Information Gathering and

Searching Approaches in the Web

Information Gathering and
Searching Approaches in the Web
Seiji YAMADA
Department of Computational Intelligence and Systems Science
Interdisciplinary Graduate School of Science and Engineering
Tokyo Institute of Technology
4259 Nagatsuta-cho, Midori-ku, Yokohama, 226-8502 JAPAN
yamada@ymd.dis.titech.ac.jp
Hiroyuki KAWANO
Department of Systems Science
Graduate School of Informatics
Kyoto University
Sakyou-ku, Yoshida-honmachi, Kyoto, 606-8501 JAPANkawano@i.kyoto-u.ac.jp
Received 15 November 2000

Abstract

The information accessible through the Internet is increasing explosively as the Web is getting more and more widespread. In
this situation, the Web is indispensable information resource for both of
information gathering and information searching. Though traditional information retrieval techniques have been applied to information gatheringand search in the Web, they are insuﬃcient for this new form of information source. Fortunately some AI techniques can be straightforwardly
applicable to such tasks in the Web, and many researchers are trying thisapproach. In this paper, we attempt to describe the current state of information gathering and searching technologies in the Web, and the application of AI techniques in the ﬁelds. Then we point out limitations of these
traditional and AI approaches and introduce two approaches: navigation
planning and a Mondou search engine for overcoming them. The navigation planning system tries to collect systematic knowledge, rather thanWeb pages, which are only pieces of knowledge. The Mondou search engine copes with the problems of the query expansion/modiﬁcation basedon the techniques of text/web mining and information visualization.

1

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Hiroyuki KAWANO

Keywords
Engine§1

The World Wide Web, Information Gathering, Web Search

Introduction
As the Internet and the Web are developing explosively, we can easily

utilize them as useful information resources. Since the Web is very huge andconstantly updated, information and knowledge in the Web is rich and recent.
In this situation, there is a possibility that we can do information gathering andinformation searching in the Web anytime, anywhere to obtain our target Web
pages. However there are signiﬁcant problems. In general, a user does not know
where his/her target Web pages exist. Thus a Web search engine is used to search
for the target pages. Unfortunately, since the ﬁltering is not suﬃcient, a hit list
from a search engine may include a large number of non-relevant Web pages.
This problem also inﬂuences information gathering. If information searching is
not correct, the gathered information is also meaningless.
Though traditional information retrieval techniques 28) have been applied
to information gathering and search in the Web, not all of the problems are
solved. For example, ﬁltering of a hit list using semantics and the context of Web
pages is hard for information retrieval methods and natural language processing
is necessary. Fortunately some AI techniques are straightforwardly applicable tosuch tasks in the Web, and many researchers are trying this approach.
In this paper, ﬁrst we describe the current state of information gatheringand searching in the Web, and survey systems developed as AI applications. Next
we point out open problems in the ﬁelds. Then we introduce two approaches:navigation planning and a Mondou search engine for overcoming the issues.

§2 Information gathering and searching in theWeb
In this section, we survey conventional approaches and implemented
systems for information gathering and searching in the Web, then we describe
their limitations.

2.1

Traditional systems[1]

Softbot, Sage and Occam
There are studies on AI planning to generate procedures for gathering

Information Gathering and

Searching Approaches in the Web

3

information through the Internet. The Softbot 5) provides a planning framework
which consists of operators and a planner in the similar way to STRIPS planning.
The environment and the operator are described by ﬁrst-order predicate calculus.
An operator represents an action by UNIX commands like mkdir, find and soon. Softbot has user interface for a naive user to describe operators without
knowledge on predicate calculus. A complete and sound partial-ordering planneris used in Softbot. Occam 15) is also a planner for gathering information. It is
more eﬃcient and able to reason about the capabilities of diﬀerent information
sources.
Considering a command “send E-mail to Dr. Yamada in TiTech” is given
to Softbot as a goal description. First it tries to reduce the ambiguity in thecommand. Concretely Softbot identiﬁes a person named Dr. Yamada in TiTech.
In this processing, knowledge about the organization of TiTech is used. Next
Softbot ﬁnds applicable operators and generates a plan for achieving the given
goal. In this example, the operators describing finger and sendmail are selected
and added into a plan. When a complete plan is obtained, Softbot executes it andthe goal is satisﬁed. Sage 14) is also developed for integrating planning, execution,
replanning and sensing to gather information in distributed resources. The aimof these studies is to generate a plan as a procedure of gathering information,
and a plan consists of UNIX commands or database operations.
[2]

