents can use them for
retrieving the right information item or
applying their reasoning capabilities.
8
KIWI

The second pillar of the KIWI framework is
focused on the semantic search engine.

A set of prototypal tools that enable
knowledge experts to produce a semantic
knowledge management system was
delivered by the project.

The KIWI framework and tools are applied in
some projects for designing and developing
knowledge
-
based platforms with positive
results.
9
KIWI

The KIWI project was aimed to study the
possibilities to manage huge amounts of
data and information through ontologies
10
KIWI

ontology is an “explicit specification of a
conceptualization”

Ontologies have to be public and
reachable by users (explicit); they
represent a formal and logical description
(specification) of a view of the world
conceptualization) that users are
committed to.
11
KIWI

Ontologies
show their power in two main
applicative contexts:

in managing data by different application
exchanging and processing them, that is,
when many applications are (or might be)
integrated in a technological platform or
system

in searching data, information and knowledge
on the Web.
12
KIWI

A Challenge: The Semantic Web is not
only ontology. Data, information and
service and whatever type of resources
should be indexed using metadata
extracted from the ontology to be
manageable through their semantics.

This is the most complex task for
Semantic Web researchers.
13
KIWI

Applications and methodologies
developed for the KIWI project are
conceived to be integrated in a knowledge
management platform in order to introduce
a semantic layer between knowledge
resources and applications.
14
KIWI

The KIWI project is focused principally on
the issues of defining
ontologies
and on
developing a semantic search engine able
to access the ontology itself and to extract
resources described with semantic
metadata.
15
KIWI

The final aim of the project is to sketch a
methodology for developing ontology and
describing knowledge resources and to
develop an integrated set of tools for
enabling users without deep technological
skills to use them for building their
semantic knowledge base.
16
KIWI

Structure of the tools developed for the
KIWI project
17
KIWI

OntoMaker:

An interface for developing the ontology
18
KIWI

Assertion Maker:

The Assertion Maker is able to open an
ontology and a document (in a “xml” format),
the user can associate to each section of the
document (title, paragraphs or others) a
concept or a triple (subject
-
predicate
-
object)
extracted from the ontology.

A necessary improvement is to introduce a
semiautomatic system for pre
-
processing
information resources.
19
KIWI

Semantic Navigator

empowers the search and retrieval
capabilities. Users can be quite sure that the
results’ list contains documents related to the
assertion built. The assurance is based on the
preciseness of the classification process. But
our system can be further improved.

A “trust evaluation” component is planned to
be added.
20
KIWI

The framework is structured in
3
different
phases
1.
definition and implementation of the ontology
2.
Resource indexing: Having the ontology,
resources need to be indexed or annotated
using the ontology’s concepts.
3.
every day usage of the knowledge
resources.
21
KIWI

Ontology Development

Top
-
down

Bottom
-
up

Middle
-
out

Automatic/Semiautomatic
22
KIWI

Evaluating the ontology:
OntoMeter

ontologies
are a representation of the world.
This implies that ontology has to change
when our knowledge about the world changes
23
KIWI

Three main types of evolutions that can affect
ontologies
1.
Knowledge domain’s evolution

big department is split into two smaller ones
2.
conceptualization’s evolution

changes in the aim with which users look at the world.
A user can see his/her computer from the hardware
point of view or from the software point of view
3.
specification’s evolution

Related to a new implementation in different languages
of the same ontology; it could be necessary for
improving the reasoning capability of the system in
which the ontology is deployed.
24
KIWI

OntoMeter
: a tool aimed at evaluating
some metrics on an ontology implemented
in the RDF language.

3
metrics: depth, extension, and balance
25
KIWI

Depth

a measure of the number of concepts from
the “root” concept to the most specific one; we
think that this is a measure connected to the
details of the world the taxonomy is able to
represent. If you accept that users do not
want to click to many times before reaching
the right concept, a high value of depth
means that (probably) you can break up the
ontology into many more focused
ontologies
in order to improve the user’s experience.
26
KIWI

Extension

Extension is a parameter that evaluates the
number of children
-
nodes; if a concept has
only one sub
-
concept (probably) it means that
the ontological difference among them is not
so significant, and we should re
-
think the
necessity to distinct the classes. At the same
time, if a concept has many (
10
or more)
subclasses, it could be more useful to regroup
them in order to simplify the taxonomy
browsing.
27
KIWI

Balance

Balance tells us if the different branches of
the taxonomy are developed coherently at a
similar level of depth. This metric helps us in
monitoring the continuous adding of details
and specific classes. Moreover, a poor
detailed branch of a taxonomy could be a
symptom of a not so important section of the
knowledge domain or of a not good expertise
available for designing the ontology itself.
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KIWI Application: Virtual eBMS

eBMS
: an advanced business school
working in the e
-
business management
area

Virtual
eBMS
(http://virtual.ebms.it) is a
platform for knowledge management, e
-
learning, and e
-
business.

network of collaborators, firms, and research
centers with which there is a form of
collaboration.
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KIWI Application: Virtual eBMS

This platform enables Master and PhD
students of
eBMS
to perform many tasks of
their daily work, such as accessing scientific
documents and distributing their reports to
themselves and to their tutors. Through the
Virtual
eBMS
, they can also access e
-
learning
courses created by
eBMS
research staff.
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KIWI Application: Virtual eBMS

The project management section proposes
functionalities for organizing projects,
managing people involved in projects, their
deliverables and deadlines, expected results,
and so on.
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Ontology :: Challanges

Interaction with human minds

HCI challenges and visualization

Interplay between human languages and
ontologies

Integration with existing knowledge
organization systems
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Ontology :: Challanges

Managing dynamics of ontologies

Ontology evolution

Interoperability between ontologies

Scalable inferastructure

Economic and legal constraints

Resource consumption

Incentive conflicts and network externalities

Intellectual property rights

Experience
33
Ontology :: Challanges

Security
34