GENERATING AND UNDERSTANDING OF
WEAK INFORMATION STRUCTURES BY HUMANS
HARUMI MAEDA AND TOYOAKI NISHIDA
Graduate School of Information Science,
Nara Institute of Science and Technology
8916-5, Takayama, Ikoma, Nara 630-0101 JAPAN
Phone: +81-743-72-5265 Fax: +81-743-72-5269
E-mail: harumi-m@is.aist-nara.ac.jp
Abstract: We have proposed an information representation called weak information structures whichconnects a wide variety of information media such as
natural language texts, hypertexts and images withoutde ning the semantics rigorously to integrate heterogeneous information. We have set a hypothesis that\the weak information structures are easy to generatefrom raw data, and therefore useful to extract and integrate information from a wide variety of information
sources." In this paper, we investigate how people generate and understand the weak information structures
based on three experiments.
Keywords: weak information structures, associations, generating, understanding

1 Introduction
We have proposed an information representation called
weak information structures which connects a wide variety of information media such as natural language
texts, hypertexts and images without de ning the semantics rigorously to integrate heterogeneous information. We have set a hypothesis that \the weak information structures are easy to generate from raw data,
and therefore useful to extract and integrate information from a wide variety of information sources."
To investigate how e ective the weak information
structures are in real world problems, we have developed a system called CoMeMo and tested several cases
so far[1, 2, 3].
In this paper, we investigate how people generate
the weak information structures, and how people understand the semantics of them based on three experiments.
In what follows, we rst give an overview of an information representation called Associations which is akind of weak information structures. We then describe
three experiments to investigate the hypothesis.

2Associations

Associations is a kind of weak information structures
which connects information units with direction andmany-to-many mapping.
The basic entities of the associations are a unit
which represents either a concept or an external daTodai-ji Temple

Nara City

deer Great Budda(a) Free Reminding
Todai-ji Temple

Nara Citytemple(c) IS-A

Nara Prefecture(d) Part-oftemple

Todai-ji Horyu-ji Kofuku-jiTemple Temple Temple(b) Intersection, Attrivute-Value
provideJackgive

Betty book(f) Sentence(e) Synonymgive(a) Free Reminding: Deer, Great Budda, and aphoto of Todai-ji Temple are reminded by Todai-jiTemple.
(b) Intersection, Attribute-value: There areTodai-ji Temple, Horyu-ji Temple, and Kofuku-ji
Temple as temples in Nara.
(c) IS-A: Todai-ji Temple is a temple.
(d) Part-of: Nara City is a part of Nara
Prefecture.
(e) Synonym: \Provide" and \give" aresynonymous.
(f ) Sentence: Jack gives Betty a book.
Figure 1: An Example of Various Associations

tum, and an association which connects a collection ofkey concepts with a collection of units which are normally reminded by the given keys. A dot represents
an association and arrows associated the dot represent
direction of the associations.
As Woods pointed out[4], there are various meanings in links in semantic networks. Figure 1 shows anexample of associations whose relations represent various semantics.
In the text of this paper, we describe \Nara City &temple {> Todai-ji Temple, Horyu-ji Temple, Kofukuji Temple" for representing Figure 1(a) association.

3 Experiment 1
The purpose of Experiment 1 is to investigate how people generate associations from various information suchas ideas, research memoranda or articles, and how people people understand the semantics of associations.
3.1

MethodApparatus

CoMeMo Workbench.

The subjects were postgraduate students
in our laboratory as follows. Three Ph.D. students(hereafter D), ve 2nd-year M.Eng. students (hereafterM2), and four 1st-year M.Eng. students (hereafter M1)
1.

Subjects

Procedure(1) 5 test screens were prepared using the CoMeMoWorkbench.
(2) The subjects were shown these 5 test screens(from Test 1 to 5) and answered the following
questions for each test: \Do you understand theoutline of the screen ?", \Say the meaning of eachcircles 2 ?"Test Screens Test 1: shop information around the Institutepossessed by an author. (See Figure 2 for thegenerated screen, and Table 2 for the generatingprocess.)