ARACHNID
ARACHNID17) (or InfoSpider 18) ) is an information gathering system

which manipulates multiple search agents in the Web. ARACHNID applies
an A-Life like approach for controlling the agents. Each agent has energy fordescribing its activity. If an agent ﬁnds a relevant Web page, its energy increases
and otherwise teh energy decreases. When an agent gets fully active, it produceschildren, and if it signiﬁcantly looses energy, it will be dead. In ARACHNID,
since information gathering is done on-line, it can obtain more recent information
than a search engine using oﬀ-line search.
[3]

Meta search engine
While research on a single search engine is active, meta search engines
have also been developed. A meta search engine utilizes other search engines
for information search in the Web. First it sends a query inputted by a user
to several search engines. Next it receives a hit lists from them. Finally it

4Hiroyuki KAWANO

integrates the results and indicates them to a user. MetaClower23) 24) is a typicalmeta search engine. SavvySearch8) is able to characterize search engines using
user’s feedback.
[4]

Web robot
Programs called Spider, Clower, Web robot are utilized to gather Web
pages for construction of a search engine database9) 3). When start points are
given to a Web robot, it begins to trace the links from the current Web pages andgather pages using breadth-ﬁrst search. In general, the purpose of a Web robot
is to collect Web pages as many as possible, and it has no control mechanism
for selective information gathering.
[5]

Recommender and shopping agent
WebWatcher 10) is a recommender agent which guides an user in Web
browsing. In the similar way to a human guide in a museum, WebWatcher caninfer and recommend the Web page candidates which a user seems to selectnext by using his/her browsing history. The knowledge for prediction of the
Web pages to which a user goes next is acquired by machine learning methods.
Reinforcement learning and instance-based learning are applied to obtain theknowledge. Letizia 16) is also able to recommend to a user the next interesting
Web pages.
Some learning systems have been developed for information gatheringand browsing in the Web. ShopBot 4) learns the text pattern indicating the priceof CD-ROMs, and searches for the cheapest one more eﬃciently than a human.
Fist ShopBot collects both of positive and negative training examples by actively
investigating on-line shop Web sites. It tries to give diﬀerent type of input tothe site and the returned Web pages are dealt as training examples. Using them,
ShopBot can utilize an inductive learning method and obtain a description of avender site.

2.2

Open problems

As mentioned earlier, various AI applications to information gatheringand searching in the Web have been developed thus far. Some systems successfully overcome the limitation of traditional information retrieval approach like
TFIDF indexing, statistical information and so on. However we can point out
open problems in the following.

Information Gathering and

Searching Approaches in the Web

5• Gathering pieces of knowledge: In all of conventional systems, the gathered information is only a set of pieces of knowledge. For example, any
search engine collects Web pages which mention only surface knowledge
on a query. We need more systematic and deep knowledge on a target
information described in a query.
• Appropriate combination of keywords: We need frequent patterns or associative rules to determine much more suitable combination of keywords ina query. For instance, when we describe a query, we usually consider the
distance of keywords in the meaning networks, and evaluate the coverageof expressions.
In the following sections, we introduce two approaches to address theabove limitations. Navigation planning and a Mondou search engine are described.

§3

Navigation planning

The Web is very useful for a user who wants to understand a target
concept. He/she can browse helpful Web pages to understand a target concept.
However, in general, this task is very hard because he/she may not know wheresuch Web pages are, and has to search for them over the vast Web search space.
A solution to this problem is to use a search engine with the target concept asa query. However, since the retrieved Web pages are not ﬁltered suﬃciently, auser has to select useful ones from them. Furthermore, since in most cases thegathered Web pages are pieces of surface knowledge which directly explain thetarget concept and include concepts that a user does not understand, he/she
must search the useful Web pages for them using a search engine again. Thistask is repeated until a user understands the target concept, and hence wastes
time. We consider the task as planning, and propose navigation planning 26) 25)
to automatically generate a sequence of Web pages which can guide a user tounderstand a target concept deeply.

