CoMeMo: Constructing and Sharing Everyday Memory
H.Maeda, T.Hirata and T.Nishida
Graduate School of Information Science
Nara Institute of Science and Technology
8916-5, Takayama, Ikoma, Nara, 630-01, JAPAN

Abstract

This paper describes the CoMeMo project whichaims to develop systems which articulate, organize
and utilize everyday memory in a group or in acommunity. The basic hypothesis is that connecting information without de ning the semantics using
the weak information structures is e ective to deal
with a large amount of diverse information. The
CoMeMo architecture consists of (a) CoMeMo Workbench, (b) CoMeMo Incorporator, (c) CoMeMo Mobile Terminal, (d) CoMeMo Server, (e) CoMeMo Protocol and (f) CoMeMo Agent. We have implemented
the CoMeMo Workbench, the CoMeMo Incorporator
and a prototype of the CoMeMo Mobile Terminal. We
have developed applications based on the CoMeMo architecture: (a) Information Gathering and Reorganization from WWW pages, (b) Ontology development,(c) Education Support and (d) Conference Support.

1 Introduction

Most of events in everyday life are forgotten. It isdicult to utilize ideas and thoughts which we hit onwhen watching TV or strolling at street corners, unless we make note of them. We call such ideas andthoughts in everyday life \everyday memory." We
believe that it is signi cant to construct and utilize
everyday memory. Everyday memory plays a criticalrole to facilitate human-human interaction. Sharing
everyday memory and creating common backgroundknowledge facilitates ecient message exchanges.
The CoMeMo1 project aims to develop systems
which articulate, organize and utilize everyday memory in a group or in a community. We propose the weak
information structures to gather incomplete everyday
events and gradually reorganize them. The CoMeMo
project is a part of the Knowledgeable Community
project[8] which aims to develop an environment ofcreating knowledge by humans and computers. The
CoMeMo project is to assist human memory, in thelight of human information processing.
There are some research topics in constructing andsharing everyday memory.
1. Memory Representation: how to represent
memory1 \CoMeMo" represents \Common Memory", \Continuous Memory", \Computer-enhanced Memorandum", formerly
called CM-2 [1, 2, 3, 4, 5, 6, 7].

2. Recall: how to recall from clues
3. Utilizing Memory: how to utilize memory
stored in di erent situations or from di erent
viewpoints.
4. Cooperative Construction of Memory: how
to support the process of cooperation to constructmemory5. Presentation of Memory: how to present
memory to be comprehensible to humans
In computer knowledge sharing[8, 9, 10] which aims
to implement interoperability of virtual knowledge
base, the role of (1) formal common languages whichde ne the semantics of information, (2) facilitators
which mediate between agents has been emphasized.
The protocol design between agents is important in
multi-agent systems.
On the other hand, in order to support human interactions, we aim to make use of tacit backgroundknowledge or contexts shared with humans. We believe it important to investigate tacit knowledge andcontexts in real world problems, in the light of information science to design systems which construct andshare everyday memory.
In the CoMeMo project, we attempt to explain themechanisms of (a) interaction between tacit and explicit knowledge and (b) cognition and generation ofcontexts and situations by studying human interaction
systems.

2

CoMeMo Architecture

We set a hypothesis that connecting information
without de ning the semantics using the weak information structures is e ective to deal with a large
amount of diverse information. Associations are oneof such information representations we are investigating (Figure 1); each of which is a many-to-many mapping of memory units. Associations connect a collection of key units (hereafter keys) with a collection ofunits (hereafter values) which are normally remindedby the given keys. Units are basic entities of associations which represent concepts, texts, or image les.
We call a set of associations collected from a particular point of view workspaces. The CoMeMo information base is a collection of workspaces (Figure
2).

