1. Clustering Semantically Enhanced Web Search Results
Anantha Bangalore,
MSD, Vienna, VA
Arun Sood, Professor and Chair, CS Dept
Noorullah Moghul, CS PhD student
George Mason University, Fairfax, VA.
9 September 2004

2. Overview DAIRS: Distributed Agents for Information Retrieval Systems
Software agents
Applied to Image, geospatial and text processing.
Tested within medical context
Results of initial testing are provided
Applicable in many domains
Scope for discussion
9 September 2004
© 2004 by Arun Sood

3. DAIRS - Problem Statement
Data volume is exploding
Data rich information poor environment
Many search systems provide high recall but low precision (e.g. Google)
Increased precision (relevance)
Saves user time
Enables a broader search of candidate URLs
9 September 2004
© 2004 by Arun Sood

4. Our Approach
Assumption: Google (and other) search engines provide acceptable recall
DAIRS extracts a robust and relevant result set
Use an ontology to describe the user context
Ontological filtering
Clustering of the concepts
9 September 2004
© 2004 by Arun Sood

5. Subset of UMLS Semantic Net
9 September 2004
© 2004 by Arun Sood

6. Advantage of Our Agent Approach
Easily compose solution methodologies using light weight agents: 200 agents in our system
Works in a distributed environment
Agents are mobile
Load balance agent assigns agents in the background
Exploits parallelism
Import functionality from 3rd party software, without importing the application
9 September 2004
© 2004 by Arun Sood

7. Interface to Compose Solutions
9 September 2004
© 2004 by Arun Sood

8. EXPERIMENT
Google Search – {cold, strain, fluid, adjustment, fat, condition, etc.}
Selected top 100 URLs in each search
Classified the URLs using DAIRS ( UMLS as Ontology filter and Cluto as clustering software)
Compared DAIRS results with the URL classification done manually
9 September 2004
© 2004 by Arun Sood

9. Words with Multiple Senses (Cold)
NLM has identified 50 words with multiple senses that occur frequently
Cold disease, cold temperature, cold war, cold fusion, cold springs, cold calls, etc.
Find URLs dealing with cold in a medical context (e.g. common cold)
Ontology filter (UMLS – Metathesaurus) helps deemphasize non-medical URLs
Clustering leads to separation of medical related URLs from other URLs
9 September 2004
© 2004 by Arun Sood

10. EXAMPLE URL CLASSIFICATION Common Cold
Common Cold URLs classified correctly
Undetected URLs
Contains images and links to other websites, little text
Contains very little textual content
Contains images and very little text
Mostly image content
9 September 2004
© 2004 by Arun Sood

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© 2004 by Arun Sood

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© 2004 by Arun Sood

13. URL CLASSIFICATION EXAMPLE – 2
Cold URLs –False Alarms
NEWS article describes a story at a cold place
Winter wear
Cold Calls – sales calls
Music website
9 September 2004
© 2004 by Arun Sood

14. SUMMARY OF THE RESULTS: IR Measures
CONCEPT
Google Hits
Analyzed
Correct Classification
Undetected
False Alarms
Google DAIRS
Common cold (Disease)
100 5 4 91
Cold Temperature 9 86
Strain (Muscle) 15 6 79
Strain (Bacterial) 42 14 44 8
Fluid (Substance) 2 96
Fluid (Behavior) 36 55 12
Like “cold” example, most of the misses are because of limited text at these sites – mostly images, and pointers to other web pages.
9 September 2004
© 2004 by Arun Sood

15. Location of hits: Usability Measures
CONCEPT
Google Hits
Analyzed
Correct Classification
Undetected
Common cold (Disease)
100
44,45,49,62,83
19, 50, 51, 99
Cold Temperature
43, 53, 58, 74, 96
1, 9, 17, 31, 37, 54, 64, 84, 85
Strain (Muscle)
1, 17, 18, 22, 27, 33, 42, 43, 54, 67, 79, 80, 84, 92, 95
7, 11, 20, 38, 53, 85
9 September 2004
© 2004 by Arun Sood

16. Building a Robust DAIRS
Previous study shows that some sites were not properly classified because the text content was small
Next steps
Build agent to extract links to the next level of URLs
Build agent to parse the next level of URL text and include in the search results
Build agent to OCR the images, and extract text
9 September 2004
© 2004 by Arun Sood

17. DAIRS vs. Search Engines
DAIRS complements search engines to fine tune target specific searches
DAIRS permits creating user based filters using ontologies
DAIRS facilitates the creation of user guided technology specific dictionaries
Our project on DAIRS for nanotechnology will build a mega-dictionary, which will be parsed into components of interest to clients
9 September 2004
© 2004 by Arun Sood

18. Commercial Applicability of DAIRS
For example the monitoring the developments in Nanotechnology
The dynamic issues related with a growing field is an ideal place to use a DAIRS approach to manage information
Date
Google URLs
Google News (30 days)
9/6
1.59 M
1150
6/6
712
4/29
1.42 M
1390
3/22
1.3 M
970
9 September 2004
© 2004 by Arun Sood

19. Review – Key issues Ontologies can be used to focus the search results
Significant reduction in false alarms, with some loss in detections
Discussed strategies for improving DAIRS
DAIRS complements search engines
Broad applicability
9 September 2004
© 2004 by Arun Sood

20. Questions? Can DAIRS be used for composition of web services?
How to build an ontology?
Is it possible to build a good enough representation?
Single ontology or linked ontologies?
Build a single ontology for an organization?
How difficult is it to build an agent?
What is under the hood?
Why is agent mobility important?
9 September 2004
© 2004 by Arun Sood