NETWORK AND KNOWLEDGE INTEGRATION
Hideaki Takeda

Graduate School of Information Science
Nara Institute of Science and Technology
Ikoma, Nara, Japan

Toyoaki Nishida

Graduate School of Information Science
Nara Institute of Science and Technology
Ikoma, Nara, Japan

ABSTRACT

DesingerIn this paper, we discuss how ontologies can represent andIntention

handle enormous and heterogeneous information in order toorganize information according to our intention. We discuss
ontologies from two points of view in this paper. One isMedia Level

how to formalize ontologies that can represent multiple def-Formalization Levelinitions for concepts. We propose to formalize ontologies byaspects and discussed its nature. The other is how ontologies

Model Level

can work to deal with information. We show ontology-based
information navigation system called IICA that can collect,

Productclassify, and extract information on WWW. Ontologies here
work as background knowledge to handle enormous and het-

Fig. 1. Levels of Information Representationerogeneous information.

INTRODUCTION

signers try to capture unstructured information, and some

Design is a process of creating new artifacts, especially

excellent and common organization of information can be-creation of speci cations of new artifacts for manufacture

come models. Much of designers' work is done with unstruc-from given requirements. Although it is a visible and appar-

tured information.

ent process in design, there is another usually invisible but

Figure 1 illustrates levels of information to deal with. The

also important process in design, namely, reorganization oflowest level is \model" level, i.e, information represented as

knowledge. When a new requirement is given to designers,

models. The middle level is \formalization" level, where in-they sometimes survey their internal knowledge like experi-

formation is represented by some formal language. The dif-ence and expertise and sometimes collect external knowl-

ference between the model and formalization levels is thatedge like books and catalogs in order to solve the given

the former has syntax and semantics, and the latter has only

requirement. Such introduction of new knowledge and re-syntax. The top level is \media" level where information rep-interpretation of existing knowledge cause designers' knowl-

resentation is only restricted by property of medium whichedge to reorganize to adapt current and future design tasks.

is represented by.

In this paper we focus on this process, in particular, pro-All of these three levels are needed for design because

cess of representation of unstructured information to gather

design can be completed only with mixture of some di er-from outer world.

ent domains. In general, the lower levels can deal with only
speci c domains and can o er speci c solutions in those do-

INFORMATION FOR DESIGN

mains. On the other hand, the upper level can deal with

Most crucial part of information organization is not inwell-structured information but in unstructured one. De-

wider domains but can o er less speci c solutions. The middle level can o er interface between both levels.

signers use various kind of information from physical models

Design process is performed by penetrating the media,

to natural languages. We have usually paid attention to so-formalization, and model levels in order (see Figure 1).

called \model", e.g., 2/3D model, some mathematical model

When domain of design is quit new, much e ort of designfor thermo and dynamics. Since models are well investigatedis done in the media level before going the lower levels. On

and understood, there are many books and studies to handle

the other hand, when domain is well investigated, the media

them. But models are not sources but results of knowledge

level is relatively less important and much work is done inorganizing formed for a long design history. Individual de-the model level.

|1|

We discuss the formalization level as \ontology" in thisTemplepaper. Although ontology cannot capture all functions of

Templethe formalization level, it can o er one way to connect thefounded year
sectfounder

media and model levels.

RULE OF ONTOLOGY IN KNOWLEDGE REPRESENTATION

Temple historical events

As mentioned in the previous section, we adopt \ontol-

ogy" as the formalization level. Ontology is a term in phi-style of architecture

namehistorical value

location

losophy and de ned as \the branch of metaphysical enquiry

events of the year

concerned with the study of existence itself " [1]. In AI community, an ontology is de ned as \an explicit speci cation

chief priest
branch temples
parishioners
doctrinearea
representative

Templeof conceptualization"[3] which is intended to use as base forknowledge representation. Ontology is represented as sys-cultural assets

tems of symbols in computers, i.e., symbols and their relations represents concepts and their relations. Ontology commits only declarative nature of systems of concepts because
its purpose is to provide consent for knowledge representa-

Fig. 2. What is Ontology for Temple?

tion, e.g., identi cation of concepts. Therefore ontology isadequate as representation for the formalization level.
Since our main concern is how we can deal with various

name or ocial name with according to aspect it is used.

and unstructured information, we should consider how on-

In such cases, the more concepts are collected, the less clear

tology can be used for dealing with such information. In theare meanings of concepts.