3.1

Web browsing as planning

In this research, navigation means a task that indicates a sequence of
useful Web pages to a user for guiding his/her concept understanding. The
sequence of Web pages is called a plan, and navigation planning means theautomatic generation of the plan. We can summarize the Web browsing task
for concept understanding in the following. This procedure is iterated until

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Hiroyuki KAWANO

terminated by the user.
1.
2.

Search Web pages using a search engine.
Read and understand the pages retrieved by the search engine.

3.
4.

Select unknown concepts in the Web pages.
Go to Step1 with unknown concepts as target concepts.

The procedure above is considered planning7) under the following correspondence. Using this formalization, we can apply a classical planning framework to navigation planning.
• Action: Understanding concepts in a Web page.
• State: A user’s knowledge state described with a set of words describing
concepts which he/she knows.
• Initial state: A user’s initial knowledge state.
• Goal state: A target concept described with a set of words which a user
wants to understand.
• Operator: U-Op(URL) deﬁned by the followings.
– Label: URL of the Web page
– Condition: C = {c1 , · · ·, c i }, where C means the condition words which
are necessary to understand the pages.
– Eﬀect: E = {e1 , · · ·, e j }, where E is eﬀect words which a user obtains
by understanding the page.
This navigation planning contains a signiﬁcant problem which has notbeen presented in planning. It is that the U-Op(URL) operators are not given
in advance. This is because it is impossible for a human designer to generate
the operators from all the Web pages in the Web. Hence the operators need to
be automatically generated from Web pages when they are necessary.

3.2

Generating operators from Web pages[1]

Using tag structure in a HTML file

Various methods to extract keywords from text have been studied22) .
Though most methods are based on the frequency of words, one of the mosteﬀective methods is to utilize the structure in text. Since a Web page is describedin a HTML format, we can utilize tag structures.
The prime candidates for condition words are those linked to other Web
pages, i.e. the words between <A HREF=URL> and </A>, because this tag is a

Information Gathering and7

Searching Approaches in the Web

Subgoal of node-i
query
A search engine
Goal state
Related Web pages

Node-1

Node-.... Node-Page-1....

Page- w

Generating operators.... Node-

Ope-1

....

Ope- w

Width of beam search(a) Navigation planning as
backward beam search

Generating b nodes in the next depthwith an evaluation function(b) Expansion of a node-i

Fig. 1

Navigation planning.

sign of reference to relevant topics, which are important for understanding thecurrent Web page in many cases.
Since the title of the Web page describes words which a user may acquireby reading the page, the words between <TITLE> and </TITLE> are candidates
for the eﬀect words. In the same way, headings describe knowledge which a user
may obtain by reading the section. Thus the words between <Hn> and </Hn> arealso candidates for the eﬀect words.
KeyGraph: A keyword extraction method
The extraction of condition and eﬀect words using the tag structure isnot suﬃcient. All the linked words are not necessarily candidates for condition
words, and all the condition words are not necessarily linked. Thus we look for[2]another method to assist it, and KeyGraph is found to be appropriate for thistask.
KeyGraph is a fast extraction method of keywords representing the asserted core idea in a document19) . KeyGraph composes clusters of terms, basedon co-occurrences between terms in the document. Each cluster represents a
concept on which the document is based (i.e. condition words), and terms con-

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Hiroyuki KAWANO

Fig. 2

Plan for “Concept formation”.

necting clusters tightly are obtained as author’s assertion (i.e. eﬀect words).
Furthermore the likelihood for condition and eﬀect words can be computed by
KeyGraph, and used for weight of an operator. Another merit of KeyGraph isthat it does not employ a corpus.
The extraction of condition and eﬀect words using tag structure andKeyGraph is integrated. First condition and eﬀect words are obtained with the
tag structure. Next KeyGraph also extracts condition and eﬀect words and bothof them are integrated.

3.3

Planning procedure

We have developed a navigation planning procedure. Fig.1 shows theoverview of our navigation planning system, called NaviPlan . NaviPlan automatically generates a plan for given target concepts. It uses backward beam
search 21) from a goal state (Fig.1(a)). The expansion of a node (Fig.1(b)) includes the search for relevant Web pages with a search engine and the generationof operators.
We fully implemented NaviPlan using Perl. Fig.2 shows a plan (depth= 4) generated by NaviPlan with the target concept “concept formation”. Four
useful Web pages are indicated with hyperlinked titles and URLs. By reading
the Web pages in this order, a user is able to understand “concept formation”
deeply.

