Context-based Search in Topic Centered Digital Repositories

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: • Defining expressions that enables users to specify context-based queries: • 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 Mapeditors 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 coveragetechnicality etc. are captured by the Scope/Theme property. • Instructionalfactors 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: Ifyou see item matching your context then find similar items

19. Defining a Context in TM4L