Nara

Todai-ji Temple

Nara

Todai-ji
TempleDaibutsu Nandai-mon Gate

templeAkishino-dera
TemplepictureCoMeMo Incorporator

CoMeMo Information
Base

! ?
ideas

Kofuku-ji
TempleCoMeMoMobile Terminal

WWW Pages

gatheringtreasure
Kongo Statue biggest gate in Japan

KaichoUnkei

Kamakura Period

CoMeMo Protocol

Shoro

Nandai-mon Five-Storied Three-Storied
Gate
PagodaPagodaCoMeMo Workbench
Knowledge Cycle

Figure 1: Example Associations
view of workspaceediting

templeTodai-ji Temple
Kofuku-ji Temple
Akishino-dera Temple
Todai-ji Temple

Nara

park

editing
Nara

Shoso-in Nandai-mon
Repository Gate

reorganizing

CoMeMo AgentNara Park
another workspace

Figure 3: CoMeMo Architecture

workspace: a set of associations from a view
templeNara

Mr.Yamada's description
Kofuku-ji Temple
Akishino-dera Temple
Todai-ji Temple
university
NAIST
Nara Women's University

park

Nara Park

Nara University

Figure 2: Workspaces
In our approach, the semantics of the associations
is not de ned rigorously. Instead, we leave the interpretation of the semantics to tacit human backgroundknowledge. This facilitates information acquisition
from a variety of data (e.g. images, texts, ideas, memoranda). The CoMeMo architecture helps sharing andreuse of fragmented information.
The CoMeMo architecture consists of following
components (Figure 3).
1. CoMeMo Workbench helps users explore,
browse and edit associations in the CoMeMo information bases.
2. CoMeMo Incorporator gathers and converts awide variety of information into information representations which CoMeMo can deal with.
3. CoMeMo Mobile Terminal enables users
ubiquitous use of CoMeMo information.
4. CoMeMo Server articulates and serves associations on demand, and mediates between information users and developers.
5. CoMeMo Protocol exchanges information between each component.
6. CoMeMo Agent (a) executes user tasks, (b)negotiates common problems with other agentsor humans, and (c) presents the contents of
CoMeMo information bases to users.

We have implemented the CoMeMo Workbench
and the CoMeMo Incorporator. We have implemented a prototype of the CoMeMo Mobile Terminal. The CoMeMo Server, the CoMeMo Protocol andthe CoMeMo Agent are under construction. We will
describe the CoMeMo Workbench and the CoMeMo
Incorporator in next two sections.

3

CoMeMo Workbench

The CoMeMo Workbench helps users explore,
browse and edit associations in the CoMeMo information bases.

3.1 Information Exploration

In addition to basic input, edit functions, following
functions help users to edit and reorganize information
bases.
1. Focus: to hide units which are unrelated to selected units
2. Neighbor Search: to display units which arelinked to selected units
3. Path Finding: to display relations between selected units
4. Unit Search: to display units by keyword search
In what follows, we concentrate on the path nding.
Path nding is based on the idea of \spreading activation" on semantic networks [11]. Path nding with
distance n searches a set of units which are linked byassociations with distance n. Distance 1 denotes anextent between keys and values. Figure 4 illustrates
how the algorithm works to answer a question;\Is there any temple related to Prince
Shotoku?"question: "Is there any temple related to Prince Shotoku?"
templePrince Shotoku

distance 1 from Shotoku

Todai-ji Temple
Kofuku-ji
TempleHoryu-ji
TemplePrince Shotoku

Prince Yamahsiro-oe

templetempleTodai-ji
Templepark

Nara Shoso-in Nandai-mon
RepositoryGate

NaratempleHasde-dera
TempleKofuku-ji Temple
Shoso-in
Repository

Nara
Todai-ji Temple

park

Figure 4: Path Finding
Table 1: Experimental Results of Path Finding

Table 2: Experimental Results of the CoMeMo Incorporator
Information
Sources

Distance
Precision
RecallDistance 1
64%
64%
Distance 1 + 2 76%
92%
are all appropriate 2 100 (%)
Precision: answers whose results
qyestionsappropriate results 2 100 (%)
Recall: answers which containquestions

