1. INFORMATION RETRIEVAL TECHNIQUES BY DR. ADNAN ABID
Lecture # 18
Wild Card Queries
B Tree

2. ACKNOWLEDGEMENTS
The presentation of this lecture has been taken from the underline sources
“Introduction to information retrieval” by Prabhakar Raghavan, Christopher D. Manning, and Hinrich Schütze
“Managing gigabytes” by Ian H. Witten, ‎Alistair Moffat, ‎Timothy C. Bell
“Modern information retrieval” by Baeza-Yates Ricardo, ‎ 
“Web Information Retrieval” by Stefano Ceri, ‎Alessandro Bozzon, ‎Marco Brambilla

3. Outline How to Handle Wild-Card Queries Wild-card queries: * B-Tree

4. How to Handle Wild-Card Queries
B-Trees
Permuterm Index
K-Grams
Soundex Algorithms
00:03:50  00:06:35

5. Wild-card queries: *
mon*: find all docs containing any word beginning with “mon”.
Easy with binary tree (or B-tree) lexicon: retrieve all words in range: mon ≤ w < moo
*mon: find words ending in “mon”: harder
Maintain an additional B-tree for terms backwards.
Can retrieve all words in range: nom ≤ w < non.
00:06:50  00:07:15(mon*)
00:08:15  00:08:50(easy)
Exercise: from this, how can we enumerate all terms
meeting the wild-card query pro*cent ?

6. B-Tree
00:10:20  00:11:35

7. B-Tree n = # of pairs. # of external nodes = n + 1.C D I H G K J F E
Level 0
Level 1
Level 2
Level 3
00:16:50  00:17:10
n = # of pairs.
# of external nodes = n + 1.

8. Wild-card queries: *
mon*: find all docs containing any word beginning with “mon”.
Easy with binary tree (or B-tree) lexicon: retrieve all words in range: mon ≤ w < moo
*mon: find words ending in “mon”: harder
Maintain an additional B-tree for terms backwards.
Can retrieve all words in range: nom ≤ w < non.
00:22:50  00:24:00(Easy)
Exercise: from this, how can we enumerate all terms
meeting the wild-card query pro*cent ?

9. B+ Tree
00:24:10  00:24:50
00:25:10  00:26:10

10. Wild-card queries example
Query
Sanat* AND Jayasur*
mo*y
*day
Queries:
X lookup on X$ X* lookup on $X*
*X lookup on X$* *X* lookup on X*
X*Y lookup on Y$X* X*Y*Z ??? Exercise!
Sanat
AND
Jayasur
…yad*
00:31:27  00:32:00
00:32:20  00:32:45
00:42:00  00:42:20
00:38:00  00:38:30
00:35:00  00:36:00
00:48:34  00:50:00

11. Resources Peter Norvig: How to write a spelling corrector
IIR 3, MG 4.2
Efficient spell retrieval:
K. Kukich. Techniques for automatically correcting words in text. ACM Computing Surveys 24(4), Dec 1992.
J. Zobel and P. Dart.  Finding approximate matches in large lexicons.  Software - practice and experience 25(3), March
Mikael Tillenius: Efficient Generation and Ranking of Spelling Error Corrections. Master’s thesis at Sweden’s Royal Institute of Technology.
Nice, easy reading on spell correction:
Peter Norvig: How to write a spelling corrector