ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS", Bangkok, May 23-25, 2001 
202 
More specifically, for a given term a, set B contains all terms 
with unknown connection value to a. B = {b }, (i=1 
i 
keywords into independent categories, or called multiple 
independent layers, the users can operate a query easier and 
get better results close to the desire. Moreover, the query 
speed will be improved. 
Assume a document T is one of the query results of a, and 
r contains a term set C. C = { c }, (j=1,...}. Set D is the 
j 
intersection of B and C. . D = {d }> (k=1 
k 
R represents the corresponding relevance relation of 
k 
d and a, m( d , a ). R is determined as 
k k k 
follows: 
The rule to divide keywords into multiple independent layers is 
described as follows: 
Given A is a set of terms, B and C are subset of A, and 
A = B u C . B={b } (j=1.-)- C={ c } 
j k 
(k=1,...). If for any j and k, J3 (b ,c ) = 0 and 
j k 
ß ( c , b ) = 0 . then A can be divided into two 
k ’ j 
independent layer B and C. 
1. When a user input a query that contains only term a, a list 
of documents including T are given as query results. The 
decision on the degree how the document F is related to 
the query is based on R with prior values . 
k 
2. The user examine the document T and indicates whether 
it is relevant to a or not. 
3. Then the value R is modified to either 
P k 
f 
P = min[ 1, p * (1 + s )] 
k k 
or 
11 
P = max[ 0 , P * (1 - e )] 
where e e [ 0 ,1 ] is the change scale. The query is 
evaluated again with p ' and p 
k k 
4. The fuzzy value for this relation is adjusted to the value 
which leads to the best answer. 
The approach used to adjust the strength of a relevance 
relation is simple and efficient. However, the performance is 
possible to be further improved if the users’ feedback can be 
classified. For example, the experts' feedback should have 
more weight than those of amateur users'. It is an interesting 
topic to identify the experts' feedback from the amateur users', 
or more practically, to divide the feedback into groups, then 
identify one group containing more feedback from experts than 
other groups. The analysis of the feedback shows that the 
feedback from experts has some different characteristics from 
that from the amateur users on statistics. The results might be 
used in No-Name in the future 
MULTIPLE INDEPENDENT LAYERS STRUCTURE 
Most search engines provide one textbox to users. Users input 
all the keywords and organize them with some simple binary 
combination like "And" and "Or". Some search engines 
divides the keywords into several overlapped categories like 
Yahoo. Some search engines has begun to divide the keyword 
into independent categories. However, the importance for 
search engines to divide keywords into independent 
categories has not been fully recognized. In fact, by dividing 
No-Name currently support two independent layer: Term layer 
and Location layer (Figure 2). Term layer has a tree-like 
structure containing about 2,000 terms. Location layer also has 
a tree-like structure containing 7 continents and about 200 
countries that are involved into GIS. The tree-like structure of 
term layer and location layer makes them easy to expand and 
be organized. Term layer and Location layer meet the 
requirement to be independent. The terms in term layer has no 
connection strength with the terms in location layer, and it 
accords to the intuition: A document represented by term 
"USA" has no "cause and effect" relation with a query 
represented by "GIS", and vice versa.