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Modeling Behaviour of the Users in Adaptive and Semantic-enhanced
Information Systems: The Role of a User Ontology
Dr. Liana Razmerita
Centre of Applied Information and Communication Technologies Copenhagen Business School
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Presentation Outline
IntroductionContext of research - KMSMotivation- painpoints of the actual KM toolsResearch Questions
Ontology-based User Modeling Conclusions and Outlook
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Context of researchKnowledge Management Systems (KMS)- information systems
dedicated to manage organizational knowledge (Leidner and Alavi, 2001).
Semantic-enhanced KMSs - Domain ontologies can improve KMS within organizations and among distributed web-comunities (Maedche et al., 2002, Velardi et al, 2007)
create
capitalize
share
KMS integrate complex knowledge processes:
• Support social processes: ks, kc.• Collaboration between employees;• Learning processes;• Management of tacit knowledge;
KM 1.0, KM 2.0, KM3.0 –semantic-enhanced
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Limitations of current KMSs
Need to better organize knowledge “Content is not correctly organized, not updated or [is] duplicated.”
Need for better knowledge filtering and user support “These tools need major improvement to allow users [to]
use knowledge tools in an easy way, spend less time and avoid getting lost among hundreds of document”,
”Save time when I am looking for a solution”
Need to better manage tacit knowledge “To know what people know and to make their
experience with technology and products accessible”.
Source: Survey on KMS (n=16 Ontologging End-Users )
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Research Questions
Why is it important to model users of a KMS? What are the relevant characteristics of the users of a KMS? What type of user’s behavior can be distinguished in a KMS? How to track and maintain the user models in a KMS?
What are the advantages/limitations of applying ontologies in user modeling? How to make us of the metadata?
How can a user model improve the interaction with a KMS? What type of intelligent /personalized services can be provided
based on the user’s characteristics?
How to build semantic-enhanced data models?What are the perspectives of its use in the context of the
Semantic Web?
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Related WorkUser Modeling Research
Adaptive hypermedia and personalized interactions (Kobsa, 2002),(Brusilovsky, 2007),(Cristea et al., 2007)
Ontology-based user modeling (Razmerita et al., 2003)(Kay and Lum, 2005)(Heckman et al., 2005, 2007) (Katifori et al., 2008)
Semantic Web and Ontology Research Ontology-based personalization: Dolog and Nejdl(2007) EPOS
(Schwarz and Roth-Berghofer,2002), Elena(Dolog et. al., 2003); (Henze et al. 2004)
Ontology, agents and corporate memory: FRODO(Van Elst & Abecker, 2001), CoMMA(Gandon and Dieng-Kuntz, 2001)
Knowledge Management Systems (O’Leary &Studer, 2001), (Nonaka et al. 2001), (Fischer &
Ostwald, 2001)
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From Ontology-based Knowledge Modeling to Ontology-based User ModelingIntegration of 2 different specifications for user model by 2
Spanish companiesBuilding a user ontology is not simple!
How achieve an agreed conceptualization, integrate specific characteristics of users interacting with KMS, completeness?
Adapt a methodology proposed by [Uschold and Gruninger, 1996]Step1 Specification phase
The use of IMS LIP specification Extends IMS LIP-upper ontology
Step 2 The process of coding KAON tool suite and Web ontology language extending RDF/RDFS
Step 3 The process of integrating with existing ontologies The definition of similar concepts as synonyms Different representation languages
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OI-Modeler View of User Ontology
Behavior Concept
TypeOfActivityLevelOfActivity
LevelOfKnowledge-Sharing
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Ontology-based User Modeling framework (OntobUMf)
User Model
User Profile
Editor
Intelligent Service I
EventsLogs
LogInstances
DomainInstances
User Ontology
Domain Ontology
Automatically
providedManually
provided
Intelligent Service n...
User Instances
Log Ontology
Documents
(Razmerita et al. 2003, Razmerita 2007)
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Intelligent Service: category extractor
Categories of users obtained by processing the logs (heuristics + fuzzy logic);Type of Activity: Readers/ Writers/ Lurkers;
If (nb_of_read_papers>NR) and (nb_of_contributions <NC) then user(x) =”reader” (during timeframe)If (nb_of_contributions>=NC) then user(x) =”writer” (during timeframe)….
Level of Activity: Very Active/Active/Visitor/Inactive;If (nb_of_read_papers > NR) and (nb_of_contributions >=NC+1) then
user(x) = “very active“ ….If (nb_of_read_papers =0) and (nb_of_contributions =0) Then user(x)
= “inactive” (during timeframe)
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Intelligent Service: category extractor
Level of Knowledge Sharing: Unaware, Aware, Interested, Trial, Adopters
Y=f(x1, x2)– [very high, high, medium, low, very low] x1 the type of activity: [high, medium, low]
x2 the level of activity: [high, medium, low, very low]
Y=f(x1, x2) high medium low very low
high very high very high medium
medium high medium low
low very low very low
The calculus of the level of knowledge sharing
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Findings-some evaluation results
Users are concerned with privacy and trust: User’s profile should be only partial available in a
KMS; Users want to be in control and to maintain their
profiles; The use of combo box would facilitate the
acquisition of the user’s data and the consistency of the terminology;
Ontologging end-users identify recognition and promotion as key incentive for knowledge sharing
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SummaryA top-down approach in building a user
ontology using IMS LIP specification-lack of ”know-how” on how to build an ontology?
User ontology extending IMS LIP (Information Management Systems Learning Information Package)
A standard/a specification can be usefull but can be limitative.
Ontology-based UM frameworkUser Behavior (level of activity, type of activity, level of
knowledge sharing)
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Future work
Extending OntobUMf towards its use within KM2.0 (blogs, wikis)
OntobUMf in an e-learning scenario:LevelofActivity/TypeofActivity/LevelofKnowledge
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