- 82 Views
- Uploaded on
- Presentation posted in: General

Project Overview

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Discovering Concepts Hidden in the Web

Tsau Young (‘T. Y.’) Lin

Computer Science Department, San Jose State University

San Jose, CA 95192-0249, USA

A set of documents is associated with a Matrix, called Latent Semantic Index(LSI), Then by treating the row vectors as Euclidean space points(point=TFIDF), The document is clustered(categorized)

polyhedron, the association is believed to be one-to-one

Corollary: A set of English documents and their Chinese translations can be identified via their semantics automatically.

A set of documents is associated with a polyhedron, the association is believed to be near one-to-one

Corollary: A set of English documents and their Chinese translations can be identified via their semantics automatically.

This is identified by semantics,as there is no explicit correspondence between two sets of documents.

1. Introduction

Domain: Information Ocean

Methodology: Granular Computing

Reaults

2. Intuitive View of Granular Computing

3. A Formal Theory

4.

2

- Current search engines are syntactic based systems, they often return many meaningless web pages
- Cause: Inadequate semantic analysis, and lack of semantic based organization of information ocean.

- Internet is an information ocean.
- It needs a methodology to navigate.
- A new methodology-Granular Computing

The term granular computing is first used to label a subset of Zadeh’s

granular mathematics as my research area in BISC, 1996-97

(Zadeh, L.A. (1998) Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems, Soft Computing, 2, 23-25.)

Since, then, it has grown into an active research area:

- books, sessions, workshops
(Zhong, Lin was the first independent conference using

Name GrC; there has several in JCIS)

- IEEE task force

Granulation seems to be a natural problem-solving methodology deeply rooted in human thinking.

Human body has been granulated into head, neck, and etc.

- In this talk, we will explain how we granulate the semantic space of information ocean that consists of millions of web pages

- How to organize the information ocean?
- Considering the Semantics Space

- A set of documents/web pages carries certain human thoughts. We will call the totality of these thoughts
- Latent semantic space (LSS);
- (recall Latent Semantic Index(LSI)

In data mining,

- a classification means identify an unseen object with one of the known classes in a partition
- Clustering means classify a set of object into disjoint classes based on similarity, distance, and etc.; the key ingredient here is the classes are not known apriori.

- Multiple concepts can simultaneously exist in a single web page, So to organize web pages, a powerful
Clustering

method is needed.

(The # of concepts can not be known apriori)

- The simplest representations of LSS?
- A Set of Keywords
- LSI

Definition 1. Let Tr denote a collection of documents. The significance of a term ti in

a document dj in Tr is its TFIDF value calculated by the function tfidf(ti, dj), which is equivalent to the value tf(ti, dj) · idf(ti, dj). It can be calculated as

TFIDF(ti; dj)=tf(ti; dj)log |Tr|/|Tr(ti)

where Tr(ti)denotes the number of documents in Tr in which ti occurs at least once,

1 +log(N(ti; dj))if N(ti; dj)> 0

tf(ti; dj) =

0 otherwise

where N(ti, dj) denotes the frequency of terms ti occurs in document dj by counting all its nonstop words.

where Tr(ti)denotes the number of documents in Tr in which ti occurs at least once,

1 +log(N(ti; dj))if N(ti; dj)> 0

tf(ti; dj) =

0 otherwise

where N(ti, dj) denotes the frequency of terms ti occurs in document dj by counting all its nonstop words.

