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gnowsis personalization

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Published on October 30, 2007

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Personalization in the EPOS project:  Personalization in the EPOS project Leo Sauermann, Andreas Dengel, Ludger van Elst, Andreas Lauer, Heiko Maus, Sven Schwarz DFKI GmbH 12.06.2006 persona created using http://www.sp-studio.de/ Slide2:  Leo Sauermann 1: no personalization without the person:  1: no personalization without the person 2: no person without the subjective data of the person (from the computer’s perspective):  2: no person without the subjective data of the person (from the computer’s perspective) Slide5:  Rome Peter Paul’s mental model Paul’s concepts pimo-things Paul’s files and digital resources paul:Rome http://www.comune.roma.it/ relations to occurrences Slide6:  Results of the EPOS project Szenario of EPOS:  Szenario of EPOS Knowledge Worker „Paul“ using Desktop PC Problem: Data about single ideas are stored in several applications and without context Files ↔ Emails Semantic Desktop dc:language = AEHS:  dc:language = AEHS Adaptive Educational Hypermedia System AEHS Document Space DOCS User Model UM Observations OBS Adaptation Component AC Three contributions:  Three contributions A representation of the user’s personal information items, including e-mails, files, and other data sources using RDF „native resources“ = DOCS A representation of the user’s mental model in a formal representation, using several layered ontologies. „PIMO“ A desktop service to capture the current actions of the user, representing the actions using RDF and then calculating the current context of the user. „Context Server“ = OBS UM = DOCS + PIMO + OBS Adaptive Applications PIMO = Personal Information Model:  PIMO = Personal Information Model From native structures to PIMO:  From native structures to PIMO Native data is expressed in RDF DOCS RDF/S vocabularies like foaf, vCard, Dublin Core data + structures Personal Information Model PIMO Personal Concepts Topics Places People Types Workflow with relations to files and folders PIMO is The “personal ontology”:  PIMO is The “personal ontology” “shared” across applications filled from DOCS, Company ontologies and domain ontologies used by the – user creates instances creates classes & properties (on the fly) annotates PIMO is filled:  PIMO is filled automatically from data Data Paul‘s files & e-mails aperture.sf.net (check it out!) PIMO used and extended:  PIMO used and extended PIMO ontology languages:  SemDesk Upper Level Person Role Document Organization Time SemDesk Mid-Level Manager Project Contract Company Offer ontology imports Message dfki.de/ont/pim/pimo PIMO ontology languages PIMO-Basic defines the basic language constructs. PIMO-Upper A domain-independent ontology defining abstract sub-classes of Thing. PIMO-Mid: More concrete sub-classes of upper-classes. The EPOS mid-level ontology serves to integrate various domain ontologies and provides classes for Person, Project, Company, etc. PIMO ontology languages:  Domain Model: Bibtech A Heiko Car-Ent Report56 Report EPOS dfki.de/ont/pim/pimo PIMO ontology languages Domain ontologies A set of domain ontologies where each describes a concrete domain of interest of the user. The user’s company and its organizational structure may be such a domain, or a shared public ontology. all PIMO ontology layers:  Rep Lang SystemItems SemDesk Upper Level Person Role Document Organization Time SemDesk Mid-Level Manager Project Contract Company Offer Native Data Vocabularies vCard vEvent dublin core foaf image Person Image Thing sub-classes Domain Model: Bibtech A Heiko Car-Ent Report56 Report EPOS ontology imports Message aperture.semanticdesktop.org/data dfki.de/ont/pim/pimo all PIMO ontology layers Paul‘s PIMO - Personal Information Model :  Paul‘s PIMO - Personal Information Model PIMO of Person:Paul Paul Project Z Report41 File X e-mail2 vCard H Rep Lang SemDesk Upper Level SemDesk Mid-Level Native Data Vocabularies Domain: Bibtech A Domain: Paul’s company User Model:  User Model UM = DOCS + PIMO + OBS To capture a user model, we need to know PIMO the categories/model of the user DOCS the documents/e-mails attached to the categories OBS the current context of the user This holistic user model can now be used for several personalized applications Context Service:  Context Service Plugins gather user actions Elicitation of task concepts Notification of GUI Context Representation:  Context Representation Context in EPOS context of a knowledge worker context