Section 4, we discuss formalization of ontology and extend

Each concept is meaningful if and only if concept is used

it to deal with various information sources. In the Section 5,

in proper way, that is, concept is used with concepts which

we discuss practical value of ontology by showing use of on-come from the same conceptualization. We call this unit

tology for information handling like collecting, classi cation,

aspect. We can say that an aspect is a consistent view forand extraction.

conceptualization. Then ontology can be composed of some

MULTIPLE ASPECTS

aspects.

tology in order to realize multiple ontologies.

the common-sense world of traveling, we may use aspects

Aspects

like trac aspect or geography aspect.

We use various aspects, for example in engineering we use

In this section, we introduce aspect as component of on-

aspects like dynamics aspects, kinematics aspects. To model

There are two issues on aspect. One issue is what should

As we mentioned in the previous section, an ontology is

be in aspect. Aspect should have a vocabulary to describe

related to a conceptualization. Building a large single ontol-

phenomena in its domain. It should also have a theory which

ogy means that we can model a large domain by a single

associates concepts in its vocabulary. And the theory should

conceptualization. It is one of reasons that building large

be consistent. In the other words, aspect is what we canontologies is not easy in fact.

conceptualize the world without inconsistency .

1

We often confuse and force to unify concepts from di erent conceptualization when we try to build large ontologies.

The other issue is how to compose aspects from otheraspects.

For example, Figure 2 shows how concept \temple" is mod-We provide two types of basic connections among aspects.

eled di erently. One may think temple as item in textbook

One is combination aspect. This is just integration of aspectsof history, so \founded year", \sect", \historical events",

for di erent domains. For example, one of the ways to build

and so on are used with \temple". One may think temple
as place for religion, so \doctrine", \parishioners", \branch

aspect-for-travel is to combine aspect-for-hotel and aspectfor-trac. In this case, concepts like \railway" which is intemples", \chief priest" are concepts related to \temple". Of

aspect-for-trac do not exist in aspect-for-hotel, because

course, some concepts are appeared in more than one aspectdomain of modeling is di erent from each other. In aspect-

like \name" and \location".

for-travel, concepts like \tour" are de ned using concepts

Mixture of concepts from di erent conceptualization often

from both aspects.

confuse us. Some di erent concepts in di erent aspects may

The other is category aspect. This is collection of aspects

denote the same fact, for example \chief priest" in aspect-which share domains but come from di erent conceptualiza-

for-temple-as-religious-place and \representative" in aspectfor-temple-as-public-organization may be the same. On thetion.

other hand, concepts used in more than one aspect may
denote di erent facts, for example \name" is used in common, but its meaning is either religious name or historicalThe other important content of aspect is intention, because aspect is abstraction of the target world by some intention. But
intention itself is seldom written explicitly. We have tried to represent intention of aspect of object modeling as function[8].
1|2|

De nition 1An atomic aspect A with a consistent theoryTAsight-seeing tourL

aspect(A)( ) of a rst language
and with a unique namesatis es the following formula.

aspect(A) $ T (A)sight-seeing pointsight-seeing hotel

aspect(A) is an identi er of aspect which has a similar e ect

to the second argument of predicate ist in Ref. [5], and acharge withtwo meals

templemodal operator in Ref. [6].
Then, we introduce

hotel

and
place

itinerary

hotel chargemLm
E and L as modal extension of LE

L respectively. Here we assume domain of individuals

are always the same regardless of possible worlds. In thefollowing discussion, we assume this language

Lm
E.

A combination aspect is simply de ned as follows.

