The document presents a novel mind map query approach to improve information retrieval by addressing the limitations of traditional bag-of-words methods, which fail to capture the user's intent and the relative importance of terms. It highlights the benefits of using mind maps to better represent user queries, with a focus on associative context and term weighting based on their significance. Future work includes developing an academic recommender system and enhancing the system's ability to infer central ideas from user queries.

1. Ms. Sunayana R. Gawde
M.Tech Part I
14109

2. Original Paper by:
 Rihab Ayed
 Farah Harrathi
 M. Mohsen Gammoudi
 Mahran Farhat

3. Presented in:
 Fourth International Conference on Computer
Science, Engineering and Applications
(ICCSEA 2014), July 26 – 27, 2014 in Chennai, India

4. Traditional Approach:
 Bag of Words-misinterpretation of user’s need
 user cannot express clearly the main words of his idea of
search and the less important words.
 idea of the user is packaged in a linear form which does
not express the nodes in the network of the user’s mind.

5. Related Work:
Pearl trees system:
 Storing interests in Mind Maps
 Search:
 Bag of Words
 Selecting a Pearl
 Problem:
 Semantic Clustering
 Ex. “popular terms Information Retrieval”

6. Proposed Approach
 Mind Map Query:
 Mind Map query formulation by the user and
 An internal representation of the Mind Map query by
the IR system.

7. Why only Mind Maps?
 The association aspect:
 The relative importance of terms:

8. Example Query:
 “The definition of a concept in a thesaurus or by
standard norms”
 Bag of Words: Equal Weighting for all terms:
“definition of concept thesaurus standard norms”

9. Mind Map Query Approach:

10. Example Query:
 “a good java course which describes inheritance and
polymorphism, but also contains other notions of java.
This course should contain exercises”
 Bag Of Words:
“good java course inheritance polymorphism
exercises”

11. Mind Map Query Approach

12. Example Query:
 “the definition of a semantic resource, for example
thesaurus, ontology”
 Bag Of Words:
semantic resource definition

13. Mind Map Query Approach:

14. Internal Semantic for Mind Map
Queries
 𝑇ℎ𝑒 𝑤𝑒𝑖𝑔ℎ𝑡𝑠 𝑜𝑓 𝑡𝑒𝑟𝑚𝑠 𝑤𝑖 = 𝜎(ℎ−𝑝 𝑖−1) ∗ α
Where
 𝜎= The power of importance between levels
 α = The weight attributed to the leafs of the graph
 𝑝𝑖 =The depth of the node 𝑛𝑖 in the query graph
 h = The height of the query graph

15. Example Query:
“Documents about Precision measure in Information
Retrieval, for example: GMAP, MAP”

16. Calculation:
 W1=Term Weight of Precision
 W1=2(2−0−1) ∗ 1/2 = 1
 W2=W3=W4=2(2−1−1) ∗ 1/2 = 1/2
 Term Precision was twice more important than other
terms.

17. Experimentation:
 On medical corpus (CLEF 2009, (Cross Language
Evaluation Forum)
 74’902 images from 20’000 English journal articles in
Radiology.
 25 queries in the collection test.
 IRS based Query
 Mind Map Query

18. Results:

19. Future Work:
 Guessing of Central Idea by the System.
 Academic recommender System.

20. References
 Rihab Ayed, Farah Harrathi, M. Mohsen Gammoudi and
Mahran Farhat(2014) A Mind Map Query in Information
Retrieval: The ‘User Query Idea’ concept and preliminary
results. Fourth International Conference on Computer
Science, Engineering and Applications (ICCSEA 2014), July
26 – 27, 2014 in Chennai, India
 Kamvar, M., Kellar, M., Patel, R. and Xu, Y. (2009)
Computers and iPhones and Mobile Phones, a logs based
comparison of search users on different devices. In
Proceedings of the 18th International Conference on World
Wide Web (Madrid, Spain, April 20-24, 2009). WWW'09.
ACM, New York,NY,801-810.

21. Thank You!!