Constructing a Personal Web Map with Anytime-Control of Web Robots
Seiji Yamada

Norikatsu Nagino

CISS, IGSSE, Tokyo Institute of Technology
4259 Nagatsuta, Midori, Yokohama 226-8502, JAPAN{yamada, nagino}@ymd.dis.titech.ac.jp
Abstract

this situation, the WWW is very useful for a user who wants
to gather interesting information. However there is a significant issue that a user does not know where the information
exists. A practical and simple solution of the problem is
to use a search engine like MetaCrawler, AltaVista, YaHoo
with the interesting information as a query. The search engine provides a list of relevant Web pages to a user. Unfortunately, since a database of a search engine is very huge
and adequate filtering is hard, many Web pages including
irrelevant ones may be indicated.
While search engines having large databases are valid, it
is important that a user constructs a small, personal databaseof relevant Web pages to his/her interest like Bookmarks.
Since such a Web page database includes only interesting
Web pages, irrelevant Web pages are not indicated for auser’s query like large search engines. For the personal
database of Web pages, it is important that a user can control
the construction of a database to customize it. We call sucha database PWM: Personal Web Map. PWM is a structured
database as a classified Web pages and constructed underuser’s control. A user can determine keywords as input toa PWM system and a PWM is built by gathering relevant
Web pages to them and classifying the gathered Web pages.
Also a PWM system show a 2D-map of PWM through a
Web browser and a user can give feedback on his/her preference to the system. The construction of PWM is interactive
since a user can control the granularity on a PWM.
In this paper, we propose a PWM and develop a method
to construct it. First we define a PWM as a structured
database. Next we explain anytime-control of multiple Web
robots which is suitable to build a PWM and make experiments for evaluation. A PWM system is considered as amulti-agent system using a blackboard model. The blackboard indicates the density distribution in every dimensions
under vector representation of Web pages. Clustering a
PWM and indicating its 2D-map are done by SOM. Finally
we make experiments to evaluate a PWM using four Web
robots and verify the utility, and limitation is discussed.
WebWatcher[7] and Letizia[9] are able to indicate theIn this paper, we propose a PWM (Personal Web Map)
which is a personal and small database of interesting Web
pages to a user, and develop a method to construct it under
the user’s control of multiple Web robots. Though generalsearch engine with large databases like YaHoo, AltaVista,
MetaCrawler are valid, it is important that a user constructs
a small, personal database of relevant Web pages to his/herinterest like Bookmarks. For such a Web page database, we
propose a PWM: a personal database of interesting Web
pages to a user which he/she can control its construction.
First a user gives keywords indicating his/her interest to asystem, and it constructs a PWM concerned with the keywords. For building a useful PWM, it is necessary that auser can interrupt the construction of a PWM anytime andinstruct a sub-field in which a PWM should be expanded
more. For this function, we develop an anytime-control
algorithm for multiple Web robots. A density distribution
blackboard is used, and an uniform distributed PWM isbuilt. Whenever a system is interrupted by a user, it provides
a valid PWM in terms of keeping search space wide, andindicates many alternatives on which he/she wants more information. From Web pages in a database, document vectors are generated and used to construct a 2D-map of a
PWM by using self-organization maps. A user easily recognizes a PWM through the 2D-map, and gives instruction byclicking a node about which he/she wants more detail information. We made experiments by users and found out that
our method outperformed breadth-first search for constructing a useful PWM. As results, a PWM system is considered
as a promising approach to assist a user in gathering relevant information in the WWW.

1 Introduction
The accessible information through the Internet is increasing explosively as the WWW becomes widespread. In
1

2 Personal Web Map
First we define a PWM (Personal Web Map). PWM is alayered sets of Web pages which is built under user’s control. Fig.1 shows a structure of PWM consisting of sets
of gathered Web pages called W PSs (Web Page Sets). The
first layer includes a W PS00 including directly relevant Web
pages to keywords given by a user as input. W PSi j means
the jth W PS in the ith-layer, and a layer may include multiple W PSs. After gathering, the Web pages are classified
by SOM and clusters (white circles in Fig.1) are generated. A user allows a PWM system to construct layers recursively. When he/she points a cluster in a W PSn j about
which he/she wants to know more, a system generates themore detail W PS(n+1)k for the cluster k in a (n + 1)th layer.
With such a procedure, a user is able to control construction of PWM for including detail information about which
he/she wants to know more.
In a PWM, each W PS contains about hundreds of Web
pages. You may claim a PWM must be included by adatabase of a large search engine. However we often faceWPS 00Layer- 0

granurality
High

WPS 10. . . .

