Multi Order Matching & Knowledge Bridge: Techniques for Post-Processing Search Results

1. Multi Order Matching & Knowledge Bridge:Techniques for Post-Processing Search Results CS598CXZ – Spring 2005 Project ID: HLE Presenter: Hieu Le (hieule2@uiuc.edu)

2. Introduction • Task: Re-organizing search results so that minimize the effort of users to examine • A lot of similar works have done. What’s new here?  We care more about helping users examine clusters • We care more about the cohesion of the cluster  We care more about longer phrases, not only single words

7. Step 1: Clustering • Used technique: Multi Order Matching (MOM) • Consider different lengths of segments • Consider importance of segments to documents

8. Step 1: Clustering

9. Step 2: Ordering inside clusters • Used technique: Knowledge Bridge (KB) • Minimize users’ effort to walk through result in a cluster • Minimize knowledge gap inside a cluster

10. Step 2: Ordering inside clusters

11. Step 2: Ordering inside clusters ρ(A, C) + ρ(B, C) < ρ(A, B)

12. Step 3: Ordering clusters • Ranked score of each item in a cluster will contribute to general ranked score of it. • Cohesion of items in a cluster also contribute to general ranked score.

13. What’ve done so far • Propose 3 steps of re-organizing search results • Developing and implementing MOM • Developing KB • Designing and Implementing user interface • Implement caching function for Google API to avoid limitation of 1000 queries a day.

14. Remained works • Implementing KB • Developing method for step 3, implementing it • Conducting experiment

15. Issues • How to quantify the method systematically?

16. Thanks