1. Personalized Interaction With Semantic Information Portals Eric Schwarzkopf DFKI (Eric.Schwarzkopf@dfki.de)

2. Motivation & Goals (1) Literature research consists not only of information retrieval, but includes also ● Making sense of retrieved information ● Locally organizing this information ● Sharing gained knowledge But existing portals focus on the IR process.

3. Motivation & Goals (2) ● Support additional research activities ● Identify and utilize relationships between interactions to assist the user – What are the relationships between sensemaking and information management? – How can those be used to assist in information retrieval?

4. Interaction Scenario (1 / 3) Document Collection 1. builds up Working Context 2. uses Information Portal 3.1 expresses recommendation need 3.2 evaluates User Model Recommendations 3.3 updates 3.4 generates

5. Interaction Scenario (2 / 3) 1. browses Information Portal User Model Recommendations 3.2 updates Interaction History 2. implicitely creates 3.1 evaluates Current Location 3.3 adds recommendations 4. evaluates options

6. Interaction Scenario (3 / 3) 1. identifies relevant items 2. adds items Current Location 3. annotates items Document Collection Semantic Relationships

7. Conceptual Overview Information Portal Document Manager Recommender Client Server Browsing Searching Organizing Sensemaking Expressing user's knowledge and needs Requesting recommendations Utilizing IR- functionality

8. Document Manager: Requirements What are the main features a document manager has to provide? – Possibility to spatially organize documents. – Easy creation of informal structures. – Creation of hierarchical structures. – Annotation of documents. – Possibility to define and browse semantic relationships. – Searching the document collection. – Sharing of created structures.

9. Document Manager: Whiteboard Central interaction component: A 2d whiteboard. – Allows spatial organization of documents. – Facilitates creation of informal structures and hierarchies.

10. Document Manager: Annotations Documents can be associated with free-form text annotations... – Intended for pointing out relevant information contained in the document....and short descriptions displayed on the whiteboard. – Intended as brief reminders.

11. Document Manager: Semantic Relationships (1 / 5) What about relationships between documents? – “X contains a nice example of the material presented in Y.” – “X improves on Y.” –... Spatial organization and group hierarchies are not appropriate... – Clutters the whiteboard. – Not possible to express detailed semantics. – Only facilitates the definition of symmetric relationships.

12. Document Manager: Semantic Relationships (2 / 5)...nor are notes or short descriptions. – Cumbersome to define and utilize relationships. – Relationships are hidden from the system, so it cannot use them to provide any assistance to the user. Our approach: – Provide an explicit, internal representation of semantic relationships. – Allow the user to easily create and modify this representation via the whiteboard. – Facilitate browsing this semantic network.

13. Document Manager: Semantic Relationships (3 / 5) Definition of relationships via drag and drop. Browsing via a graph-based interface.

14. Document Manager: Semantic Relationships (4 / 5) User creates a semantic structure on top of the set of documents and the associated general metadata. ● Semantic relationships – Content-based relationships – Problem-specific relationships ● General metadata – Bibliographic data – Taxonomic relationships ● Document set – unstructured

15. Document Manager: Semantic Relationships (5 / 5) Further applications of the semantic network: – Query Augmentation ● Identify documents related to the user's current query. ● Use IR query augmentation techniques on this set of documents. – User Modeling ● Characterize current working context. ● Assess interests and knowledge. – Recommender ● Provide relationship-specific recommendations. ● “The work described in this document improves on the work presented in the document you are currently reading.”

16. Document Manager: Searching & Sharing Support for text search. – Full contents of documents. – Notes associated with documents. – Bibliographic data. Collaborative support limited to sharing whiteboards.

17. Recommender Will provide content and strategic recommendations: ● Suggest papers likely of interest ● Recommend concepts to browse or limit a query to – Names of authors – Topic areas – Conferences ● Provide hints for specializing or generalizing queries

18. Recommender: Components Recommender Document Repository Information Sources: ● Knowledge Base ● Semantic Networks ●... Strategies How to use info sources given a recommendation need.

19. Recommender: Semantic Networks Example Query: What documents use material presented in a given document? Partial Net Global, Weighted Network Combine nets created by the user community. Weight edges according to confidence.

20. User Model The UM represents: ● The user's knowledge and interests. ● The user's current working context. ● The user's current goal. UM requirements: ● Intelligible. ● Accurate. ● Quickly adaptable. ● Comparable, mappable to KB and document space,...

21. User Model: Conception Proposed conceptual model consists of three layers: – General, fairly accurate, slowly adapting long-term profile. ● Represents general interests in terms of ontological concepts. – More specific, medium-term profile. ● Represents the current working context (ontological concepts, characteristic terms). – Very specific, quickly adapting, but likely less accurate short- term profile. ● Represents the user's current information need (ontological concepts, characteristic terms).

22. User Model: Data Sources General Knowledge Working Context Current Information Need Interaction with Information Portal ● Recently accessed pages ● Recent queries Interaction with Document Manager ● Recently accessed documents ● Recent modifications of the KB Personal KB ● Contained items ● Structure UM Layers

23. User Model: Usage UM Data Functions Query Augmentation Result Filtering Recommendations Contains sets of weighted documents + metadata