De nition 2T (ACOM (A1; : : : ; An)), aspect theory of com-

charge without
meal

bination aspect for aspectde ned as follows;Tbusiness hotel(

A1 ; : : : ; An , is a consistent theory

ACOM (A1 ; : : : ; An )) = aspect(A1 )^ : : : ^ aspect An ^ I A ; : : : ; An
where I A ; : : : ; An is a set of formula of language Lm [faspect A ; : : : ; aspect An g, which means inter-aspect the(business tour

: category aspect

(

1

(

1)

(

)

1

)

A1 ; : : : An .

ory among: combination aspect

)

(

)

Apparently, it would cause unexpected results if some ofFig. 3. Multiple and Sharable Ontologies

aspect theories share predicates. We ideally assume that if
the same predicates appear in some aspects, there should

2

When a temple is modeled di erently we have justshown, we can assume there is a category-aspect-for-temple.
This

aspect has

some speci c

aspects

for temple like

aspect-for-temple-as-history-textbook and aspect-for-templeas-religious-place as component. Since component aspects
share domains, it is reasonable (but not mandatory) that
there are relations among concepts in di erent component
aspects. Such relations are contents of the category aspects.
Since combination and category aspects can use othercombination or category aspects as component, we can construct large aspects from relatively small aspects. We callsuch relatively large aspects as ontologies.
Figure 3 is an example how di erent ontologies can bede ned with sharing aspects.
Two aspects can share aspects in their constitution, or beconnected by category aspects. We call these two aspects arecompatible. That is, they may share or transfer information
to each other.
In the following sections, we

rst describe aspect in a

logical framework, and then in a programming framework.
Finally, we show how communication between di erent aspects can be established.

A Logical Formalization of Aspect

Since our basic policy is to de ne aspects constructively,

we start from de ning atomic aspect and then de ne more
complicated aspects.

We assume a
rst-order language LE , and predicate
aspect of LE . L is a rst-order language which is the same
to LE except predicate aspect is removed.
Aspect Theory.

First we de ne atomic aspect, aspect

which does not depend on any other aspects.

share some concepts in them . In such cases, it should be
represented by category aspects.
On the other hand, a category aspect is more complicatedbecause it does not imply that both of aspect theories arealways true. In order to represent a category aspect, we introduce modal operators

2 (necessity) and 3 (possibility)

and assume S4 modal system. Then a category aspect for
two aspects is de ne as follows.

De nition 3T (ACAT (A1; : : : ; An)), aspect theory of cate-gory aspect for aspect
ned as follows;

T

A1 ; : : : ; An , is a consistent theory de-

ACAT (A1 ; : : : ; An )) = 3aspect(A1 )(^ : : : ^ 3aspect An ^ I A ; : : : ; An
where I A ; : : : ; An is a set of formula of language Lm [faspect A ; : : : ; aspect An g, which means inter-aspect the(

(

1

(

1)

)

(

1

)

)

(

)

A1 ; : : : An .
I (A1 ; : : : ; An ) is again an inter-aspect theory among A1 ; : : : ; A2 .

ory amongSince we can use combination and category aspects ascomponent of aspects, we can de ne hierarchical aspects
using combination and category aspects. In other words,

An aspect A is represented A = f (A1 ; : : : ; An ) where
A1 ; : : : ; An are aspects and function f is composed by ACOM
and ACAT .
Inter-aspect Relations.

between aspect,

Then we can de ne relations

inclusion and strict inclusion.

De nition 4An aspect A is included in aspect B if andonly if aspect(B ) ` 3aspect(A).
De nition 5An aspect A is strictly included in aspect B

aspect(B) ` aspect(A).
Of course, it is too strict in practise. In programming approach
we allows the same predicates in di erent meanings.
if and only if2|3|

(de ne-aspect aspect-nameNote that there are two reasons for these relations, i.e.,

Ontoligua de nitions are here

one is composition or category relations among aspects and...

the other is logical implication. Strict inclusion corresponds)

weaker-than relation in Ref. [6].

(a) atomic aspect

Similarly, relations between formula and aspect are de-(de ne-aspect aspect-name(:use componet-aspct1, ...)(:rename (aspect!predicate . new-name) ...))

ned.

De nition 6A formula f is included in aspect A if andaspect(A) ` 3f .
De nition 7A formula f is strictly included in aspect A
only if

if and only if

Ontoligua de nitions are here

aspect(A) ` f .

...

