1. Context-based Search in Topic Centered Digital Repositories Christo Dichev, Darina Dicheva Winston-Salem State University Winston-Salem, N.C. USA {dichevc, dichevad}@wssu.ed

2. Topic Maps Model for Organizing and Locating Information –With a distinctive feature to support  Single access point to all relevant information about certin topic. –Derived features:  Aids both finding the right and finding the related information.  Provides a ground for exploratory search. Can we do more in e-learning applications? –Possible approach - incorporate a contextual framework to improve exploration experience.

3. Topicality is not everything What kind of things the users are searching for ? – A typical use of e-learning repositories is for locating resources related to certain task.  searching for topics is only one aspect of the search. Besides the topic users need resources satisfying additional criteria, for example: –Beginner level code examples on Prolog –Advanced articles related to Prolog negation. –Which subtopics of a more general topic are related.  Are backtracking and negation related ?  What about negation and closed world assumption? –The task requires resources from different subtopics.  writing a paper on “Programming Techniques”

4. Support for exploratory search How to support exploration ? –Provide a cue where the most promising area for exploration lies. –A possible cue:  Set of documents partially satisfying user’s criteria for relevancy. Assumptions: –The targeted resources can be described by specifying a “locality” in the TM, coupled with contextual factors for additional filtering. –The “locality” can be expressed in terms of topics and relations paired with a traversal mechanism that identifies what is in and what is out of the “locality”. –The retrieved set, can be filtered based on the remaining criteria.

5. The Approach We explore this idea in two directions: 1. Defining expressions that enables users to specify context-based queries: 2. Using the result of the context-based queries for improved query-browsing interaction.

6. ….And Now…. Commercials ….

7. TM4L - Topic Maps For E-learning TM4L - intended to complement existing Topic Map editors and visualization tools. It combines –TM4L Editor –TM4L Viewer Two groups of users are targeted: –authors with a limited background of ontologies; –learners seeking information support in their course tasks. TM4L is currently available as a standalone application. http://compsci.wssu.edu/iis/nsdl/download.html

8. TM4L Functionality In TM4L the initial set of relations includes five relations: –Whole-Part –Superclass-Subclass –Instance-Of –Related –Similar This set can be extended with arbitrary user defined relations.

9. TM4L – Partonomy View

10. Topic Search in TM4L

11. Example of TM4L Application

12. Contextual Aspects in Information Organization In practice, we group topics and resources based on a certain set of relation types. It allows user to select from all related topics the ones that are related in a certain way. This suggests the following strategy –Users specify the relation types they are interested in combined with some contextual factors –The system draws the locality along the user defined axes, filtering the resources within the locality based on contextual factors. –So resolving the locality is equal to traversal

13. Traversal is Insufficient Topic locality traversals enable users to specify TM projections and define regions in such projections –by specifying the topics and relation types they are interested in –by traversing the relations to extract the info within the region.... but this strategy addresses only topicality Topic and resource grouping may be done on several ontological levels that reflect different contexts. When users search for resources, several criteria are in play –What is the resource about ? –In what form and level is the content presented? –How is it related to the current user’s task?

14. TM4L Perspective In our context-based IR framework, topic- relation traversal is intended to capture the “aboutness” of the search. Some other contextual factors –Level of difficulty such as, depth of coverage technicality etc. are captured by the Scope/Theme property. –Instructional factors such as Lecture Notes, Examples, Tutorial, Exercise, Demo etc. are captured by “Resource type” property.

15. Contextual Queries in TM4L

16. Similar vs. Relevant In information retrieval, the terms “similar” and “relevant” are often used interchangeably. Concepts are related, and their relationships imply some kind of similarity. This aspect of similarity is addressed by the contextual queries, which: –restrict the query within a user defined region’ –filter the result on the basis of document properties.

17. Different Aspects of Similarity Resources can be similar –by topic, –by the level of granularity, –by the source, –by the presentation style, etc. Assumptions: –Similar resources can be shelved together.  A resource can be similar in different aspects to some other resources  A resource can be in different kind of shelves at the same time –Retrieved resources, can point to their (multiple kind of) shelves, where the user can find other relevant material.

18. Context is Elusive Some intuition: While It is not always possible to articulate contexts, we are able to recognize items matching our context Possible approach: If you see item matching your context then find similar items

19. Defining a Context in TM4L