As preliminary experiments (10 questions) had
shown that (a) when appropriate answers are obtainedby nearest path, far more search is in vain and (b)many unnecessary answers are obtained in distance 3
search, we decided to evaluate distance 1 search anddistance 1 + 2 search. Distance 1 +2 search meansthat if no answers are obtained in distance 1 search,
distance 2 search is performed.
We manually constructed an information base referring a sightseeing guidebook of Nara. It contains
1,286 units and 862 associations. We tested path nding against this information base. We obtained appropriate 38 answers out of 50 queries (76% at precisionrate, distance 1 + 2; See Table.1). In distance 1 + 2
search, units which cannot be found in distance 1 arefound. This is why it improves both of precision andrecall rate.
Information uni cation uni es various associations
such as generated from WWW pages and those created by humans into new associations and remove unnecessary associations using heuristics.
Figure 5 shows an example of information uni cation: information in the right 2 workspaces are uni ed
onto the left workspace.
This facility helps users to utilize information bases
possessed by himself/herself or other members in
his/her small group. When it is used in bigger societies, ontology and background knowledge bases may
be necessary to supply the de cit of the contexts.
Another example is shown in Figure 6. Members of
our lab have constructed information bases of slides,
survey and so on. User A wrote a plot to write a paper

Nandai-mon
Gate

Nara Park

Figure 5: An Example of Information Uni cation

shrine3.2 Information Uni cation

shrineNara Park

Chogoson-ji
Templefoundation

Todai-ji Temple

Kofuku-ji
TempleNaradistance 1 from temple

EmperorSuikosontempleNara Sightseeing
Guidebook
Nikkei Newspaper
Full-text Database

Test

Precision

RecallT est1T est2T est1T est2T est1T est2

92%
86%

0
0

63%
83%

91%
71%

90%
83%
HTML
Docu68%73%
ments on WWW
units
Precision: appropriate
generated units 2 100 (%)
appropriate units
Recall: units whichshould be extracted 2 100 (%)(Figure 6(a)) and then uni ed information bases in thelab. As a result, a piece of slides or papers containedin User B's survey information base and User A's slide
information base are integrated through the workspace(Figure 6(b)).

4

CoMeMo Incorporator

5

Applications

The CoMeMo Incorporator gathers and converts awide variety of information into information representations which CoMeMo can deal with. For example,
information is gathered from HTML documents on
WWW and newspaper databases[2, 3, 4, 5]. The current algorithm is quite simple; extracting keywords
by morphological analysis[12] and generating associations by analyzing HTML structures (Figure 7). Figure 8 shows an example of associations generated fromWWW Pages concerning Kyoto sightseeing information.
To evaluate the e ectiveness of the methods, wetested several cases to generate associations from fulltext newspaper database, HTML documents and soon. The number of units was around 1000 { 2000.
Table 2 shows the results. See [2, 3, 4, 5] for details.
We have developed some applications based on the
CoMeMo Architecture. The rst three systems con-

(a)Workspace Before Information Uni cation(b)Workspace After Information Uni cation(http://www.notredame.ac.jp/Other/Kyoto/matsuri/kamo-odori-j.html)
Figure 8: An Example of Associations Generated fromWWW Pages
cerns the CoMeMo Workbench and Incorporator, andthe last one is a prototype of the CoMeMo Mobile
Terminal.

5.1 Information Gathering and Reorganization from WWW Pages

Figure 6: Unifying CoMeMo Information Bases<H1>Sightseeing Information in Nara</H1><H2>Todai-ji Temple</H2><UL><LI>buildings<UL><LI>Daibutsu-den Hall
<LI>Shoso-in Repository<LI>Nigatsu-do Hall</UL>
<LI>nearest station<UL><LI>Kintetsu Nara Sta.
</UL>
<UL>

Sightseeing Information Nara
Todai-ji Temple
buildings

Daibutsu- Shoso-in Nigatsu-do
den Hall Repository Hall
Sightseeing Information Nara
Todai-ji Temple
station

Kintetsu Nara Sta.

