1. Efficient Ranking of Keyword Queries Using P-trees
Fei Pan, Imad Rahal, Yue Cui, William Perrizo
Computer Science Department
North Dakota State University
Fargo, ND

2. Outline The Keyword Ranking problem The P-tree technology
EIN-ring Approach
Efficient Ranking Algorithm using P-trees
Summary

3. Introduction
keyword Ranking is the process of ordering documents that best match a given query defined by a finite number of keywords
For our purpose, the query is also viewed as a mini-document
Similarity between documents is based entirely on their contents

4. Introduction
PRANK proceeds by finding the kNN of a given query (viewed as a document) using the EIN-ring (later)
After that, the systems returns a weighted list of matching documents (weighting scheme is discussed later)

5. Introduction Motivation Increase in the number of text documents
Medical articles
Research Publications s
News reports (e.g. Reuters)
Others
access to these information has become in great demand

6. Introduction (cont.)
text has no explicit structure like other data (e.g. relational database)
Vector Space Model have been proposed by Salton (1975) for text
Each document is represented as a vector whose dimensions are the terms in the initial document collection
The query is also represented as document vector in the given space

7. The P-tree technology
Tree-like data structure that store numeric (and categorical) relational data in vertical bit format by
splitting each attribute into bits
representing each bit position by a P-tree
The next example shows the conversion of columns into P-trees (next slide)
All columns are converted to binary first

8. The P-tree technology (cont.)
Construction of Basic Peano Count trees

9. The P-tree technology (cont.)
Basic P-tree Operations

10. Predicates using P-trees
There are five basic type of predicates, i.e., x > c, x  c, x  c, x<c, x=c, where c, is bound value.
We come up with five very useful propositions for each of them using P-trees.

11. The Formula Propositions
Proposition for predicate Px>c
Briefly, for low bound c=(bm..bi..b1)2
Proposition for predicate Pxc
Similarly, for upper bound c=(bm..bi..b1)2

12. The Examples Calculation of Px>c Px > (4)10 > (100)2
Crude Method
Px > (4)10 > (100)2
= (P3P2) (P3  P2’  P1)
Our formula:
= P3(P2P1)

13. The Examples (cont.) Calculation of Px  c Crude Method
Pxj  (70)10 = Pxj  ( )2
= (P7’P6’)  ( P7’P6P5’ P4’P3’P2’)
 ( P7’P6P5’ P4’P3’P2P1’)
Our formula
Pxj  (70)10 = Pxj  ( )2
=P7’  (P6’  (P5’  (P4’ ( P3’ ( P2’  P1’ P0’))))

14. The EIN-ring Approach
Definition of Equal Interval Neighborhood Ring (EIN-ring)

15. The EIN-ring Approach (cont.)x X
r+ r 
Px-r-<Xx+r+ 
P’x-r<Xx+r
Px-r-<Xx+r+ ^ P’x- r<Xx+r

16. EIN-ring Formulation

17. The Examples
Calculation of Pc1<x  c2
Crude Method
Our formula

18. Efficient Ranking Algorithm
Simplified Prototype of the Data Model

19. Efficient Ranking Algorithm
Dimensionality values are the measurements, called Term Frequency by Inverse Document Frequency (TFxIDF)
TF (t): how many times term t exists in a document
Local weight measuring the importance of a term to document
IDF: log(N/Nt) where Nt is the number of documents containing at least one occurrence of t and N is the total number of documents
Global weight measuring the uniqueness of the term
Normalization is then applied to solve the problem document sizes
term t might exist 10 times in a 100-pages document but only 9 times in 1-page document
Clearly the t is more associated with the 2nd document
All representations are converted to P-trees

20. Efficient Ranking Algorithm
Step1. Consider each weight in W1, W2 and W3 sections in Table 1 as a dimension of the search space X. We have a space of (k+2)-dimensions.
Step2. Let Xstart be the point in X having the largest weight values, i.e., xstart-i = max(Wi)
Step3. Calculate Pmin  Pmax, where Pmin = PY<Xstart-i-ri, Pmax = PX<Xstart-i-ri-.
Ranking Algorithm Using EIN-ring

21. Experiment Results
We compared P-rank with scan based ranking approach on MeDLINE data from KDDCup 2002 task2 with three size groups, denoted as DB1, DB2, DB3, which contain 1,000, 8,000, and 15,000, respectively.

22. Architecture of P-RANK

23. Conclusion
We describe the architecture, implementation, and evaluation of the P-RANK system for extracting evidences of products of genes from text document, e.g., biomedical papers.
Our contributions in this paper include a new efficient keyword query system using data structure P-tree and a fast weighted ranking method using the EIN-ring Formulations.

24. Future direction
Accuracy
Gene Alignment and Matching

25. Thanks!