Layer-1. . . .

Web pages which a user wants to see next. Using browsing history, they learn to predict useful Web pages for auser. These systems are consider as customizing systems
that learn user’s preference in searching Web pages. Unfortunately the systems do not build personal Web page
databases.
SPHINX[12] is a framework in which a user can achieve
the personal crawling tasks. However the customization is
on searching of Web pages, not building a personal Web
page database. Thus the purpose is different from ours.
Fish search[2] is a distributed search algorithm for gathering relevance information. Agents have energy, whichis gained from relevant Web pages and lost from irrelevant pages. Agents having hight-energy can reproduce themselves and others having low-energy may die.
ARACHNID[10] is a more excellent system that can gather
information by learning information agents. In the similar
way to fish search, agents reproduce themselves or die depending on their energy, and obtain energy from relevant
Web pages. Furthermore ARACHNID is adaptive to thechange of an environment. These systems do distributed
control of agents, however the control method is inspiredby an artificial life approach and different from anytimecontrol.
Kohonen’s SOM (Self-Organizing Map) have been used
to classify documents in the WWW. WEBSOM[5][6] isable to classify articles in network news groups and display the clustered results. WEBSOM just does clustering
documents when they are given, and has no contribution to
information gathering. In this research, we use SOM forclustering gathered Web pages.

: a cluster of Web pages: WPS

Figure 1. PWM: Personal Web Map.

the facts that many interesting Web pages are not captured
by such a large search engine. We consider a PWM whichis controlled by a user is promising for fast personal searchengine with a necessary and sufficient Web page database.

3 Overview of a PWM system
Fig.2 shows the overview of a PWM system. It consistsof PWM, a Web page database, a density distribution blackboard, multiple Web robots and an information retrieval system like a search engine. The system provides two different modes in a user’s purpose: Building mode and Utilizing
mode. In a building mode, a user can control to construct
a PWM. The input of the system are keywords on which
a user wants to build a PWM, and a system outputs a 2Dmap of a PWM to him/her. A user can click a node on the
2D-map for gathering information about it. In a utilizing
mode, a user can use the Web page database to search fornecessary information. He/she may browse the database or
input queries to a search engine. The 2D-map of PWM isalso indicated to a user as a structured map like YaHoo’s
interface.
The two modes do not need to be phased. A user can interrupt the system anytime, and input keywords for a building mode or input queries to use the search engine for autilizing mode.
Multiple Web robots gather relevant Web pages to keywords. They monitor a density distribution blackboard, andtry to gather pages in the most sparse area. This is calledanytime-control, and we will mention its detail in the next
section. The anytime-control makes a database of Web
pages uniform and provides a valid 2D-map with many alternatives a use can select. Web pages gathered by Web
robots are stored in a Web page database. All Web robots
work asynchronously.
A density distribution blackboard is updated with key-4 Generating a database for PWM with multi
Web robots
How can we construct a PWM? For efficiency, we should
use multiple Web robots for building a PWM. Next how
should we control them? We propose anytime-control: anovel control method for Web robots to effectively gather
information for PWM.