Figure 7: Generating Associations from Mark-up Documents

Recently the World Wide Web has become very
popular for human information sharing. Although anumber of search tools available, it is dicult to extract useful information and reorganize it according touser's point of view.
We have developed an application to gather WWW
pages, extract information and reorganize it according
to user's input.
We gave 100 WWW pages concerning AI researchers to CoMeMo Incorporator for organizing AI
directories. The CoMeMo Incorporator extracted
units about 7 classes (researchers, projects, e-mail,
topics, universities, departments and laboratories),and generated associations. It then reorganized theseunits and associations to display various lists according to user's input.
The overview of the process is illustrated in Figure 9. The algorithm is as follows; (1) to generateunits and associations by morphological analysis andanalyzing HTML structure, (2) to identify class of thegenerated units using heuristics, (3) to unify units andassociations using heuristics, and (4) to reorganize associations by path- nding according to user's input.
Figure 9(a) shows an example result when a user input \reasoning",\researcher",\e-mail",\project" and\university". For example, researchers such as Adam
Farquhar, Alon Levy, Edward Feigenbaum and James
Allen are extracted rst, because the word \reasoning"
and their names are written near in WWW pages, andthen their related information is reorganized. The experimental results are shown in Table 2. See [3, 4] fordetails.

WWW Pages(a)
Sightseeing Information in Nara

James F.Allen's Home Page

Barbara Hayes-Roth's Home Page

Todai-ji: Emperor Shomu built Great Budda Hall in 745..
Kofuku-ji : This temple is famous for the Five-Storied・ ...
Pagoda and East-Golden-Hall.
・・

Plain Text, Hypertext
(b)
Nara

Todai-ji(c)
Kofuku-ji

NaratempleKofuku-ji

Todai-jibuilding
Shomu
Daibutsu 745
Five-Storied Pagoda
Great Budda HallGreat Budda Hall East-Golden-Hall

FiveStoriedPagoda

Associations

URCS Faculty Member

James F.Allen

Ph.D.

Daibutsu(e)

Associations

TRAINS

Barbara Hayes-Rothhayes-roth@cs.stanford.edu

University ofJames Allen
Toronto University ofRochester
research interests

KSL
Affiliation/
Position

Knowledge Representaiotn andReasoning

Reasoning About Plans

CM-2 output

E-mail Address

Adaptive
Intelligent
Systems
Project Leader

Senior Research
Scientist(a) Test 1 (researcher)

Todai-ji:is-a temple:has-part
Great Budda
Hall
Kofuku-ji:is-a temple:has-partFive-StoriedPagoda
temple:has-part? building:in ? placestatueAssociations

Daibutsu:is-a statue(d)Five-StoriedPagoda:is-a building

templeis-a

Great
Budda Hall:has-part
Daibutsu

Nara

is-a
in

in
Todai-ji

Kofuku-ji
hasparthaspartbuilding
is-aGreat Budda Hall

Shomu
:is-a person

in

Frame

is-a

Daibutsu is-a

FiveStoriedPagoda
statueSemantic Network(f)(defconcept temple is-primitive (defrelation own(:and object:range object(:some own building):domain temple)(:exactly 1 place)))(defrelation place(defconcept statue is-primitive:range city(:and object)):domain temple)(defconcept builidng is-primitive(:and object))(create Nara city)(create Todai-ji temple)(create Kofuku-ji temple)(create Five-Storied-Pagoda
building)(create Daibutsu statue)(set-value Todai-ji own '(Daibutsu))(set-value Todai-ji place '(Nara))(set-value Kofuku-ji own'(Five-Storied-Pagoda))(set-value Kofuku-ji place '(Nara))

Predicate Logic

Figure 10: Ontology Development

5.2 Ontology Development(b) Test 2 (project)

Ontology plays a central role in achieving large scale
knowledge sharing. Unfortunately, development of ontology is often a quite painstaking and time consuming
task, because it needs much e ort to collect and select terms through task analysis. We have applied ourapproach based on the weak information structures toontology development. It allows for data-driven ontology development, by accumulating raw data and incrementally creating the structure of concepts throughhuman-computer collaboration (raw dataassociations re ned associations semantic networks
frames predicate logic) . The overall process of ourapproach is shown in Figure 10.
We gave 30 WWW pages concerning ARPA Intelligent Integration of Information (I3) Initiative 2 to
CoMeMo Incorporator. Each page contains overview
of projects which belong to the I3 Initiative. The
CoMeMo Incorporator generated associations. Generated associations themselves are incoherent and can-!
!

