UTILITY OF WEAKLY STRUCTURED MEMORY ORGANIZATIONFOR INTEGRATING HETEROGENEOUS INFORMATIONHARUMI MAEDA

KAZUTO KOUJITANIyTOYOAKI NISHIDA

Graduate School of Information Science,
Nara Institute of Science and Technology,
8916-5,Takayama,Ikoma,Nara 630-01 JAPAN
Phone: +81-7437-2-5265 Fax: +81-7437-2-5269
E-mail: harumi-m@is.aist-nara.ac.jp
Nara

Abstract

This paper proposes the use of weakly structured
memory organization as a means of helping people develop and maintain memory from heterogeneous information sources. In order to endorse the claim, we haveimplemented a system called CM-2. CM-2 is characterized as an intelligent ampli er whose primary design goal is enhancement of human intelligence. CM-2
helps users: (a) build the memory containing multimedia information, (b) organize the memory from multiple perspectives, (c) incorporate and integrate information from external sources (e.g., WWW, CD-ROM),(d) access the memory in an associative fashion, and(e) incrementally re ne and organize the memory. We
report the evaluation of CM-2 against two test cases:(1) ontology development and (2) information reorganization of WWW pages.
keywords: weakly structured memory organization, ontology development, information reorganization

1 Introduction
The purpose of this research is to help people in theinformation age develop and maintain memory (eitherpersonal or group) from diverse heterogeneous information sources from the world-wide information network. Critical issues there are: (a) information gathering and ltering, (b) information classi cation andevaluation, (c) handling multimedia information andmedia conversion, (d) incremental and adaptive construction of the memory structure, and (e) information retrieval from the memory structure. This paperaddresses the last two problems.
In this paper, we point out that use of weakly structured memory organization is e ective in achieving oury Presently with OMRON Corporationtemple

Todai-ji Horyu-ji Kofuku-ji

Figure 1: Weakly Structured Memory Organization
goal and endorse the claim by an implemented system called CM-2 and a couple of experiments. CM-2 israther characterized as an intelligent ampli er whose
primary design goal is enhancement of human intelligence, rather than an arti cial intelligence which exhibits intelligence behavior by itself. In other words,
our research is characterized more as a knowledge media research[1] than as an AI research.
The technical contribution of this paper is the weakly
structured memory organization consisting of associations, each of which is a many-to-many mapping ofmemory units. In a couple of test cases, we have madea preliminary experiments on generating associations
from existing HTML documents and re ning them.
In what follows, we rst describe the role of weakly
structured memory organization and overview the CM2 information base system. We then report the evaluation of CM-2 against two test cases: (1) ontology development and (2) information reorganization of WWW
pages.

2

Weakly Structured MemoryOrganization

We introduce Weakly Structured Memory Organization as a primary information structure for articulating conceptually diverse information by aggregating
conceptually relevant information. The basic entities
of weakly structured memory organization are (a) aunit which represents either a concept or an external
datum, and (b) an association which connects a collec-tion of key concepts with a collection of units which isnormally reminded by the given keys. Figure 1 shows
an example of associations. This denotes that fromgiven concepts \Nara" and \temple", one may be reminded of \Todai-ji", \Horyu-ji", and \Kofuku-ji".

(a)Sightseeing Information in Nara
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

3CM-2

Information Base SystemCM-2

1 is a knowledge media information base systemon which humans and computers collaborate each otherto accumulate, share and explore diverse information
gathered from heterogeneous information sources.
CM-2 helps the user or the group of users: (a)build the memory containing multimedia information,(b) organize the memory from multiple perspectives,(c) incorporate and integrate information from external sources (e.g., WWW, CD-ROM), (d) access thememory in an associative fashion, and (e) incrementally re ne and organize the memory.
In addition to basic editing, browsing and retrieval
mechanisms, CM-2 has four facilities to support theseuser activities and they are stated below.

Information Capture Facility Information capture
facility generates associations from various information
sources. The general procedure of information capture
facility for WWW pages consists of the following steps:(1) extraction of noun phrases and generation of units
based on morphological analysis[2] and (2) generationof associations by analyzing the structure of HTML
documents.

(b)
Nara

Todai-ji(c)
Kofuku-ji

Naratemple

Kofuku-ji

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

FiveStoriedPagoda

AssociationsDaibutsu(e)Todai-ji:is-a temple:has-part
Great Budda
Hall
Kofuku-ji:is-a temple:has-partFive-StoriedPagoda
temple:has-part? building:in ? placestatueAssociationsDaibutsu:is-a statue(d)Five-StoriedPagoda:is-a buildingtemple
is-aGreat
Budda Hall:has-part
DaibutsuNarais-aininTodai-ji

Kofuku-ji

haspartbuilding
is-aGreat Budda HallShomu:is-a person

in

Frame

haspartFiveis-a
StoriedPagoda
Daibutsu is-astatue

Semantic 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-Pagodabuilding)(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

Information Re nement Facility Information re-Figure 2: Test Case 1: Ontology Development

IS-A Relation Generation Facility IS-A relation
generation facility generates IS-A relations by analyzing the class of given units using heuristics.

task, because it needs much e ort to collect and selectterms through task analysis. We apply our approachbased on the weakly structured memory organization
to ontology development. It allows for data-driven ontology development, by accumulating raw data and incrementally creating the structure of concepts throughhuman-computer collaboration (raw data ! associations ! re ned associations ! semantic networks !
frames ! predicate logic) . The overall process of ourapproach is shown in Figure 2.

nement facility re nes incoherent associations into coherent associations using heuristics and measurementof similarity.