shall support (personal) knowledge management Contextual elements (CEs) relevant documents, topics, places, actions, tasks, organizational entities, … from the user's DOCS and PIMO not alien data, but known, familiar entities and structures Service Oriented Architecture ContextService gathers events using RDF messages from Plugins represents context as RDF model, using the PIMO S. Schwarz. A context model for personal knowledge management. In Proceedings of the IJCAII WS. on Modeling and Retrieval of Context, Edinburgh, 2005. Applications:  Applications Context Assistance:  Context Assistance Sidebar can be switched off shows current context documents people projects topics changes dynamically use: open related information, pro-active, non-obtrusive assistance system Application: Drop Box:  Application: Drop Box Helps filing information uses PIMO structures concepts and folders uses DOCS for text similarity Knows the users model and is trained by using it process flow files are stored into a Drop-Box folder files are text-analysed and possible target folders are suggested Drop-Box user interface shows user selects a folder, classify files are moved and classified not used, but obvious: OBS – current context Semantic Search:  Semantic Search search over EPOS data (PIMO) can be personalized using rules SPARQL queries example # found something? -> infer other representations via SPARQL (?hit retrieve:item ?x) -> querySparql('CONSTRUCT { ?x pimbasic:hasOtherRepresentation ?y } ') # found a project? -> also show members (?hit retrieve:item ?project), (?project rdf:type org:Project) -> querySparql('CONSTRUCT { ?project org:containsMember ?m. }). Innovation search result expansion using SPARQL customized rules for search – only when word “x” is searched, include these results, etc Semantic Search:  Semantic Search Evaluation:  Evaluation Methodology used for Evaluation in EPOS:  Methodology used for Evaluation in EPOS Case Study Method Case Study with 8 researchers from DFKI Preperation Phase three months: users learn the system, bugfixing Evaluation period of 1 week with daily usage Daily interviews with questionnaire Usage data collection Explicit user feedback for proposals from context elicitation – user had to check if results were correct General observations from questionnaire Personal Ontology represents the view of the user (90% positive answers) it is “valuable” for searching and classifying information Semantic Desktop is perceived as “helpful in their daily work” Example findings from Case Study:  Example findings from Case Study Move & Classify via EPOS DropBox Filing is faster than before due to proposal of locations? Yes on 40% of all days There was still manual filing, but only in 8% of all reported filings Multiple classification have been used  2,5 categories per file: multicriterial classification PIMO is populated on-the-fly Lessons learned: changing the linker:  Lessons learned: changing the linker Before: Evaluation Manual linking with the Linker was seldom used Whereas semi-automatic linking was appreciated by means of Move & Classify, Topic-linking, PIM mapping Redesign After: Resources can be “Tagged”, the metaphor is known from Web 2.0 applications. Tags are searched semi-automatically Benefit is immediately seen User interface is simpler … to be evaluated again …. Semantic Search:  Semantic Search evaluated at Siemens SBS by Mark Siebert and Pierre Smits customization features used integrated a proposal ontology conclusion: Through peer search and semantic enhancements, recall was increased. Precision may decrease, depending on the setup and scenario. Outlook:  Outlook Our goal:  Our goal This is my personal computer Slide34:  Semantic Applications Desktop Search Gnowsis Server Aperture Crawlers Outlook e-mail server filesystem Outlook Crawler Ont. Matching Files e-mail Gui invocation Tagging Clustering Domain Ontologies Lucene Index Web 2.0 Interfaces Open source and reusable:  EPOS will be continued our results are code for you Open source and reusable gnowsis-beta http://www.gnowsis.org Nepomuk http://nepomuk.semanticdesktop.org MyMory http://www.dfki.de/mymory Slide36:  note: Demo of Nepomuk today at the EU projects session I can demo gnowsis beta 0.9 Summary:  Summary The PIMO ontology stack and Paul’s PIMO allow us to personalize using precise knowledge about the user User observation components identify contexts based on PIMO Applications use PIMO + context in combination open source, will be continued Questions:  Questions persona created using http://www.sp-studio.de/

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