The phenomenon of vagueness, manifested by terms and concepts like Tall, Red, Modern, etc., is quite common in human knowledge and it is related to our inability to precisely determine the extensions of such terms due to their blurred applicability boundaries. In the context of Ontologies and Semantic Web, vagueness is primarily treated by means of Fuzzy Ontologies, namely extensions of classical ontologies that apply truth degrees to vague ontological elements in an effort to quantify their vagueness and reason with it. Nevertheless, while a number of fuzzy conceptual formalisms and fuzzy ontology language extensions for representing vagueness in ontologies have been proposed by the community, the methodological issues entailed within the development process of such ontologies have been rather neglected. In this talk we position vagueness within the overall lifecycle of semantic information management and we present IKARUS-Onto, a methodology for engineering fuzzy ontologies that covers all typical ontology development stages, from specification to validation.

1. 1 l July 15, 20131 l
Dealing with Vagueness in Ontologies and Semantic
Data – A Methodological Perspective
Dr. Panos Alexopoulos
Senior Researcher in Semantic Technologies
iSOCO S.A.
University of Aberdeen
9/4/2013

2. What will I talk about
Semantic Web and Information Management
The IKARUS-Onto Methodology
Roadmap & Ongoing Research
Vagueness in Semantic Information

3. What will I talk about
Semantic Web and Information Management
The IKARUS-Onto Methodology
Roadmap & Ongoing Research
Vagueness in Semantic Information

4. The Semantic Web
„The Semantic Web is an extension of the
current web in which information is given well-
defined meaning, better enabling computers and
people to work in co-operation.“
[Berners-Lee et al., 2001]
„The Semantic Web is a collaboration of the
World Wide Web Consortium (W3C) and others
to provide a standard for defining structured
data on the Web.“

5. The Semantic Web Vision
Use the Web like a single global database
Move from a Web of documents to a Web of
Data
MovieDBMovieDB
CIA
World
FactBook
CIA
World
FactBook
But how can we
integrate all this
information?
Slide by Boris Villazon-Terrazas

6. 1. By structuring and interlinking web information
Global Identifier: URI (Uniform Resource Identifier), which is a string of
characters used to identify a name or a resource on the Internet.
http://cia.../Bolivia
http://imdb.../TLLuvia
Data Model: RDF (Resource Description Framework), which is a standard model
for data interchange on the Web
http://.../population
http://.../name
8000000
“Even the Rain”
http://.../filming_location
MovieDBMovieDB
CIA
World
FactBook
CIA
World
FactBook
Slide by Boris Villazon-Terrazas

7. 2. By adding meaning with ontologies
„Ontologies are explicit descriptions of domains ...
... that establish a joint terminology between members of a community
of interest (human or machines)...
... by standardizing and formalizing the meaning of terms …
... through the definition of concepts, relations and axioms“

8. Linked Open Data (2011)
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

9. Semantic Web Information Management & Access Paradigm
Streaming resources Slide by Boris Villazon-Terrazas

10. What will I talk about
Semantic Web and Information Management
The IKARUS-Onto Methodology
Roadmap & Ongoing Research
Vagueness in Semantic Information

11. Vagueness
„Vagueness is a semantic phenomenon where predicates admit
borderline cases, namely cases where it is not determinately true
that the predicate applies or not“
[Shapiro, 2006]
This happens when predicates have blurred
boundaries:
•What’s the threshold number of years
separating old and not old films?
•What are the exact criteria that distinguish
modern restaurants from non-modern?

12. Vagueness in human communication
I am telling you this is a
strategic client for the firm
with large-budget projects!
Come on, $300,000 in
two years is hardly a
large budget!

13. Vagueness in human computer interaction
I would like an
inexpensive modern
restaurant near the city
centreThere is a restaurant
3km away, is that
near or not?

14. Why care about vagueness in the Semantic Web?
● To improve semantic information access: Human users will probably
never stop using vague terminology, so systems need to learn to deal
with it.
● To improve semantic interoperability: Semantic information is based
on human knowledge and the latter’s vagueness can always cause
disagreements and meaning misalignments.
● To increase data coverage: Trying to avoid vagueness in semantic
data can exclude really useful knowledge.