(b) combination aspect(de ne-category-aspect aspect-name(:use componet-aspct1, ...)(:category-type category-type)(:rename (aspect!predicate . new-name) ...)(de ne-translation aspect-name(=atmoic-formula1 atmoic-formula2)((:query-procedure nil or t
:inform-procedure nil or t

These de nitions mean that there are two types of interpretation of aspect theories. One is represented as strict

inclusion which is traditional way of inter-theory relation.
The other is inclusion which takes account of all alterna->

tives of theories. By having two types of interpretation, we
can deal with both strictly a single representation and vari-translation-ruleety of representations.

Theorem 1If aspect A is strictly included in aspect B , thenA is included in aspect B.
Another relation is compatibility which is criteria two
3
aspects are related to each other .
De nition 8Aspect A and B is compatible if one of thefollowing condition is satis ed;(1)
andis the same aspect,(2) there exists aspect
which has bothandascomponent,(3) there exist compatible aspect 0 and 0 are components ofandrespectively.
Formulaiswith aspect
if andonly if there exists aspect
in which is andis compatible
with .

2 A
2
2

B

A

De nition 9

C

B

A

A

A

f

compatible
B
fA

Compatibility assure neither consistency nor translatability between aspect theories, but denotes existence of connection between aspects.

ASPECTOL: A Language for Aspects

Here we show a language of aspects called

ASPECTOL

extension of Ontolingua-like ontology de nition (see [2]).

ASPECTOL is shown in Figure 4.

De nition of an atomic aspect consists of declaration of
aspect name and de nitions of classes, relations, and functions. De nition of a combination aspect is de nition of an
atomic aspect and declaration of including aspects. De nition of a category aspect consists of a set of translation formulae. A translation formula is de ned between two aspects
in a category aspect, and is de ned as define-translation

which describes logical relation between concepts in bothaspects. It can represent a class of direct relations between
formulae in two aspects which we have discussed in the previous section. A left hand side of an implication formula is aformula of the aspect of the

Ontoligua de nitions are here(c) category aspect

Fig. 4. De nition of aspect

ONTOLOGY FOR INFORMATION NAVIGATIONThe other view of ontology we discuss in this paper is how

it can contribute to organize real unstructured information.
We built a system called IICA (Intelligent Information Collector and Analyzer) that can gather, classify, and extract
information on Internet by using ontologies.

Ontology-Based Approach for Information Navigation(Aspect-based Ontology Description Language), which is an
Syntax of

)

B

B

B

...
))
...
)
(de ne-translation ...)...

rst argument and a right handside is a formula of of the aspect of the second argument.

Information on the Internet, in particular, WWW infor-

mation is various and enormous and furthermore totally decentralized, so that there need services to guide such information. But the existing approaches are insucient to organize information because they treat information without
knowledge.
There are mainly two types of services to guide information on the Internet. The

rst is so-called \WWW spider"that collects and indexes WWW pages periodically so thatusers can ask pages by keywords, e.g., Lycos and Infoseek.
The second is directory service that provides hierarchicalcategories of WWW pages, e.g., YAHOO!. The former isuseful when users can express what they want by keywords,
but it is dicult to use when they have only vague goals.
On the other hand, in the latter approach, users canndtheir goals just by choosing categories repeatedly so that it
is possible tond their goals even if their goals are vague.

But it depends on how categories are well maintained.
Ontology-based information navigation proposes a new
way of information navigation because information is treatedas knowledge. In our approach, users can de ne their goals

3

Term compatible is borrowed from Ref.[4].

by browsing ontologies, and then the system collects and|4|Internet
WWW
restaurant

1 search/collecttempleset of WWW pages

TravelHousing

URL name ingredient effecthotspring

2 classify

3 extract

extracted information

classified WWW pages

As taxonomy
As heuristics for pruning

Hotel(Japanese)
Hotel(Western)
LeisureRecreation Ground
TheatreTheme ParkAs features of concepts

Zoo
Sea

ontology

Excursion Ship
Bay Area
AquariumFig. 5. Roles of ontologies in IICA
SpringSpring Name
Visiting Place
Type of BathIngredient
Effectclassi es WWW pages, and furthermore extract informationin them. All information in these processes are provided in
ontologies (See Figure 5).