Frame Generation Facility Frame generation facility generates frames, in other words, reorganizes given
associations to a set of entities and attributes by pathnding.

4 Test Cases
4.1

Test Case 1: Ontology Development

Ontology plays a central role in achieving large scale
knowledge sharing. Unfortunately, development of ontology is often a quite painstaking and time consuming
1 \CM" stands for \Contextual Media" which stands for ourlong term theoretical goal.

Experiment We gave 30 WWW pages concerning

ARPA Intelligent Integration of Information (I3) Initiative 2 to CM-2. Each page contains overview ofprojects which belong to the I3 Initiative. CM-2 generated associations by information capture facility. Generated associations themselves are incoherent and cannot be used as ontology as they are. Information renement facility assisted users to formate these associations into more coherent structure. Figure 3 shows
2 http://dc.isx.com/I3/

Figure 3: An Example Result of Re ned Associations
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 2.
4.2

Test Case 2: Information Reorganization of WWW Pages

It is dicult to reorganize relevant information fromlarge-scale diverse information on World Wide Web.
To integrate a wide variety of diverse information onWWW, we use the weakly structured memory organization to extract information from WWW pages andreorganize it according to user's input. The overview
of the process is shown in Figure 4.

Experiment We gave 100 WWW pages concerning
AI researchers to CM-2 for organizing AI directories.
CM-2 extracted units about 7 classes (researchers, projects, e-mail, topics, universities, departments and laboratories), and generated associations. CM-2 reorganized these associations to display various directories
according to user's input.
Figure 4 (a) shows an example result when a userinputs \reasoning",\researcher",\e-mail",\project" and\university" and selects \table". For instance, researchers

such as 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.
Figure 4 (b) shows an example result when a user
inputs \project", \researcher",\e-mail",\university" andselects \list". In this case, projects are extracted rst
and then other relevant information are reorganized.
A summary of the results of two tests are shownin Table 1: (1) to display researchers' table (Test 1)and (2) to display projects' list (Test 2). The resultsof Test 1 (90% at precision rate, 83% at recall rate)
are better than those of Test2 (68% at precision rate,73% at recall rate), because original WWW pages are
persons' pages.
Table 1: Result of Test Case 2
TestPrecision
RecallTest 1 (researcher)
90%
83%
Test 2 (project)
68%
73%
units 2 100 (%)
Precision: appropriate
generated units
appropriate units
Recall: units whichshould be extracted 2 100 (%)

5

WWW Pages
James F.Allen's Home Page

Barbara Hayes-Roth's Home Page

Our work is related to recent work on information extraction from heterogeneous sources on the Internet([3],[4],[5],[6],[7]). Instead of focusing on the strategies and heuristics for information gathering, we concentrate on how to classify information obtained frommultiple information sources and integrate it into personal information base. Our approach is to provide
a framework of collaborations for human information
sharing with a low structural facilities.

6Associations
James F.Allen

URCS Faculty Member

Ph.D.

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

E-mail Address

AdaptiveIntelligent
Systems
Project Leader

Senior ResearchScientist

CM-2 output(a) Test 1 (researcher)(b) Test 2 (project)

Figure 4: Test Case 2: Information Reorganization ofWWW Pages

Related WorkConclusions

We proposed the weakly structured memory organization called associations as a means of helping people develop and maintain memory from diverse heterogeneous information sources. We described a systemcalled CM-2 which implements our claim. We applied
CM-2 to two test cases and the results indicate that
our approach is e ective for (1) ontology developmentand (2) information reorganization of WWW Pages.
As a future research, we plan to integrate associations generated by other sources such as Usenet articles.

References[1] Mark Ste k. The next knowledge medium. AI Magazine,
7(1):34{46, 1986.
[2] Eric Brill. Some advance in transformation-based part ofspeech tagging. In Proceedings of the Twelefth National Conference on Arti cial Intelligence (AAAI-94), 1994.
[3] Alon Y. Levy, Yehoshua Sagiv, and Divesh Srivasava. Towards ecient information gathering agents. In Working
Notes of the AAAI Spring Symposium on Software Agents,
pages 64{70, 1994.
[4] Robert Armstrong, , Dayne Freitag, Thorsten Joachims, andTom Mitchell. WebWatcher: A learning apprentice for theWorld Wide Web. In Working Notes of the AAAI Spring
Symposium on Information Gathering from Heterogeneous,
Distributed Environments, pages 6{12, 1995.
[5] Marko Balabanovi'c and Yoav Shoham. Learning information retrieval agents: Experiments with automated webbrowsing. In Working Notes of the AAAI Spring Symposiumon Information Gathering from Heterogeneous, Distributed
Environments, pages 13{18, 1995.
[6] Wen-Syan Li. Knowledge gathering and matching in heterogeneous databases. In Working Notes of the AAAI Spring
Symposium on Information Gathering from Heterogeneous,
Distributed Environments, pages 116{121, 1995.
[7] Michiaki Iwazume, Hideaki Takeda, and Toyoaki Nishida.
Ontology-based information gathering and text categorization from the internet. In Proceedings of the Ninth International Conference in Industrial and Engineering Applications of Arti cial Intelligence and Expert Systems, 1996.
305{314.