15. What will I talk about
Semantic Web and Information Management
The IKARUS-Onto Methodology
Roadmap & Ongoing Research
Vagueness in Semantic Information

16. Requirements for handling and managing vagueness
●Detect, identify and analyze vagueness in
information and knowledge sources.
Vagueness
Recognition
Vagueness
Modeling
Vagueness
Exploitation
●Conceptualize and semantically represent vague
information in an explicit, shareable and
machine-processable way.
●Take advantage of the modelled vagueness to
provide more accurate and complete
knowledge-intensive services to users.

17. The IKARUS Framework
„Imprecise Knowledge
Acquisition, Representation and
Use“
●Methodology for modeling vague domain knowledge
in the form of fuzzy ontologies.IKARUS-Onto
IKARUS-CBR
IKARUS-
Platform
●Fuzzy ontology-based framework for managing and
retrieving information objects in vague domains.
●Software platform for implementing applications that
manage and exploit vague semantic information.

18. IKARUS application
● Intelligent Information Access
● Electronic Libraries
● Decision Support
● Contact Centers
● eParticipation
Application Domains
● Energy Market
● History and Paleography
● Pre-sales Management
● IT Support
● Culture
Knowledge Domains

19. IKARUS-Onto
Fuzzy ontologies extend traditional ontologies
by using Fuzzy Set Theory to quantify
vagueness through degrees of truth:
•E.g. being 36 years old is considered young
to a degree of 0.4.
IKARUS-Onto is a methodology that defines a
structured process for modeling vagueness with fuzzy
ontologies easier, faster and more effectively (according
to our experiments)

20. IKARUS-Onto
1. People often confuse vagueness with uncertainty (in the sense of probability),
inexactness, ambiguity etc.
● Most frequent question in ESWC 2010 poster session while presenting a
fuzzy ontology: “Are these degrees probabilities?”
2. Domain experts and/or ontology users cannot really understand what fuzzy
degrees are supposed to represent and thus decide/judge their values.
● Most frequent question/claim by historians when asking them to populate
the fuzzy relation “hasPlayedImportantRoleInEvent”: “What are the
criteria of importance?”
3. Guidelines and practices for documenting design decisions in traditional
ontology engineering have not evolved so as to cover fuzzy ontologies as
well.
Experiences from developing and working with fuzzy ontologies

21. IKARUS-Onto
1. Ontology engineers and domain experts should be able to identify easily and
correctly the domain knowledge that is vague.
2. Domain experts and/or ontology users should intuitively decide or judge
which should approximately be the values of the ontology’s fuzzy degrees.
3. The ontology’s fuzzy degrees should reflect the interpretation of the domain’s
vagueness as accurately as possible.
4. The fuzzy ontology should be comprehensible and shareable among human
users through the explicit documentation of the intended meaning of the
vagueness’s elements and their degrees.
Goals

22. IKARUS-Onto Steps
Acquire Crisp Ontology
Establish Need
for Fuzziness
● Establish a basis for the development of
the fuzzy ontology.
● Develop or acquire the crisp ontology.
● Justify and estimate the necessary work
for the fuzzy ontology development.
● Ensure existence of vagueness in the
domain.
● Ensure vagueness is a requirement.
● Conceptualization of vagueness in an
explicit way and shareable way.
● Define fuzzy ontology elements
● Define or generate fuzzy degrees and
membership functions.
● Make fuzzy ontology machine-
processable.
● Select fuzzy ontology language and use it
to represent the defined elements.
● Ensure adequate and correct capturing of
the domain’s vagueness
● Check correctness, accuracy,
completeness and consistency.
Step Goals Actions
Define Fuzzy
Ontology Elements
Formalize Fuzzy
Elements
Validate Fuzzy
Ontology