4.1 Keyword vectors and a Web page database
In a density distribution blackboard, Web pages are represented with keyword vectors which are similar to term
vectors used for the vector space model[13]. A keyword
vector Vp of a Web page p described in the following.
Vp= (v p1 , v p2 , · · · , v pn )
f (p,t1 ) f (p,t2 )f (p,tn ),,···,=m(D,t1 ) m(D,t2 )m(D,tn )where m(D,t) = max f (p,t)
p∈D

The value v pi stands for normalized occurrence frequency of the input keyword ti in a Web page p, and shows
the importance of ti in the Web page. The max(ti ) of a keyword ti is obtained by the following procedure. A keyword
ti is inputted to a search engine and the most relevant Web
page to ti is fetched. The occurrence frequency of ti in the
Web pages is max(ti ). Thus max(ti ) means a maximum occurrence frequency of ti over a large number of Web pages.
Since the max(ti ) is computed once for each keyword andfixed, a system can decrease the computational cost for updating a density distribution blackboard. The order of thecomputation is linear: O(n) where n is the number of stored
keyword vectors in a density distribution blackboard. They
are normalized by the maximum value in Web pages of a
Web page database D, thus the range of values is [0, 1].

4.2 A density distribution blackboard
A blackboard system[4] provides a useful architecture tocontrol a distributed multi-agent system. Agents read andwrite the information to layered shared memory, called ablackboard, and share them flexibly. Thus we use a blackboard for Web robots to share the information. The shared
information is a density distribution which is the distribution of the number of keyword vectors in a Web page
database over the keyword vector space.

Vector value of keyword-2

word vectors detected from Web pages in the database. A
2D-map is generated depending on document vectors by
Self-Organizing Maps.

1
[6]

[5]

[3]

[9]

0.5

0

0.5

1

Vector value of keyword-1

Figure 3. Density distribution blackboard.

The range [0, 1] of each axis in a n-keyword vector spaceis divided into m patches. Since the n-keyword vector space
has n dimensions, mn patches are generated in total. Next
the densities for each patches are computed, and they are
stored in a density distribution blackboard. Fig.3 showsa two dimensional density distribution blackboard for two
keywords. Each axis is divided into two patches. The black
points and the numbers within blankets indicate keyword
vectors of Web pages and densities of patches respectively.
If a patch has high density, it has been explored well andWeb robots do not need to investigate there. If a patch has
low density, it was not explored sufficiently and Web robots
should investigate there more.

4.3 Anytime-control of Web robots using a blackboard model
4.3.1 Control policy for PWM
Though many search engines use multiple Web robots forgathering Web pages, few ones control them effectively.
Thus we propose anytime-control: a novel method for controlling Web robots to build a PWM.
An important point to build a PWM interactively is thatthe building process may be interrupted anytime. A PWM
system allows a user to interrupt information gathering tosee intermediate results and control it. Hence it shouldbe able to indicate appropriate results whenever a user requests. We consider the appropriate results are Web pages
which were gathered uniformly on keywords. If it does notcontrol Web robots, the obtained PWM will be uneven andits coverage will be far more narrow than an uniform one.
Fig.4 shows an uneven PWM and an uniform PWM

Building mode
Keywords
PWM
User

SOM

2D-mapDocument
vectorWeb pages
database

KeywordvectorDensity distribution
blackboad

2D-mapUtilizing mode

Query

SearchengineWeb robot-1...

Web robot-n

Hits list
Anytime-control

User

WWW

0

Keyword-2

Keyword-2

Figure 2. Overview of a PWM system.

A

Keyword-1(a) An uneven PWM

4.3.2 A procedure for a Web robot
B0

Keyword-1(b) An uniform PWM

Figure 4. An uniform PWM.

Thus we need an algorithm that returns an uniform PWM
whenever a user interrupts. We developed such an algorithm
which is called anytime-control 1. The control is a simple
distributed procedure shown in the following. Each Web
robot acts using the algorithm asynchronously, and stops
when a user interrupts it or the number of obtained Web
pages becomes the given a limit number or a limit time has
come.
Anytime-control procedure for a Web robot
1. See a density distribution blackboard and determine
the most sparse patch.
2. Select a Web page α randomly in the patch.

when ten Web pages were gathered. The two keywords
were given to a PWM system, and the X and Y axis indicate the relevance to keyword-1 and keyword-2 respectively. The black circles stand for start Web pages, and thecrosses indicate keyword vectors of gathered Web pages.
Though both of the PWM started from the same keyword
vector, the results are different in the coverage (the circle A
and B). Since a user selects a keyword vector about which
he/she wants to gather more detail information, the coverage of subsequent layers is restricted within circle A and B.
Thus the coverage of an uneven PWM becomes far morenarrow than that of the uniform one. Obviously we prefer a
PWM with a wide coverage.

