Ontology Maturing for Searching, Managing, and Retrieving Resources

Simone Braun, Andreas Schmidt,
Andreas Walter, Valentin Zacharias

Using the Ontology Maturing Process Model
for Searching, Managing, and Retrieving
Resources with Semantic Technologies

FZI Research Center for Information Technologies
Karlsruhe, GERMANY

{braun|aschmidt|awalter|zach}@fzi.de
http://www.fzi.de/ipe

Problem & Research Question

How to improve searching in,
managing of, and retrieving of
resources through the use of
(semantic) annotations

2

Motivation and Current Approaches

Motivation for our approach comes from
deficiencies in current systems:

• Tagging, its advantages and its problems
• Semantic annotation, its advantages and its
problems

© FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 3

Tagging

The use of arbitrary keywords for managing,
searching, and finding resources
Advantages:
• Lightweight, easy, adaptable,
no setup, proven - used by millions
Disadvantages:
• Lack of precision due to problems like homonyms,
synonyms, multilinguality, typos, different ways
to write words, tags at different levels

noodle (pasta) vs noodle (swear word)
spaghettoni vs vermicellini
noodle vs Nudel
spagetti vs spaghetti
SpaghettiCarbonara vs Spaghetti_Carbonara4
pasta vs spaghetti

Semantic Annotation

The use of (semantically) described entities for
managing, searching, and finding resources
Advantages:
• Through the use of concepts (instead of words) avoids tagging
problems such as homonyms, synonyms etc.
• Potentially better management, searching, and browsing
Disadvantages:
• Despite years of research so far not widely used
• Needs ontology that is used for annotation
o this is often created by different users (KE experts) and updated
only seldomly
o hence it’s often out-of date, incomplete,
inaccurrate and incomprehensible

Hypotheses

Tagging and semantic annotation
approaches can be combined in a way
that avoids their respective drawbacks
while retaining the advantages

The core concept is the lightweight
and simple collaborative evolution of
the ontology used for annotation
More on Motivation: Simone Braun, Valentin Zacharias
6
Social Semantic Bookmarking, PAKM 2008

Structure of Work & Presentation

Process
Model

Iterative
Co-Dependent
Development

Implement. Evaluation

7

Process Model:
• Ontology Maturing
model to explain
collaborative ontology
development processes
and guide tool
Process development
Model
Implementations: Evaluation:
• Image annotation • Multiple
with ImageNotion evaluations to
• Web resource Iterative validate process
annotation with model & tools
Co-Dependent and to guide tool
SOBOLEO
Development development


8


Process
Model

Iterative
Co-Dependent
Development


9

Quality of a Collaboratively Created
Ontology

Good ontologies for semantic applications are a balance of
Appropriateness
• Representation of the domain
• wrt. the purpose of the ontology for the semantic application
• Tight coupling between usage and updating of ontology elements
Social Agreement
• Ontology represents a shared understanding of the community
elaborated in social & collaborative processes
• Learning process of the users
o deepen their understanding of the real world
o the vocabulary (ontology elements) to describe the world

Formality
• Ontology development is a process of continuous evolution
• Different levels of formality might coexist 10

Process of Ontology Maturing

Based on the assumption that ontologies cannot be formalized in a
single activity
Rather the result of continuous negotiation & collaborative
learning processes taking place when applying the ontologies
11


Users annotate resources with arbitrary tags
New concept ideas emerge
e.g. recent/specific tags like ‘whole grain spaghetti’

12


A common terminology evolves through the
collaborative (re-)usage of the tags
Tags are defined and refined, useless or incorrect ones are
rejected
13
e.g. adding German ‘Vollkornspaghetti’ and a description


Community members begin to organize the concepts
with hierarchical & ad hoc relations
resulting in a lightweight ontology
e.g. ‘spaghetti’ <is broader> ‘whole grain spaghetti’
14


Adding axioms allows for exploiting relationships for
reasoning
Users add more precise relations between entites; such as
partonomic relations, disjunction etc.
15
e.g. ‘water‘,‘semolina‘ <is part of> ‘spaghetti‘

The Artifact, Knowledge & Social Dimensions

Concentrating only on the development of the
ontology is not sufficient to create & analyze
community-driven semantic applications

Need to consider that users have different levels of
understanding of parts of the domain and that this
understanding also evolves within usage processes

Viewing ontology development as collaborative learning
processes requires to consider interaction,
communication, and coordination processes
The development of social processes & competencies

The Artifact Dimension

Artifacts – a product of human conception
That mature from simple tags to formalized or even
axiomatized ontology elements

The artifact dimension identifies available ontology
elements and their relations


Knowledge Dimension

The knowledge dimension is concerned with the
knowledge of the users that ultimatively determines
what they can model
On the individual level:
• Alignment processes bringing forth a sufficient level of shared
understanding of the domain
• Learning processes on artifacts creation methods
On the collective level:
• Development of an understanding as such


Social Dimension

The social dimension is concerned with social
structures & processes
Users need to learn to collaborate
On the individual level:
• General willingness & competencies to interact, communicate,
negotiate, compromise, and accept rules
On the collective level:
• The development of rules, best practices, identification of
leaders etc.