23. Clarifying Vagueness
● Vagueness involves predicates that admit
borderline cases namely cases where it is
unclear whether or not the predicate
applies.
● E.g. Tall, Near, Expert, Modern etc.
Definition
● Uncertainty: “Today it might rain”
● Inexactness: “Paul is between 25 and 30
years old”
Confused Notions
● Degree vagueness: The existence of
borderline cases stems from the lack (or at
least the apparent lack) of precise
predicate applicability boundaries along
some dimension.
● E.g. Bald, Tall etc.
● Combinatory vagueness: The predicate
has many applicability conditions, yet it is
not possible to determine which of these
are sufficient for its application.
● E.g. Religion, Expert, Strategic etc.
Vagueness Types

24. Detecting Vagueness in Ontologies
● A concept is vague if it admits borderline
cases, i.e. if there are (or could be)
individuals for which it is indeterminate
whether they instantiate the concept.
● Usual suspects:
● Concepts that denote some phase or
state (e.g Adult, Child)
● Attributions, namely concepts that
reflect qualitative states of entities
(e.g., Red, Big, Broken etc.)
Vague Concepts
● Such terms are identified by considering
the ontology’s attributes and assessing
whether their potential values can be
expressed through vague terms.
● E.g. gradable attributes such as size or
height give rise to terms such as large, tall,
short, etc.
Vague Datatype Terms
● A relation is vague if there are (or could be)
pairs of individuals for which it is
indeterminate whether they stand in the
relation.
Vague Relations

25. Fuzzy Ontology Elements
● A fuzzy ontology concept may have
instances that belong to it at certain
degrees.
● E.g. “John is a TallPerson to a degree of
0.5”.
Fuzzy Concepts
● A fuzzy ontology relation links pairs of
concept instances to certain degrees.
● E.g. “John is expert at Machine Learning
to a degree of 0.9”.
● Similarly, a fuzzy attribute assigns literal
values to concept instances at certain
degrees.
Fuzzy Relations and Attributes
● A fuzzy datatype consists of a set of vague
terms which may be used within the
ontology as attribute values.
● E.g. Low, Average, High for the attribute
Project Budget.
● Each term is mapped to a fuzzy set that
defines the term’s meaning.
Fuzzy Datatypes

26. Defining Fuzzy Ontology Concepts/Relations
Identify Element
Determine Vagueness
Type
● Competitor ● belongsToFilmGenre
● Degree Vagueness ● Combinatory Vagueness
● Degree vagueness in the
dimension of the number of
common technologies.
● Lack of minimum concrete criteria
for classifying films to a given genre
● The degree to which the number
of common technologies make
the given company a competitor
● The degree to which the film’s
characteristics classify it to the given
genre.
● “Company X is a competitor to a
degree of 0.7”
● “The Shining is a horror film to a
degree of 0.8”
Step Example 1 Example 2
Describe Vagueness Meaning/Source
Describe fuzzy degree interpretation
Generate Fuzzy Degrees

27. Defining Fuzzy Ontology Datatypes
Identify Datatype
Identify fuzzy datatype
terms
● Project Budget ● Consulting Experience
● Low, Average, High ● Junior, Senior, Veteran
Step Example 1 Example 2
Generate Term
Membership Functions

28. Fuzzy Ontology Formalization and Validation
● Typically extensions of description logics:
● Fuzzy OWL 2 (Bobillo & Straccia)
● Fuzzy OWL 2 QL (Pan et al)
● …
● Important choice parameters:
● The range and expressivity of
supported fuzzy ontological
elements
● The range of supported fuzzy
reasoning capabilities
● Supporting tools like editors,
reasoners etc.
Fuzzy Ontology Languages
● Correctness: All the fuzzy ontology
elements convey a meaning which is
indeed vague
● Accuracy: The fuzzy degree are perceived
as natural and relatively accurate by those
who use the ontology.
● Completeness: All the vague elements
have been identified and represented within
the ontology.
Vague Relations

29. IKARUS-Onto Evaluation Process
Formation of 3 teams
IKARUS-Onto Training
● Team 1 to develop a fuzzy ontology without IKARUS-Onto
● Team 2 to do the same with IKARUS-Onto
● Team 3 to validate and compare the two resulting ontologies
● Teams 2 and 3 are trained in using IKARUS-Onto
● Paralled development of the same fuzzy ontology by teams 1 and 2
● Team 3 validates and compares the 2 developed ontologies
● Evaluation of the whole process by the 3 teams
Step Description
Fuzzy Ontology
Development
Fuzzy Ontology
Validation
Feedback & Evaluation