Figure 9: Information Gathering and Reorganizationfrom WWW Pages

2 http://dc.isx.com/I3/

!

!

!

Figure 12: InfoCommon in Use

Figure 11: An Example Result of Re ned Associations

InfoCommon Information Basenot be used as ontology as they are. Users and computers collaborated to form these associations into
more coherent structure on the CoMeMo Workbench.
Figure 11 shows an example result of re ned associations concerning the project \Agent-Based Software
Interoperation". It corresponds to level c ontology(re ned associations) in Figure 10. See [6] for details.

PDA

InfoCommon
Server

5.4 Conference Support

We have implemented a shared-card information
system to support community information sharing
called InfoCommon, which is a prototype of the
CoMeMo Mobile Terminal.
We evaluated the usefulness of InfoCommon at the
ICMAS'96 (Second International Conference on Multiagent Systems) Mobile Assistant Project[13], whichis the world rst experiment in applying mobile computing systems to community support. 100 personal
intelligent communicators with handy phones wereloaned to conference participants to actually try out
the system. Figure 12 is a photo taken in the Nara
Park where an excursion was held, which shows how
a user actually used InfoCommon.

Retrieval
Information Card Base

Information
Sending
User

5.3 Education Support

A high school teacher Mr.Moriyama has developeda system called CALON based on the CoMeMo Workbench which helps high school students learn Japanese
history. He used the system at the classroom for one
year and evaluated it.
CALON allows teachers to express their knowledge
and students to access the contents of textbooks.
He evaluated how students understand the semantics of associations written by himself. He created associations from historical facts. 41 students tried toconvert these associations into natural language texts.
He found that 90% students could do the above tasks.
Next, he examined how students create associations
from natural language texts in textbooks. From thisexperiment, he found how students understand thehistorical facts. See [7] for details.

Knowledge Base

Information
RetrievalSending

RetrievalMonitoring
News
Sending

Session

Abstracts

ProfilesLocal

InfoCommon
Information
Volunteer

Figure 13: Overview of InfoCommon
InfoCommon provides visual interface for retrieving
and sending information cards. Figure 14 shows ascreen image of InfoCommon. The relation between
two information cards is displayed by a link. InfoCommon information base consists of (a) a knowledge
base which links keywords and information cards using
the weak information structure and (b) an information
card base as shown in Figure 13.
InfoCommon information bases store static information such as abstracts of papers, session, local information and pro les to share information among participants.
InfoCommon supports the following functions.

Given aset of keywords, InfoCommon will respond with theset of information cards connected to the keywords.
The result of retrieval is stored in the user's local information base where the user can re-arrange the collection of information cards, and add/remove nodes/
links as desired.
Content-based Information Retrieval

Information Sending (Posting News)

InfoCom-is built on a conventional News service. Users
can add information cards on their local informationbase and send them to a News server.
monquisition and integration becomes.
Our approach is to provide a framework of collaborations between humans and computers to construct
and share everyday memory with a low structural facilities and to facilitate raw information from vast information sources to be incorporated without muchlabor and gradually re ned and elaborated as moreinsights are obtained.

Figure 14: Screen Image of InfoCommon
The following results indicate the system's usefulness for information sharing in community: (a) 51% ofusers were satis ed of the system, (b) 81% felt search
results were ne and (c) 55% answered that the system was useful for getting information they need. We
consider the results are supporting evidence of the effectiveness of the weak information structure for information sharing in community. See [14] for details.