 Test 2: description of the Peru Case generated

from an newspaper article.

1 1st-year M.Eng. students were all newcomers at the experiment because the Institute has postgraduate students only.
2 We didn't explain associations and therefore used terms \circle."coffeebus route

Narayama Street

closemain street
Lawson's at Shikahata

Omar

Wednesday

NaRa Family

convenience store

department store

near

Saidai-ji

medicine

NAIST

liquorsupermarket

COSTparking

I guess the most of readers are not clear what are written. I claimthat this is because we don't share the background knowledge
about our Institute and Japanese language.

Figure 2: Test Screen 1 (Shop Information around theInstitute)
Table 1: Restoration RateSubjects
DM2
M1
MeanTest
1
100%
100%
60%
87%

Test
266%
56%
70%
64%

Test
3
100%
80%
40%
73%

Test
4
100%
100%
93%
98%

Test
597%
56%
44%
66%

Mean
93%
78%
61%
78%

 Test 3: information of CFP(call for papers) an-nounced in our laboratory. (See Figure 4).

 Test 4: research memoranda about agents pos-sessed by an author.

 Test 5: research memoranda about knowledge

media possessed by an author.

In Test Screen 1,2,3 and 4, concepts were originallywritten in Japanese. They were written in English in
Test screen 5.
3.2

Results and Discussion

Table 1 shows the restoration rate in Experiment 1.
We evaluate that associations are comprehensible
to adults who have computing skills as a result that
(1) all subjects answered the outline in Test 1 without
being explained the meaning of associations and (2)the restoration rate was 78% on average.
We analyze the result that the restoration rate wasbetter in D(93%) > M2(78%) > M1(61%) suggests themore human background knowledge people have, themore people understand associations.
The subjects restored the meaning of associations
by referring units they can understand when they didn't
understand the unit's labels. This tendency was apparent in Test 3 in which the structure of associations were

no.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.

Table 2: Generating Process of Test Screen 1
Ideas
Generated Associations
COST(a supermarket) is reminded by NAIST(the \NAIST & near & supermarket {>Institute).
COST"
Medicine and liquor is sold at COST, and COST has \COST {> medicine, liquor, parking"parking.
By supermarkets, convenience stores are reminded. \supermarket {> convenience store"
The convenience store nearest NAIST is Lawson's (a \NAIST & near & convenience store {>famous convenience store in Japan) at Shikahata (a Lawson's at Shikahata"place)"
Supermarkets remind me of department stores.
\supermarket {> department store"
Department stores remind me of NaRa Family (a de- \Saidai-ji & department store {> NaRa
partment store) in Saidai-ji (a place)
Family"I buy co ee beans at Omar (a shop).
\co ee {> Omar"
Omar closes every Wednesday.
\Omar & close {> Wednesday"
Omar besides at the Narayama Street (a street)\Omar {> Narayama Street"
The Narayama Street is a bus route and the main Narayama Street {> bus route, mainstreet around here.
street"coherent (e.g. conference names, their venues, periods
and so on). We analyze that the context helps humanunderstanding of associations.

homepageHTMLmobilelanguage

4 Experiment 2AI
PROLOG

JAVA
HORB

PDA

The purpose of Experiment 2 is to investigate how people generate associations from ideas and how people
understand those generated by others.

LISP

Agletsagent

developspy
proxy

4.1

WWW

Telescript
mobile agent

ETLIBM

MethodApparatus

Figure 3: A Screen Generated by Subject A

The same as in Experiment 1.

One Ph.D. students, four 2nd-year M.Eng.
students, and one sta in our laboratory.
Subjects

Procedure(1) The subjects were trained how to generate associations (students only).
(2) They generated associations reminded by a keyword \agent" on the CoMeMo Workbench (students only).
(3) They were shown associations generated by othersubjects and answered the following questions:\Do you understand what are written ?", \Do
you identify who wrote this screen ?", \If youidentify who, why ?" and \Say anything you felt
in this experiment" (students only).
(4) The same as (3) (the sta subject only).

