Ontology Maturing for Searching, Managing, and Retrieving Resources

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

FZI Research Center for Information TechnologiesKarlsruhe, GERMANY

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

Simone Braun, Andreas Schmidt,Andreas Walter, Valentin Zacharias

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Problem & Research Question

How to improve searching in, managing of, and retrieving ofresources through the use of

(semantic) annotations

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

Motivation and Current Approaches

Motivation for our approach comes fromdeficiencies in current systems:

• Tagging, its advantages and its problems

• Semantic annotation, its advantages and itsproblems

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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 waysto write words, tags at different levels

noodle (pasta) vs noodle (swear word)spaghettoni vs vermicellini

noodle vs Nudelspagetti vs spaghetti

SpaghettiCarbonara vs Spaghetti_Carbonarapasta vs spaghetti

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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o hence it’s often out-of date, incomplete, inaccurrate and incomprehensible

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

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Hypotheses

Tagging and semantic annotationapproaches can be combined in a waythat avoids their respective drawbacks

while retaining the advantages

The core concept is the lightweightand simple collaborative evolution of

the ontology used for annotation

More on Motivation: Simone Braun, Valentin ZachariasSocial Semantic Bookmarking, PAKM 2008

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Structure of Work & Presentation

ProcessModel

Implement. Evaluation

Iterative Co-DependentDevelopment

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ProcessModel



Implementations:• Image annotation

with ImageNotion• Web resource

annotation withSOBOLEO

Process Model:• Ontology Maturing

model to explain collaborative ontologydevelopment processes and guide tool development

Evaluation:• Multiple

evaluations tovalidate processmodel & toolsand to guide tooldevelopment

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ProcessModel



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Quality of a Collaboratively Created Ontology

Good ontologies for semantic applications are a balance ofAppropriateness• 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 worldo the vocabulary (ontology elements) to describe the world

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

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Process of Ontology Maturing

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

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Users annotate resources with arbitrary tagsNew concept ideas emergee.g. recent/specific tags like ‘whole grain spaghetti’

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A common terminology evolves through the collaborative (re-)usage of the tags

Tags are defined and refined, useless or incorrect ones are rejectede.g. adding German ‘Vollkornspaghetti’ and a description

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Community members begin to organize the concepts with hierarchical & ad hoc relations

resulting in a lightweight ontologye.g. ‘spaghetti’ <is broader> ‘whole grain spaghetti’

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Adding axioms allows for exploiting relationships forreasoning

Users add more precise relations between entites; such aspartonomic relations, disjunction etc. e.g. ‘water‘,‘semolina‘ <is part of> ‘spaghetti‘

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

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

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The Artifact Dimension

Artifacts – a product of human conceptionThat mature from simple tags to formalized or evenaxiomatized ontology elements

The artifact dimension identifies available ontologyelements and their relations


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Knowledge Dimension

The knowledge dimension is concerned with theknowledge of the users that ultimatively determineswhat 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


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Social Dimension

The social dimension is concerned with socialstructures & processesUsers need to learn to collaborateOn 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.


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ProcessModel



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ImageNotion: Semantic Image Annotation & Search

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


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An imagenotion represents a semantic notion graphically through an imageGuides the process of visually creating an ontology that contains imagenotions and relationsAllows for collaborative creation and maturing of ontologies Allows for semantic annotation of images & maturing of their quality


Usage of imagenotions for semantic image annotation

• Imagenotion

Imagenotion

2. Consolidation in communities

• Descriptive• Textual

- Label text• - Synonyms• Date information• Links

VisualAssociate an

image

3. Formalization: Rules and relations

1. Createimagenotions

Emergence of newideas

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SOBOLEO: Social Semantic Bookmarking of Webpages

Use CaseSupporting 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



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SOBOLEO: Social Semantic Bookmarking of Webpages

Use CaseSupporting 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



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ProcessModel



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

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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


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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“


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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 helpExtend 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

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Conclusions

Sustainable community-driven semantic applications need thefacilities such that (almost) all parts of the semantic model can beevolved by the community

The Ontology Maturing process model describes the maturingprocess of the semantic model at the artifact, social, andknowledge dimension

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

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


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Contact


Simone Braun

FZI Research Center for Information Technologies

Karlsruhe, GERMANY

[email protected]

http://fzi.de/ipe

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

http://www.imagenotion.com

http://tool.soboleo.com

Education

Ontology Maturing for Searching, Managing, and Retrieving Resources