1 / 23

Context Aware Interactive Information Retrieval

Context Aware Interactive Information Retrieval. Claus-Peter Klas Paul Landwich Matthias Hemmje Dagstuhl , Seminar on Interactive Information Retrieval,, March 2009 . Outline. User / Use Cases / User Scenarios Context Information Dialog Daffodil / Framework / Logging

damia
Download Presentation

Context Aware Interactive Information Retrieval

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Context Aware Interactive Information Retrieval Claus-Peter Klas Paul Landwich Matthias Hemmje Dagstuhl, Seminar on Interactive Information Retrieval,, March 2009

  2. Outline • User / Use Cases / User Scenarios • Context • Information Dialog • Daffodil / Framework / Logging • Relevance Feedback • Summary & Outlook

  3. User Scenarios • “Easy” task: Known-Item-Search • Supported by Google and libraries • Complex task: Thesis, Related work chapter, etc. • User has incomplete or no knowledge about domain • User has partial knowledge about domain

  4. ContextWhat is it and how to gain it? • Consider a computer scientist (e.g. thesis) • General • Affiliation, research domain, position, list ofpublications, geospatialdata • Task • Articles • Conferences • Journals • Authors • Historic interactions with system(s)

  5. Approach to IIR:Information Dialog Model • What are possible activities? • How do these activities affect our information dialogue context? Information Dialogue Context

  6. Dialogue state after a first explorative query k: Recall set

  7. Activities visualised result set • Exploration • Navigation • Focus • Inspection • Evaluation • Store I: Content set J: Interest set R: Relevance set r: Result set k: Recall set

  8. Sequence of separate queries

  9. DAFFODIL FrameworkInteractive IR

  10. Create knowledge re-present re-present retrieve Search knowledge interprete interprete Interpret results collate collate Structured storage Scientific Work orInformation Journey Choose information sources discover

  11. Rich interaction: levels of logging • User behavior • User-System interaction • Conceptional events • Service events • Interface action events • UI events • System parameters

  12. User behavior • User behavior can never be captured completely, because it is inside the users head. • What we can do is: • Captured behaviour by supervision • Video • Questionare

  13. User-System Interaction • Here we have plenty to capture, although, only interpretation of the data without user behavior is tricky. • Events • Conceptual events: High level abstract events for comparison • Service events: Events special for a IR service • Interface action events: Events from entering data, click buttons, select menue • UI events: Events from keystrokes, mouse movement

  14. System parameters • On this level all information about the soft and hardware is captured. • Parameters • Software: Efficiency of algorithms • Hardware: Usage of computer resources • Network: Load of computer network

  15. Conceptual Events • Search • Inspect • Browse • Annotate • Help • Navigate • Display • Store • Author • Communicate

  16. Events come in paths

  17. Relevance Feedback • Investigate two RF scenarios for query term suggestion and re-ranking the result list with different approaches. • Use result list (current situation) and task path events (> 1 session)

  18. Example of relevance parameters

  19. Evaluation • Investigate on methods and models to interpret log data • Evaluate the relevance feedback and show the user the difference and may ask for explicit feedback

  20. Summary & Outlook • Model for capturing the information dialog • Daffodil Framework • Log data over session bounderies (xx GB) • Running „real live“ testbed in computer science (other) • Manysophisticatedfunctionalities • Examplefor IIR: Relevance Feedback • General: Daffodilframeworkcanbe a evaluationframeworkfor IIR • Try It Out: http://www.daffodil.de

  21. Goal: Cognitive enhanced model of information retrieval Problem Information deficit Information need Query Adjustment Discovery Presented knowledge Cognition 1. State (concrete) 2. State (uncertain) 3. State (fuzzy) Knowledge Stored knowledge Represented knowledge Core IR-engine Cognitive enhanced IR-User interface Human [Lan07]

  22. Statistics/History of DAFFODIL • DAFFODIL started in 2000 as national funded project @ University of Dortmund in the IR group of Norbert Fuhr • 2 PhDs, more to come, > 14 Master/Bachelor thesis, • > 14 Publications in JCDL, ECDL, etc. • Lives unfunded in teaching, projects and as evaluation framework now at Duisburg-Essen and Distance University of Hagen • Projects: Daffodil, DELOS NoE on DL, SHAMAN (EU), EMTO (Philosophers), LACOSTIR, INEX

  23. Adaptive Framework & Concepts User model Personalisation Recommendation Adaptivity Awareness Kollaboration

More Related