3. Select a linked Web page β randomly in the Web pageα.
4. Fetch a html file of the page β.
5. Generate a keyword vector from the obtained file andwrite it into a density distribution blackboard. Also
store the file in a Web page database.
In the above procedure, Step 1 makes a PWM uniform.
Each Web robot tends to explore in an area with the least
1 This name is inspired by an anytime-algorithm[1] which, returns valid
results anytime, has been studied in real-time problem solving in artificial
intelligence.

information, and this keeps the density of Web pages uniform. Additionally, URLs of the fetched Web pages are
stored, and the Web pages are not selected in Step 2 and 3.
Note that in order to gather Web pages in the same patch
to the Web page α for Step 3 and 4, a system tries to get
Web pages linked from the Web page α. This is a valid
way supported by some evidences. Menczer[10] pointed
out R > G, where R is the conditional probability that a Web
page is relevant given that it is linked by another Web pagethat is relevant with the same query, and G is the generalityof the query, i.e., the fraction of Web pages that are relevant.
Since R > G means that is is more likely to hit a relevant
Web page from another relevant Web page than from any
random Web page, the linked Web pages in Step 3 and 4
tend to be within the same patch to the Web page α. Anotherevidence is a real-time search mode of WebCrawler[3]. The
algorithm assumes that following links from Web pages thatare similar to what the user wants is more likely to lead torelevant Web pages than following any link from any Web
page. Under the assumption, WebCrawler works well.
4.3.3 A procedure of a PWM system
Eventually the procedure of a PWM system is described in
the following.
1. Initialize a W PS00 as a empty set, and set Current
W PSi j by W PS00 .
2. A system inputs MetaCrawler[11] the keywords as aquery, and obtains the most relevant 10 Web page. A
PWM system initializes a density distribution blackboard by giving it the document vectors of the 10 Web
page as a start point.
3. Start Web robots under anytime-control for gathering
relevant Web pages.
4. If interrupt of a user is given, work SOM to make a 2Dmap of PWM and display it to a user. If a user clicks anode in the 2D-map, set W PSi j by i = i + 1, and go to
Step 2.
5. If the number of gathered Web pages becomes the limit
number, stop and wait a user’s request.

5 Human computer interaction
5.1 SOM-based display
SOM (Self-Organizing Maps)[8] have been applied tobuild a 2D-map from a large text database. Because
the learning algorithm is simple, and the input of highdimensional vectors are automatically classified. For example, WEBSOM[5][6] has been developed for clustering
a lot of articles for network news.

Since the keyword vector has the identical dimension to
the number of keywords and it is usually more than three,a system needs to reduce the dimension to two in order toindicate a PWM to a user. Thus we can construct a 2Dmap of a PWM by using SOM. We explain the SOM-based
procedure in the following. SOM learns with random input vectors in the document vector space, and the document
vectors of gathered Web pages are given to the learned SOM
and classified.
1. Generate a set S of document vectors from Web pagesof current W PS in a Web page database. The document
vector is slightly different from a keyword vector innormalization. They are normalized as a unit vector
using the following formula. E p and f (p,t) are thedocument vector of a Web page p and the occurrence
frequencies of a word t in a Web page p.
Ep= (e p1 , e p2 , · · · , e pn )
f (p,t1 ) f (p,t2 )f (p,tn )
,
,···,
=
l(p)
l(p)
l(p)

n

∑ f (p,ti )2

where l(p) =i=1

2. Fig.5 shows the structure of SOM. Construct SOM using the number of keywords as the input nodes and cas the number of competitive nodes. The competitive
layer is set to two dimension.
3. Generate a set R of random document vectors with random values through [0, 1] in each dimension of a document vector. Give the R to SOM repeatedly, and update
is done as the following[8].
(a) Let an input vector and weights of links fromall input nodes to a competitive node ui be E p= [e1 ,e2 ,· · ·,en ] and Ui = [ui1 ,ui2 ,· · ·,uin ] respectively.
(b) SOM compute the similarity between the input
vector and competitive nodes. The similarity isevaluated by Euclidean distance. using th the following formula.
E p − Ui  = ∑(e p j − ui j )2
j(c) Since we need a 2D-map, a square having thewinner as the center is used as neighborhood.
The formula for updating the weights of neighbors is shown in the following, where the α is alearning rate. The α and the size of neighborhood
are scheduled to decrease as learning progresses.
oldunew
i j = ui j + ∆ui jCompetitive
layer

