Simone Braun, Andreas Schmidt,
              Andreas Walter, Valentin Zacharias



Using the Ontology Maturing Process Mod...
Problem & Research Question




How to improve searching in,
managing of, and retrieving of
resources through the use of
 ...
Motivation and Current Approaches


   Motivation for our approach comes from
   deficiencies in current systems:

     • ...
Tagging


         The use of arbitrary keywords for managing,
         searching, and finding resources
         Advantag...
Semantic Annotation


The use of (semantically) described entities for
managing, searching, and finding resources
Advantag...
Hypotheses


   Tagging and semantic annotation
 approaches can be combined in a way
that avoids their respective drawback...
Structure of Work & Presentation


              Process
               Model



               Iterative
             Co-...
Process Model:
        Structure of   Work & Presentation
                                  • Ontology Maturing
          ...
Structure of Work & Presentation


              Process
               Model



               Iterative
             Co-...
Quality of a Collaboratively Created
 Ontology

Good ontologies for semantic applications are a balance of
   Appropriaten...
Process of Ontology Maturing




Based on the assumption that ontologies cannot be formalized in a
single activity
Rather ...
Process of Ontology Maturing




Users annotate resources with arbitrary tags
   New concept ideas emerge
   e.g. recent/s...
Process of Ontology Maturing




A common terminology evolves through the
collaborative (re-)usage of the tags
   Tags are...
Process of Ontology Maturing




Community members begin to organize the concepts
with hierarchical & ad hoc relations
   ...
Process of Ontology Maturing




Adding axioms allows for exploiting relationships for
reasoning
   Users add more precise...
The Artifact, Knowledge & Social Dimensions


Concentrating only on the development of the
ontology is not sufficient to c...
© FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe  ...
The Artifact Dimension



Artifacts – a product of human conception
That mature from simple tags to formalized or even
axi...
Knowledge Dimension


The knowledge dimension is concerned with the
knowledge of the users that ultimatively determines
wh...
Social Dimension


The social dimension is concerned with social
structures & processes
Users need to learn to collaborate...
Structure of Work & Presentation


              Process
               Model



               Iterative
             Co-...
ImageNotion:
Semantic Image Annotation & Search

Semantic Image Annotation & Search
Benefits for image annotation
 • Seman...
ImageNotion:
     Semantic Image Annotation & Search

     An imagenotion represents a semantic notion graphically through...
ImageNotion:
Semantic Image Annotation & Search




 © FZI Research Center for Information Technologies Karlsruhe, Germany...
SOBOLEO:
 Social Semantic Bookmarking of Webpages

Use Case
  Supporting knowledge workers working together in one domain ...
© FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe
SOBOLEO:
 Social Semantic Bookmarking of Webpages

Use Case
  Supporting knowledge workers working together in one domain ...
© FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe
Structure of Work & Presentation


              Process
               Model



               Iterative
             Co-...
Evaluations - Overview


5 Evaluations:
  (S1) CKC Workshop @ WWW 2007
  • 33 participants
  • 202 concepts, 393 relations...
Evaluations - Overview


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


(I2) Worksh...
User Acceptance & Usefulness


Majority of users appreciated both tools and we could
show that people from a variety of ba...
Evaluation Results II

Evaluations also uncovered interesting effects showing
importance of social and knowledge dimension...
Conclusions


Sustainable community-driven semantic applications need the
facilities such that (almost) all parts of the s...
Contact




                                                                        http://imagination-project.org
       ...
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Ontology Maturing for Searching, Managing, and Retrieving Resources

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presentation of the paper "Using the Ontology Maturing Proces Model for Searching, Managing, and Retrieving Resources with Semantic Technologies" at the ODBASE 2008 conference, Monterrey, Mexico, Nov 13 2008

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Ontology Maturing for Searching, Managing, and Retrieving Resources

  1. 1. 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
  2. 2. Problem & Research Question How to improve searching in, managing of, and retrieving of resources through the use of (semantic) annotations 2
  3. 3. 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
  4. 4. 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
  5. 5. 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 5
  6. 6. 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
  7. 7. Structure of Work & Presentation Process Model Iterative Co-Dependent Development Implement. Evaluation 7
  8. 8. Process Model: Structure of Work & Presentation • 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 Implement. Evaluation 8
  9. 9. Structure of Work & Presentation Process Model Iterative Co-Dependent Development Implement. Evaluation 9
  10. 10. 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
  11. 11. 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
  12. 12. Process of Ontology Maturing Users annotate resources with arbitrary tags New concept ideas emerge e.g. recent/specific tags like ‘whole grain spaghetti’ 12
  13. 13. Process of Ontology Maturing 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
  14. 14. Process of Ontology Maturing 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
  15. 15. Process of Ontology Maturing 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‘
  16. 16. 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 16
  17. 17. © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 17
  18. 18. 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 © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 18
  19. 19. 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 © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 19
  20. 20. 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. © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 20
  21. 21. Structure of Work & Presentation Process Model Iterative Co-Dependent Development Implement. Evaluation 21
  22. 22. 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 © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 22
  23. 23. ImageNotion: Semantic Image Annotation & Search 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 © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 23
  24. 24. ImageNotion: Semantic Image Annotation & Search © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 24
  25. 25. 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 © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 25
  26. 26. © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe
  27. 27. 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 © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 27
  28. 28. © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe
  29. 29. Structure of Work & Presentation Process Model Iterative Co-Dependent Development Implement. Evaluation 29
  30. 30. 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 30
  31. 31. 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 © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 31
  32. 32. 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“ © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 32
  33. 33. 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
  34. 34. 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 © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 34
  35. 35. 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 © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 35

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