3.4

Experimental evaluations

Information Gathering and

Searching Approaches in the Web

9

Now let us evaluate NaviPlan according to the usage of the system
as a navigation planner. In this experiment, we employed the search engine
MetaCrawler, for expanding nodes in the planning procedure of NaviPlan . The
system compared with NaviPlan here was also MetaCrawler.
For each of 10 subjects (8 graduate school students and 2 university
staﬀs) , we investigated the number of Web pages he/she read for understandinga target concept and the brief explanation on the goal. As results, we found out
NaviPlan achieved eﬃcient navigations to the goal, due to the strong intensionof operators to reach the goal from user’s initial state.
In order to investigate the eﬀect of having a plan instead of only one
page to read, we also made experiments using more diﬃcult target concepts forthe same subjects to understand. Consequently we conclude that planning in
NaviPlan contributes to increasing user’s knowledge until he/she understands agoal concept, by reading multiple but small number of pages.

§4Intelligent search system

In 1996, We have developped “Mondou” web search engine, which belongs to the ﬁrst generation of research oriented Japanese web search engines∗1 12). We have applied the rapid emerging technologies of data mining to themechanism of query modiﬁcation and expansion. We also implemented Java
applets in order to support the search steps. In this section, we explain major
technical aspects of Mondou systems, such as data cleaning, data mining andinformation visualization.

4.1

Architecture of Mondou search engine

The Mondou consists of the three primary modules, such as web robot,
text database and query server, which are shown in Fig. 3.
The fundamental functions of the web robot are described in Section 2.1[4]. In addition to the basic mechanisms of the web robot, our web robot parses
HTML tags and extracts the important keywords from the title, headings andsub-headings, anchor strings, and other tagged attributes. Moreover it gives appropriate weight values to derived keywords in order to measure the importanceof web pages using the heuristic functions.
∗1

The URL of Mondou web search engine is http://www.kuamp.kyoto-u.ac.jp/labs/infocom/
mondou/index e.html. The name of Mondou comes from the term of Zen, which is asophisticated discussion style in Japan, so the discovery is in the processes of questions
and answers.

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Hiroyuki KAWANO

Fig. 3The structure of Mondou.

Our text database handles not only the characters in the web pages,
but also the connectivities of hyperlinks. Therefore, the primary database tableconsists of the following attributes, “keywords, URLs, hyper links, http servers,
IP addresses”, and the system also keeps the control/management data tables
for operating the web robots.
The query server is the most important program in the Mondou system,
we implement typical data mining algorithms to derive appropriate association
rules. The associative keywords with some attribute values are visualized on thejava applets. Furthermore, in order to adjust the demand of searchers, we tried
to apply agent technologies and the integrating mechanism of heterogeneous
databases to Mondou systems.

4.2

Data cleaning by web robots
As we mentioned in Section 2.1, the simple web robot visits web servers

sequentially in the breadth-ﬁrst manner. However, we have to consider the
performance of gathering the web pages more eﬀectively, then we try to collectmeaningful or useful web pages as fast as possible.
It is so hard to discover the meaningful knowledge from a web graph,then we try to extract the interesting features of web pages using the following
two types of hyperlinks.
• Inside link: the link to other pages on the same web server.
• Outside link: the link to other pages on the other web servers.
Actually, when we browse web pages, we consider the connectivities anddistance of the web page from/to other web pages. Therefore, it may be helpful

Information Gathering and

Searching Approaches in the Web11

to analyze and characterize the connectivities of hyperlinks, in order to fastcollect important web pages in the same and diﬀerent web servers.
Therefore, we proposed a focused crawling algorithm in our paper12) ,which is based on the assumption that the important web pages are referredmany times from other servers. And it is also helpful to reduce the number ofnoisy/meaningless web pages from the view point of data cleaning.
Furthermore, in order to reduce the load of the network and the number
of web page requests, we are trying to implement cooperative web robots basedon a distributed scheduling algorithm27) .