...
Node-1 Node-2 Node-n(e 1, e 2, ..., en )Input layer
Input vector

Figure 5. Self-Organizing Network.
∆ui j =

α(e j − ui j )0: i in the neighborhood: otherwise

4. After learning, the set S of document vectors of gathered Web pages is inputted to a learned SOM, winner
nodes for each vector are determined, and classification is done. The winner node of a Web page indicatesthe class. Also unit keyword vectors are inputed to alearned SOM and the winner nodes are labeled withcorresponding keywords. The winner nodes are called
keyword nodes, and the unit keyword vector of keyword n is (0, · · ·, 1, 0, · · ·, 0) where only the nth valueis 1. The keyword nodes are colored differently, andthe other nodes among them are colored in graduation.
Furthermore the size of a node is in proportion to thenumber of keyword vectors whose winner is the node.
We show an execution example in which a user wanted
to know about “intelligence information retrieval in the
WWW” and input five keywords: “intelligence”, “retrieval”, “gathering”, “WWW” and “information”. The
limit number was set 1000 for anytime-control, and it took
16 hours for a PWM system to fetch the 1000 Web pages.
The number of patches on each axis is ten, thus the amountof patches is 105 because the number of keywords is five.
We use two Web robots for gathering Web pages.
After gathering Web pages, a learned SOM with random document vectors classifies the Web pages. We set the
number c of competitive nodes by 100 which are positioned
10×10.
The number of training examples was set 10000, hence
the 1000 document vectors of gather Web pages are inputtedto SOM ten times. The initial learning rate and the initialneighborhood were set α = 0.5 and a square with a 5 length
sides (ds) respectively, and they were decreased linearly using following formulas where t is the number of training
examples.

Figure 6. 2D-display of PWM.


α = 0.5 1 −

t 
10000


d = 5 1−

t 
10000

An executed example of 2D-map for first layer PWMs
is shown in Fig.6. The 2D-map was generated from keywords of subject-1 in Table. 1. You can see colored keyword nodes, and the distance between them stands for theirsimilarity. The size of a node stands for the number of Web
pages classified in the node.

5.2 User feedback
Using a 2D-map, a user can easily point nodes about
which he/she wants to know more. When a node is pointed
by a user, a system opens a dialog window called a nodeinfo window in which a user can see the titles of the Web
pages whose winner node is the pointed node and the occurrence frequencies of all words in the Web page. A user
determines whether he/she wants to know more about thenode, and clicks the more detail button for request. Notethat a 2D-map provides access to intermediate nodes except
keyword nodes. This makes user’s selection of intermediateconcepts easy.
If the more detail button is clicked, a PWM system will
gather Web pages for constructing the more detail W PS in
the next deeper layer (Fig.1). The procedure is basicallyStandard deviation

similar to one described in 4.3.2, however the density distribution blackboard is restricted. The occurrence frequencies
of n keywords is restricted within the range [min1 , max1 ],[min2 , max2 ], · · ·, [minn , maxn ], where mini and maxi are
the minimum value and the maximum value of ith keyword
in the Web pages included in the pointed node. Thus a system gathers only the Web pages having keywords includedin the restricted area. This restricts the next W PS within theneighborhood of the pointed node.

70
60
50
40
30
20
Web robot search
Anytime control

10

5.3 Search engine
0

A PWM system also provides a search engine on a Web
page database to a user. The search engine is built by using afull-text search method straightforward. Unfortunately thissearch engine is not fully implemented at present.