6 Related Work

Our work is related to recent work on information gathering from heterogeneous sources on Internet ([16],[17],[18],[19],[20]). Instead of focusing on thestrategies and heuristics for information gathering, weconcentrate on how to classify information obtained
from multiple information sources and integrate it into
personal information spaces.
CYC[21] and ARPA Knowledge Sharing E ort[10]have made a signi cant contribution in the sense they
shed light on the importance of knowledge and information sharing and that they have presented a selfcompleted computational model. Their approach orients computer information sharing while ours is forhuman information sharing.
Gaines uses semantic networks as information representation for group knowledge sharing[22]. Our approach is based on much weaker information representation than semantic networks.
Kautz studied the use of agents in assisting andsimplifying person-to-person communication for information gathering tasks[23]. They focus on the use of asoftware agent. We concentrate on the process of how
humans create knowledge and information.
Sumi[24], Hori[25] and Kunifuji claim the importance of knowledge and information in the eld of creative thinking support.
The basic recognition behind this research is atrade-o between the bene t from conceptually wellstructured information representation and the costfor organizing information space. The more wellstructured information representation becomes, themore useful it is for computational manipulation, however, the more expensive the cost of information ac-

7

Future Work

8

Concluding Remarks

As information systems spread into human societies, supporting human interaction in community becomes more important. We think that one directionof future work is to support communication directly,
such as matchmaking proposed by Ishida[26]. Other
direction may be indirect support based on information sharing to nd people who has information one
needs.
The CoMeMo architecture supports to send and receive information produced in everyday life. We attempt to study knowledge creation on social networks
which is proposed by Imai, Nonaka[27], and Hori[25]and so on.
We described the CoMeMo project for constructing and sharing everyday memory in a group or in acommunity. The basic hypothesis is that connecting
information without de ning the semantics using the
weak information structures is e ective to deal with alarge amount of diverse information. We implemented
the CoMeMo Workbench, the CoMeMo Incorporator
and a prototype of the CoMeMo Mobile Terminal. We
evaluated applications based on the CoMeMo architecture.
In addition to experiments stated in this paper, weare currently investigating cognitive modeling on how
humans create the weak information structures andhow humans understand the semantics of the weak
information structures using their tacit knowledge.

References[1] T. Nishida, H. Takeda, K. Iino and M. Nishiki,\A knowledge media approach to ontology development," Towards Very Large Knowledge Bases:
Knowledge Building & Knowledge Sharing 1995,(Editor N.J.I. Mars), IOS Press, pp. 84-94, 1995.
[2] H. Maeda, K. Koujitani and T. Nishida, \A Proposal of Associative Structures for User-Friendly
Interface in Information Bases," Progress in Human Interface, Vol.5, No.1, pp.49{56, 1996, (inJapanese).
[3] H. Maeda, K. Koujitani and T. Nishida,\A Knowledge Media Approach Using Associative Representation for Constructing Information
Bases," Proceedings of The Ninth International

Conference on Industrial and Arti cial Intelligence and Expert Systems (IEA/AIE-96), pp.117{
126, 1996.

[4] H. Maeda, K. Koujitani and T. Nishida, \Information Reorganization using Associative Structures," Transactions of Information Processing Society of Japan, Vol.38, No.3, pp.616{625, 1997, (inJapanese).
[5] K. Koujitani, H. Maeda and T. Nishida, \Constructing Information Bases with a WeeklyStructured Medium," Progress in Human Interface, Vol.5, No.2, pp.69{76, 1996, (in Japanese).
[6] K. Koujitani. \Constructing Ontologies Based on
Real Data," Master's Thesis, Graduate School ofInformation Science, Nara Institute of Science andTechnology, 1996, (in Japanese)[7] A. Moriyama,\Construction of Computer Aided
Learning System Using Weakly-Structured Knowledge Representation and its Application in Education of History of Japan in High School", Master's
Thesis, Graduate School of Information Science,
Nara Institute of Science and Technology, 1997,(in Japanese).
[8] T. Nishida, \Very Large Scale Knowledge Base
Systems," Journal of Information Processing Society of Japan, Vol. 35, No. 2, pp. 130-139, 1994,(in Japanese).
[9] R. Neches, et al., \Enabling Technology for Knowledge Sharing," AI Magazine, Vol. 12, No. 3,
pp. 36{56, 1991.
[10] R. S. Patil et al. \The DARPA Knowledge Sharing E ort: Progress report, Principles of Knowledge Representation and Reasoning," Proceedingsof the Third International Conference, (eds. C.
Rich, B. Nebel and W. Swartout), Morgan Kaufmann, 1992.
[11] M. R. Quillian, \Semantic memory," In Marvin
Minsky edition, Semantic Information Processing,
MIT Press, 1968.
[12] E. Brill, \Some Advance in TransformationBased Part of Speech Tagging," In Proceedings of