30. IKARUS-Onto Evaluation Process
● Knowledge engineers and domain experts of the teams that were trained in the
methodology:
1. How easy did you find the task of becoming familiar with the whole process and
applying it in practice?
● Domain experts of the two developing teams:
1. How easy was it for you to identify vague knowledge within the given ontology?
2. How easy was it for you to assign fuzzy degrees to the defined fuzzy elements?
● Knowledge engineers of the two developing teams:
1. How easy it was for you to guide the domain experts in their tasks (identification
of vague knowledge and assignment of fuzzy degrees)?
Evaluation Questions

31. IKARUS-Onto Evaluation Process
● Knowledge engineer of the evaluation team:
1. How easy was for you to determine the criteria of the validation and evaluation
process when you weren’t aware of IKARUS-Onto?
● Domain experts of the evaluation team:
1. Given the validation criteria of IKARUS-Onto, but not the rest of the
methodology, how easy was it for you to perform the validation?
2. How easy was it after knowing the whole IKARUS-Onto?
● Which ontology was easier to validate and how did each ontology perform in terms of
completeness, correctness, and accuracy?
Evaluation Questions

32. IKARUS-Onto Evaluation Results

33. IKARUS-Onto Evaluation Results

34. IKARUS-Onto Evaluation Results

35. What will I talk about
Semantic Web and Information Management
The IKARUS-Onto Methodology
Roadmap & Ongoing Research
Vagueness in Semantic Information

36. Vagueness in the Semantic Information Lifecycle
Information &
Knowledge
Management
Business
Intelligence
Interactive
Systems
Semantic
Publishing and
Linked Data
Semantic
Information
Reuse
Semantic
Information
Retrieval
Conceptual and
Ontological
Modeling
Representation
Languages and
Standards
Modeling
Methodologies
and Processes
Model &
Represent
Vague
Semantic
Information
Use & Exploit Share & Access
Acquire &
Generate
Semantic
Information
Extraction
Knowledge
Elicitation &
Acquisition
Ontology
Learning

37. Current ongoing research
● Problem: Manual definition of the degrees and membership functions of the
fuzzy elements is difficult:
● Too many!
● High level of subjectivity.
● Context dependence.
● Changing interpretations
● Idea: Utilize application-specific user input:
● Develop, initialize and deploy the fuzzy ontology.
● Define and use an application-dependent mechanism for generating and
gathering vague assertions.
● E.g. in the project PARLANCE we use dialogues between the
system and the users to elicit vagueness-related feedback.
● Use the vague assertions to generate fuzzy degrees and membership
functions for the respective elements.
1. Automating the fuzzy degree acquisition process

38. Current ongoing research
● Problem: Comprehensibility and shareability of (both crisp and fuzzy)
ontologies is hindered by the lack of adequate description/documentation of
their vagueness’s characteristics:
● Users often disagree with the existing definitions of vague elements
● User often misinterpret the intended meaning of a vague term and use it
wrongly.
● Idea: Define and use a vagueness meta-ontology to describe and share the
characteristics of vague elements:
● Vagueness Type
● Vagueness Dimensions
● Applicability Context
● …
2. Making ontologies vagueness-aware

39. References
● P. Alexopoulos, M. Wallace, K. Kafentzis, D. Askounis (2011), “IKARUS-Onto:
A Methodology to Develop Fuzzy Ontologies from Crisp Ones”, Knowledge
and Information Systems, Volume 32, Issue 3, Page 667-695
● P. Alexopoulos (2013), “Engineering Fuzzy Ontologies for Semantic
Processing of Vague Knowledge”, Semantic Multimedia Analysis and
Processing, CRC Press, 2013

40. Thank you for your attention!
Dr. Panos Alexopoulos
Senior Researcher in Semantic Web Technologies
Email: palexopoulos@isoco.com
Web: www.panosalexopoulos.com
LinkedIn: www.linkedin.com/in/panosalexopoulos
Twitter: @PAlexop