4.3

Data mining for query modification
The research of data mining6) consists of databases, statistics and various

kinds of AI research, such as machine learning, inductive learning, knowledge
representation, information visualization and others.
The data mining plays an important role in order to support the discovery procedures in the Mondou system. We improve the typical data mining
algorithms of association rules 1) , and apply them to the set of web pages.
Shortly speaking, given a set of web documents, where each document
consists of various keywords, an association rule is an expression X ⇒ Y , where
X and Y are sets of keywords. The intuitive meaning of such a rule is that
documents which contain the keywords X tend to also contain the keywords Y .
The support threshold value of the rule X ⇒ Y is the percentage of documentsthat contain both X and Y .
Table 1 shows typical examples of derived keywords, the number of URLs
and associative keywords by our algorithm ∗2 .
Table 1

Examples of associative keywords.

keywords

No. of URLs

Applications

975

windows, speech, computer, internet, helper, · · ·Informatics

462

genome, medical, department, center, school, · · ·Japan

20,366

japan country, webec, japan, japanese, backEngine2,836

lycos, yahoo, dragon, search, creative, japan

Mining134

akita, geology, college, net, data

Mondou28

rcaau, search∗2

related keywords

A derived keyword, “rcaau”, stands for ”Retrieval loCation by weighted AssociAtionrUle,” which appears in the web page of Mondou.

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Hiroyuki KAWANO

In order to reﬁne these rules much more by Mondou, it is eﬀective toclassify several attributes and use the relationships of diﬀerent attributes by using semi-structured data. Moreover, our Mondou system derives the diﬀerent
set of association rules from the diﬀerent databases, such as netnews archives,electronic journals and other digital libralies. Finally our system deduces much
more eﬀective combination of keywords from heterogeneous databases by using
the diﬀerence of derived rules. We also implement the agent oriented communication interface, diﬀerent Mondou systems can communicate each other by highlevel query languages like KQML.

4.4

Information visualization
In order to visualize the characteristics of association rules and recognize

the features of document clusters, we constructed interactive visual interfaces13)
as Java applets.
In Fig.4, this 3D graph shows the distributions of derived keywords basedon ROC graphs11, 20). We also implemented ﬁsheye graph drawing techniques2) .
By the simple mouse operations, users can determine a combination of keywords
easily.
For example, in Fig.4, by a query with a keyword “mediator”, Mondou derives associative keywords, such as “information”, “data”, “distributed”,“supporting”, “connections”, “personal” and others. Fig.5 shows an example ofvisualizing results for an selected query using some of above derived keywords.
In the ﬁgure, numerical numbers mean the length of queries, and the color ofa keyword changes by pointing the number. In the right window, this applet
displays the search results which satisfy the combination of black keywords.
Consequently, users can easily select the interesting combination of keywords by data mining and information visualization, which are provided by our
Mondou system.

§5

Conclusion

We surveyed the conventional systems of information gathering andsearching in the Web. Information gathering systems, Softbot, Sage, ARACHNID and so on were introduced. Though some of them have successfully overcome the problems which traditional IR systems hardly cope with, there arestill open problems. We also pointed out two signiﬁcant problems, the gathering
pieces of knowledge and the appropriate combination of keywords in the ﬁelds.

Information Gathering and

Fig. 4

Searching Approaches in the Web

13

Visual interface for keyword selection

As an attempt to introduce a promising approach to gather systematic
information, not pieces of knowledge, we described navigation planning thatgenerates a plan guiding user to understand a concept in the Web. It has theability to generate operators during planning from Web pages using keyword
extraction methods. The search for useful Web pages for a user to understandgoal concepts was formalized using an AI planning framework, and an operator
corresponding to the understanding of a Web page was deﬁned with conditionand eﬀect knowledge. Then we described the whole planning procedure, andveriﬁed the eﬀectiveness experimentally.
In a ﬁeld of a Web search engine, many researchers are trying to automatically generate the appropriate combination of keywords in a query. A
Mondou web search engine was introduced as one of web search engines in the14

Hiroyuki KAWANO

Fig. 5Visual interface of document clusteringﬁrst generation, which was based on the emerging technologies of data mining,text mining, web mining and information visualization. In particular, we focused on the query modiﬁcation, which is a typical and important problem in
the research of information retrieval. Our implemented functions, the derivationand visualization of associative keywords in order to modify the initial query,are empirically found very eﬀective for novice users.

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