5.4 Evaluation
5.4.1 Evaluating anytime-control
In order to verify the effect of anytime-control, we make experiments. To compare with anytime-control, we use general Web robot search used in many search engines. In general, Web robots are not control effectively and the strategy
is a simple breadth first search. Thus we call it Web robot
search described in the following.
Web robot search
1. Initialize the Web page list F = [ ].
2. Set a current Web page α by a start Web page, andappend all linked pages in α to F.
3. Pick the head page β in F and remove it.
4. Fetch the page β.
5. Add all linked pages in β into the tail of F.
6. Go to Step 3.
For evaluating the effect of anytime-control, we investigate the standard deviation SD of the number of Web pages
in each patch, which is describing like the following formula. xi and x are the number of Web pages in patch i andthe average of them over all the patches. n is the total of
Web pages. The SD indicates the scattering of Web pages,
thus the PWM is more uniform as the SD is smaller.

1 n
SD =
∑ (xi − x)2
n i=1
Fig.7 shows the experimental results. In the graph, the xaxis and y-axis stand for the number of gathered Web pages
and SD. As seeing from the graph, anytime-control is able
to keep a PWM more uniform than Web robot search.

500

1000 1500 2000 2500 3000 3500 4000
The number of gathered Web pages

Figure 7. Uniformity of anytime-control.

5.4.2 Evaluating the gathered relevant Web pages
The purpose of a PWM system is to gather relevant Web
pages to a user’s interest effectively. Thus we make experiments with 8 subjects for evaluating Web pages gathered
by a PWM system. The subjects are Master course students
in a computer science department. Two methods: a PWM
system with anytime-search and that with Web robot search
are compared through the following identical procedures insame settings. Then we investigate the number of relevant
Web pages in gathered Web pages in the 1st and 2nd layer
W PSs. We implemented a PWM system using Perl, Java,SQL and parser programs on Linux in a Pentium II (233M
Hz) PC-AT machine with 128M RAM. Four Web robots
works on the machine as different processes, and they are
not in parallel .
First the subjects independently input 5∼7 keywords(partially shown in Table. 1) to a PWM system and a systemgathers relevant Web pages for three hours using anytimecontrol or Web robot search. Then first layer PWMs are
generated. The number of patches on each axis is five, thus
the amount of patches is 5n where the number of keywords
is n. A system generates the 2D-map of first layer PWM
and indicates it to a user, and he/she selects a node about
which he/she wants to know more. Next a system gathers
the second layer PWM for three hours, and stops.
For 8 subjects, we investigate the average number of
relevant Web pages in the first, second and both layer
PWM generated by anytime-search and Web robot search.
The relevance is determined by subjects. The experimental results are shown in Fig.8 where the average of ratio
number o f relevant pages gathered by anytime−search= TThehenumbero f relevant pages gathered by Web robot search for firstlayer, second layer and both of the layers is indicated. Whenthe ratio is more than one, anytime-search gathered more
relevant Web pages than Web robot search. As seeing fromthis figure, our PWM system using anytime-search outper-

in the WWW.
Table 1. Keywords given by subjects (partially).
Subject-1
Subject-2
Subject-3

KQML, multi agent, communication,
collaboration, interaction, interface, userGP, khepera, Genetic Programming,
robot, mobile, service, intelligenceagent, www, navigator,
personalize, profile, AI

Average of ratio

4
3.5
3
2.5
2
1.5
1
0.5
0
1st layer

2nd layer

Total

Figure 8. The average ratio of relevant Web
pages.

forms that using Web robot search in all cases, and we findout our approach is promising to gather relevant Web pages
effectively. Specially the 2nd layer shows the advantage ofanytime-search.

6 Conclusion and open problems
We proposed PWM (Personal Web Map) for a user togather interesting Web pages efficiently, and developed a
PWM system. The system is able to anytime-control for
multiple Web robots to gather relevant Web pages effectively. For controlling Web robots, a density distribution
blackboard is used, and an uniform distributed PWM isbuilt. From Web pages in the database, document vectors
are generated and used to SOM-based classification for constructing 2D-map. A user easily recognizes PWM throughthe 2D-map, and gives feedback by pointing a node about
which he/she wants more detail information. We also implemented a PWM system and made experiments for evaluation. As results, we found out that our PWM is a promising
approach to assist a user in gathering relevant information

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