Treat each row as a point in Euclidean space. Clustering such a set of points is a common approach (using SVD)

Note that the points has very little to do with the semantic of documents

Euclidean space has many metics but has only one topology;

We will use this one

1. Given by Experts

2. High TFIDF is a Keyword

- “Wall”, “Door”. . ., “Street”, “Ave”

- 1-association
(“Wall”, “Street”) financial notion,

that nothing to do with the two vertices, “Wall” and “Street”

- 1-association
(“White”, “House”)

that nothing to do with the two vertices, “White” and “House”

- 1-association
(“Neural”, “Network”)

that nothing to do with the two vertices, “Wall” and “Street”

- (open) 1-simplex:
(v0,v1) open segment

(“Wall”, “Street”) financial notion,

- End points (boundaries) are not included

- LSS of Documents/web pages
Simplicial Complex

- A special Hypergraph
- Polyhedron Simplicial Complex

- r-association
Similarly r-association represents some semantic generated by a set of r keywords, moreover the semantics may have nothing to do with the individual keywords

There are mathematical structure that reflects such properties; see next

- 1-simplex: open segment (v0,v1)
- 2-simplex: open triangle (v0,v1, v2) ;
- 3-simplex: open tetrahedron (v0,v1, v2 , v3)
- All boundaries are not included

- A (open) r-simplex is the generalization of those low dimensional simplexes (segment, triangle and tetrahedron) to high dimensional analogy in r-space (Euclidean spaces of dimension r)
- Theorem. r-simplex uniquely determines the r+1 linearly independent vertices, and vice versa

- The convex hull of any m vertices of the r-simplexis called an m-face.
- The 0-faces are the vertices, the 1-faces are the edges, 2-faces are triangles, and the single r-face is the whole r-simplex itself.

A line segment where two faces of a polyhedron meet, also called a side.

- A simplicial complexC is a finite set of simplices such that:
- Any face of a simplex from C is also in C.
- The intersection of any two simplices from C is either empty or is a face for both of them

- If the maximal dimension of the constituting simplices is n then the complex is called n-complex.

Let B(p), p V, be an elementary granule

U(X)= {B(p) | B(p) X = } (Pawlak)

C(X)= {p | B(p) X = } (Lin-topology)

Cl(X)= iCi(X) (Sierpenski-topology)

Where Ci(X)= C(…(C(X))…)

(transfinite steps) Cl(X) is closed.

Divide (and Conquer)

Partition of set (generalize) ?

Partition of B-space

(topological partition)

The pair (V, B) is the universe, namely

an object is a pair (p, B(p))

where B: V 2V ; p B(p) is a granulation

The inverse images of B is a partition (an equivalence relation)

C ={Cp | Cp =B –1 (Bp) p V}

- Cp is called the center class of Bp
- A member of Cpis called a center.

- The center class Cp consists of all the points that have the same granule
- Center class Cp = {q | Bq= Bp}

The set of center classes Cp is a quotient

set

US, UK, . . .

Iran, Iraq. .

Russia, Korea

(Divide and) Conquer

Quotient set

Topological Quotient space

- C (in the case B is not reflexive)

B-granule/neighborhood

C-classes

C-classes

C-classes

B-granule

C-classes

B-granule/neighborhood

Cp -classes

Cp -classes

(Divide and) Conquer

Quotient set

Topological Quotient space

B-granule/neighborhood

Cp -classes

Cp -classes

B-granule/neighborhood

Cp -classes

Cp -classes

B-granule/neighborhood

Cp -classes

Cp -classes

- Topological Reduct
- Topological Table processing

In UK, a financial service company may consulted by competing companies. Therefore it is vital to have a lawfully enforceable security policy.

3

- Brewer and Nash (BN) proposed Chinese Wall Security Policy Model (CWSP) 1989 for this purpose

"Simple Security", BN asserted that

"people (agents) are only allowed

access to information which is not

held to conflict with any other

information that they (agents)

already possess."

Simple CWSP(SCWSP):

No single agent can read data X and Y

that are in CONFLICT

SCWSP says that a system is secure, if

“(X, Y) CIR X NDIF Y “

CIR=Conflict of Interests Binary Relation

NDIF=No direct information flow

SCWSP says that a system is secure, if

“(X, Y) CIR X NDIF Y “

“(X, Y) CIR X DIF Y “

CIR=Conflict of Interests Binary Relation

SCWSP requires no single agent can read X and Y,

- but do not exclude the possibility a sequence of agents may read them
Is it secure?