Process
Model

Iterative
Co-Dependent
Development


21

ImageNotion:
Semantic Image Annotation & Search

Benefits for image annotation
• Semantics allow for improved navigation through image
archives (e.g. images with the same persons, events)
• Multilinguality, reusability of ontology elements:
saves time for image annotation compared to textual
annotation
Requirements for semantic image annotation
• Work integrated, collaborative creation process of ontologies
• Easy understandability of ontology elements
• Usability and simplicity: tools and work steps must be
informal, lightweight, easy-to-use and easy to understand


ImageNotion:

An imagenotion represents a semantic notion graphically through
an image
Guides the process of visually creating an ontology that contains
imagenotions and relations
Allows for collaborative creation and maturing of ontologies
Allows for semantic annotation of images & maturing of their
quality Imagenotion
• Imagenotion
1. Create 2. Consolidation in communities
imagenotions
Emergence of new • Descriptive
ideas • Textual
- Label text
Visual • - Synonyms
Associate an • Date information
image • Links
Usage of imagenotions
for semantic image
annotation
3. Formalization: Rules and relations


ImageNotion:


SOBOLEO:
Social Semantic Bookmarking of Webpages

Use Case
Supporting knowledge workers working together in one domain in
developing a shared ontology and a shared index of relevant web
resources organized with this ontology

Course
• Users encounter a web page
• Annotating with concepts from the ontology or arbitrary tags
• Gathering arbitrary tags as “prototypical concepts” for later
consolidation and placement
• Or immediate switch to the ontology editor
o e.g. for adding synonyms or structuring with broader/narrower/
related relations



Process
Model

Iterative
Co-Dependent
Development


29

Evaluations - Overview

5 Evaluations:
(S1) CKC Workshop @ WWW 2007
• 33 participants
• 202 concepts, 393 relations, 155 resources, ∅ 3 concepts per
resource
(S2) Workshop of the IM WISSENSNETZ project
• 4 participants with no modeling background
• Guided user tests with observation, thinking aloud, interviews,
questionnaires & screenrecording
(S3) Workshop @ EATEL SummerSchool 2008
• 24 participants with mixed background (CS, pedagogy etc.)
• 182 concepts, 323 relations, 76 resources, ∅ 3 concepts per
resource

Evaluations - Overview

(I1) Online survey
• 137 participants
• Task: create imagenotion „Manuel Barroso“

(I2) Workshop of the IMAGINATION project
• 3x6 participants (Wikipedia users, French image agency
employees, Italian history students)
• Task: annotate historical images according to the users area of
expertise


User Acceptance & Usefulness

Majority of users appreciated both tools and we could
show that people from a variety of backgrounds are
able to understand & interact with semantic
annotation

Users liked in particular
• the ease of use of ontology editing
• the simple way for annotating with concepts & tags
• possibility to integrate not yet well defined concepts
• having ”starter concepts” & “to get the ontology building
almost for free“


Evaluation Results II

Evaluations also uncovered interesting effects showing
importance of social and knowledge dimension, e.g.:

Mutual Support
• Specialists for tool use or domain areas quickly emerged and
were asked by others for help
Extend tools to support users in identification and contacting
these specialists
Interest in Background Knowledge
• Users showed great interest in learning more about the subject
matter of the current resources they were annotating (e.g. by
looking things up in Wikipedia)
Encourage and extend tools to support this, e.g. by automatically
adding texts from wikipedia as tag descriptions
33

Conclusions

Sustainable community-driven semantic applications need the
facilities such that (almost) all parts of the semantic model can be
evolved by the community

The Ontology Maturing process model describes the maturing
process of the semantic model at the artifact, social, and
knowledge dimension

SOBOLEO and ImageNotion implement these vision of community-
driven semantic applications and have been favourably received by
users in multiple evaluations

For the future we plan more long-term evaluations to further
validate the model and improve the tools

Contact

http://imagination-project.org
http://mature-ip.eu

http://www.imagenotion.com

Simone Braun
FZI Research Center for Information Technologies
Karlsruhe, GERMANY
Simone.Braun@fzi.de
http://tool.soboleo.com
http://fzi.de/ipe

Ontology Maturing for Searching, Managing, and Retrieving Resources

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