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for annotation o this is often created by different users (KE experts) and updated only seldomly o hence it’s often out-of date, incomplete, inaccurrate and incomprehensible © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 5

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: 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

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

Process of Ontology Maturing Users annotate resources with arbitrary tags New concept ideas emerge e.g. recent/specific tags like ‘whole grain spaghetti’ 12

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

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

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‘

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

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

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

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

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

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

ImageNotion: Semantic Image Annotation & Search © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 24

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

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

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

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

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

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 © FZI Research Center for Information Technologies Karlsruhe, Germany | Information Process Engineering | www.fzi.de/ipe 34

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

Ontology Maturing for Searching, Managing, and Retrieving Resources

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