The Intuitive Wall Model implicitly requires: No sequence of agents can read X and Y:

A0 reads X=X0and X1,

A1 reads X1and X1,

. . .

An reads Xn=Y

CompositeInformation flow(CIF) is

a sequence of DIFs , denoted by

such that

X=X0X1 . . . Xn=Y

And we write X CIF Y

NCIF: No CIF

Aggressive CWSP says that a system is secure, if

“(X, Y) CIR X NCIF Y “

“(X, Y) CIR X CIF Y “

Simple CWSP ? Aggressive CWSP

This is a malicious Trojan Horse problem

- Theorem If CIR is anti-reflexive, symmetric and anti-transitive, then
- Simple CWSP Aggressive CWSP

- CIR: Anti-reflexive, symmetric, anti-transitive

Cp -classes

CIR-class

Cp -classes

Association mining by Granular/Bitmap computing

- Theorem 1:
All isomorphic relations have isomorphic patterns

- Bitmaps in Granular Forms
- Patterns in Granular Forms

1. All isomorphic relations are isomorphic to the canonical model (GDM)

2. A granule of GDM is a high frequency pattern if it has high support.

1. The granules of GDM generate a lattice of granules with join = and meet=.

This lattice is called Relational Lattice by Tony Lee (1983)

2. All elements of lattice can be written as join of prime (join-irreducible elements)

(Birkoff & MacLane, 1977, Chapter 11)

Theorem. Let P1, P2, are primes (join-irreducible) in the Canonical Model. then

G=x1* P1 x2* P2

is a High Frequency Pattern, If

|G|= x1* |P1| +x2* |P2| + th,

(xj is binary number)

|x1*{v1v5v6}=(20, 3rd)

+x2*{v2} =(10, 3rd)

+x3*{v3v4}=(10, 2nd)

+x4*{v7} =(20, 4th)

+x5*{v8v9} =(30, 1st)|

= x1*3+x2*1+x3*2+x4*1+ x5*2

|x1*{v1v5v6}+x2*{v2}+x3*{v3v4}+x4*{v7}+x5*{v8v9}|

= x1*3+x2*1+x3*2+x4*1+ x5*2

1. x1=1

2. x2 =1, x3 =1, or x2 =1, x5 =1

3. x3 =1, x4 =1 or x3 =1, x5 =1

4. x4 =1, x5 =1

|x1*{v1v5v6}+x2*{v2}+x3*{v3v4}+x4*{v7}+x5*{v8v9}|

= x1*3+x2*1+x3*2+x4*1+ x5*2

1. x1=1

|1*{v1v5v6} | = 1*3=3

(20, 3rd) |{v1v5 v6 v7 } {v1 v2v5 v6 }|=

|{v1v5 v6 }|=3

|x1*{v1v5v6}+x2*{v2}+x3*{v3v4}+x4*{v7}+x5*{v8v9}|

= x1*3+x2*1+x3*2+x4*1+ x5*2

x2 =1, x3 =1, or x2 =1, x5 =1

|x2*{v2}+x3*{v3v4}| =(1020, 3rd)

|x2*{v2}+x5*{v8v9}| =(10, 2nd) (10, 3rd)

x3 =1, x4 =1 or x3 =1, x5 =1

x4 =1, x5 =1

|x1*{v1v5v6}+x2*{v2}+x3*{v3v4}+x4*{v7}+x5*{v8v9}|

= x1*3+x2*1+x3*2+x4*1+ x5*2

x3 =1, x4 =1 or x3 =1, x5 =1

| x3*{v3v4}+x4*{v7}| =(10, 2nd 3rd)

| x3*{v3v4}+x5*{v8v9}| =(10, 2nd) (30, 1st)

x4 =1, x5 =1

|x1*{v1v5v6}+x2*{v2}+x3*{v3v4}+x4*{v7}+x5*{v8v9}|

= x1*3+x2*1+x3*2+x4*1+ x5*2

x4 =1, x5 =1

| x3*{v3v4}+x5*{v8v9}| =(20, 4st) (30, 1st)