4.2

Results and Discussion

An example screen generated by the Subject A is shownin Figure 3 3
All subjects generated associations within 30 minutes. We analyze that adults who have computing
skills can generate associations without diculty.
All subjects understood the meaning of associations
generated by others. Concerning a screen generated by
the subject C, all other subjects identi ed that it wasmade by him. We analyze that ideas can be transmitted using associations among people who share knowledge. All subjects except for the subject C laughedwhen they saw screens wrote by the subject C. 80%(4out of 5 student subjects) said that they had somefun during the experiment. The sta subject said that
\I can assume the subject's knowledge level concerning research topics", \I may want to ask a report for3 Concepts were originally written in Japanese.

Prof.Nishidaformation in the test screen rst but I foundthat I misunderstood the article after reading it. (Subject 2)"

15 July 1997

IEEE INFOCOM'98

recommend{ \I thought I understood the meaning of in-computer communication andbig conference
area networking

29 March -2 April, 1998

submission
deadline
conference Test 2:

San Francisco, USA

heldICTAI'97{ \The test screen is helpful because I cangood for sightseeing:-)November 4-7, 1997

pick up important information quickly although it took me for a while to know howto understand the associations. (Subject1)"{ \I prefer seeing the test screen to reading acollection of CFPs (Subject 2)."

Newport Beach, California, USA

topic

May 16, 1997
Tools with Artificial Intelligence

Figure 4: Test Screen 3 (Information of CFP Announced in our Laboratory)
this subject", \I want to talk to this subject, becauses/he may be interesting", and so on. We think thattransmitting ideas using associations between groups
leads to know people and therefore facilitates for human communication.

5 Experiment 3We investigate the di erence of understanding between
natural language texts and information extracted fromthem using associations.
5.1

MethodApparatus

The same as in Experiment 1.

Three 2nd-year M.Eng students in our lab-

Subjectsoratory.

Procedure

The following cases were tested.

 Test 1: Subjects were shown Test Screen 2 of Ex-periment 1 and original articles, and asked somecomments.

 Test 2: Subjects were shown Test Screen 3 ofExperiment 1 and 3 CFPs (each CFP is written
in a A4 paper), and asked some comments.

In both tests, subjects were not explained the meaning of associations.
5.2

Results and Discussion

Some of the Comments are described below.
 Test 1:{ \An index like the test screen is helpful forbrowsing an newspaper article. (Subject 1)"

We think that it is useful to extract informationfrom information sources using associations.

6Conclusions

We investigated how people generate and understandthe weak information structures based on three experiments. We found the following. (a) Adults who havecomputing skills can generate weak information structures without diculty. (b) Ideas can be transmittedusing weak information structures among people whoshare knowledge. (c) Associations are comprehensible
to adults who have computing skills. (d) The morehuman background knowledge people have, the morepeople understand weak information structures.

References[1] Harumi Maeda, Kazuto Koujitani and Toyoaki Nishida.
A Knowledge Media Approach Using Associative Representation for Constructing Information Bases. In Proceedings of The Ninth International Conference on Industrial and Arti cial Intelligence and Expert Systems(IEA/AIE-96), pp.117-126, 1996.
[2] Harumi Maeda, Masao Kajihara, Hidekazu Adachi, Atsushi Sawada, Hideaki Takeda and Toyoaki Nishida.
Weak Information Structures for Community Information Sharing. International Journal of Knowledge-BasedIntelligent Engineering Systems, Vol.1 No.4, pp.225234, 1997.
[3] Harumi Maeda, Takashi Hirata and Toyoaki Nishida.
CoMeMo: Constructing and Sharing Everyday Memory. In Proceedings of the Ninth International Conference on Tools with Arti cial Intelligence (ICTAI'97),
pp. 23-30, 1997.
[4] William A.Woods. What's in a Link: Foundations for
Semantic Networks. Readings in Knowledge Representation. pp. 217-241. Morgan Kaufmann Publishers, Inc.,
1985.