the Twelfth National Conference on Arti cial Intelligence (AAAI-94), 1994.
[13] Y. Nishibe, H. Waki, I. Morihara and F. Hattori,\Analyzing Social Interactions in Massive Mobile
Computing - Experiments of ICMAS'96 Mobile
Assistant Project-," IJCAI-97 International Workshop on Social Interaction and CommunityWare,pp.19-24, 1997.
[14] H. Maeda, M. Kajihara, H. Adachi, A. Sawada,
H. Takeda and T. Nishida, \Weak Information Structure for Human Information Sharing,"
IJCAI-97 International Workshop on Social Interaction and CommunityWare, pp.7-12, 1997.
[15] T. Nishida and H. Takeda, \Towards the knowledgeable community," Knowledge Building andKnowledge Sharing, IOS Press, pp. 155{164, 1994.

[16] A. Y. Levy, Y. S. Sagiv and D. Srivasava,\Towards ecient information gathering agents,"

Working Notes of the AAAI Spring Symposium on
Software Agents, pp. 64{70, 1994.
[17] R. Armstrong, D. Freitag, T. Joachims and T.
Mitchell. \A learning apprentice for the World
Wide Web," Working Notes of the AAAI Spring
Symposium on Information Gathering from Heterogeneous, Distributed Environments, pp. 6{12,1995.
[18] M. Balabanovi'c and Y. Shoham. \Learning information retrieval agents: Experiments with automated web browsing," Working Notes of the AAAI
Spring Symposium on Information Gathering from
Heterogeneous, Distributed Environments, pp. 13{
18, 1995.
[19] W-S Li. \Knowledge gathering and matching inheterogeneous databases," Working Notes of the
AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments, pp. 116{121, 1995.
[20] M. Iwazume, H. Takeda and T. Nishida,\Ontology-Based Information Gathering and Text
Categorization from the Internet," Proceedings of
the Ninth International Conference in Industrial
and Engineering Applications of Arti cial Intelligence and Expert Systems pp. 305{314, 1996.
[21] R. V. Guha and D. B. Lenat, \Enabling Agents
to Work Together," Communications of ACM,
Vol. 37, No. 7, pp. 127{142, 1994.
[22] B. R. Gaines and M. L. G. Shaw, \Using Knowledge Acquisition and Representation Tools to Support Scienti c Communities," AAAI-94, 1994.
[23] H. Kautz and B. Selman and A. Milewski, \Agent
Ampli ed Communication," Proceedings of theThirteen National Conference on Arti cial Intelligence (AAAI-96), pp. 3{9, 1996.
[24] Y. Sumi and K. Hori and S. Ohsuga, \ComputerAided Thinking Based on Mapping Text-Objects
into Metric Spaces," Proc. of The 2nd Paci c Rim
International Conference on Arti cial Intelligence,
1992.
[25] K. Hori, \A System for Aiding Creative Concept
Formation," IEEE Transactions on Systems, Man,
and Cybernetics, Vol. 24, No. 6, pp. 882{894, 1994.
[26] T. Ishida and T. Nishimura, \Global Information Network for Community Support," Journal of
Information Processing Society of Japan, Vol. 38,
No. 1, pp. 48{53, 1997 (in Japanese).
[27] I. Nonaka and H. Takeuchi, \The KnowledgeCreating Company: How Japanese Companies
Create the Dynamics of Innovation," Oxford University Press, 1995.