Sightseeing

Roles of ontologies

Basically ontologies represent background knowledge that

Fig. 6. Ontology as Interface

human use consciously or unconsciously when they read
pages. For example, suppose searching information on \arti cial intelligence". If you

nd a page on \expert systems",

you may think this pages is related to your goal even if
there are no descriptions on \arti cial intelligence" directly.
It is because there is ontological relation between \arti cialintelligence" and \expert systems", probably the former issuperclass concept of the latter.
In IICA, there are four roles of ontologies, i.e., ontologies
plays di erent roles in each step in information navigation,i.e., guide for users for choosing goals, heuristics for pruning search in information gathering, categories in information classi cation, and features of concepts in information
extraction.
We adopt a simple structure to represent ontologies toachieve above roles. It is dicult to use strict representation like logical formula because information sources are notstrict either. There are two types of concepts, i.e., class andattributive concepts, and interclass and class-attribute relations. A class concept has a name and keywords that areused as keywords in search and classi cation, and attributive concepts which are connected by class-attribute relation. Class concepts can be connected by interclass relation
which is super-sub relation with weight to represent similarity between two concepts. There are some types of class-attribute relations like has-some which indicate how many

Then, IICA collects keywords of concepts speci ed byusers and those of concepts related to these concepts. ThenIICA collects links whose titles include these keywords. It isbecause concepts close to the given concepts in ontologicalrelations are also important tond information, e.g., theirsuperclass or subclass concepts may be used in pages. Ontologies are used as heuristics for pruning search secondly.
Thirdly, IICA evaluates full texts of the collected pages
and classi es them into concepts in ontologies. In classi cation process, TFIDF method [7] is used. It is e ective when
there may be many pages found by search process (see Figure 7). ontologies are used as categories to classify pages.
Finally, IICA extracts information from pages by consulting attributive concepts of the given class concepts. Each
concepts can have attributive concepts which are represented by patterns of texts. For example, concept \hotspring" can have four attributive concepts;(define-pclass(hot-spring((has-at-least name)(has-some types-of-bath)(has-some ingredient)(has-some effect))))Concept \e ect" which is one of attributive concepts ofvalues attributive concepts should hold. Attributive concept
represents one which has one or more values and is repre-\hot-spring" is represented as follows;

sented as pattern of words or concepts (see next subsection).

(define-concept(effect(is disease with (or "効能>" "効果 " "効く ")))
(define-concept(disease(is (or "+ 病>" "+ 傷>" "+ 痛>")))

Processes of Information Navigation

Firstly, users can browse concepts in ontologies andchoose some of them as goals they want. Here ontologies
plays guide for de ning goals (See Figure 6).

|5|SpringnameTraffic

Sea

NearestStationTraffic
MethodType of
Bath

IngredientClassified
URLs

Sightseeing

Festival

By BusKanaketa JR
Sankaku
Spa
Station

Aerated,chalybeate

On foot

No Special
Ingredient

YoshioSpa

JRYoshioStationWalk

History

Fig. 8. Results of Information Extraction

SpringREFERENCES[1]A. Flew, editor. A Dictionary of Philosophy. Pan Books,
1979.

Fig. 7. Results of Information Classi cation[2]T. R. Gruber.

Ontolingua: A mechanism to support

portable ontologies. Technical Report KSL 91-66, StanTheford University, Knowledge Systems Laboratory, 1992.

rst rule represents that concept \e ect" is concept\disease" which appears with \効能"(\ecacy" in Japanese)[3]T. R. Gruber. Toward principles for the design of ontolo-or \効果"(noun \e ect" in Japanese) or \効く"(verb \e ect"gies used for knowledge sharing. Technical Report KSL

in Japanese) in a single sentence. The second is that concept

93-04, Knowledge Systems Laboratory, Stanford University, August 1993.

\disease" is a word ending either character \病" (illness)or \傷"(injury) or \痛"(ache). As results, IICA can build[4]N. Guarion, M. Carrara, and P. Giaretta. Formalizing on-a table as summary of some di erent pages for a concept

tological commitments. In Proceedings of AAAI-94, pages(See Figure 8). In this560{567, 1994.

gure, each row contains values of

attributive concepts extracted from a single page, and eachContexts: A Formalization and Some Applications. PhD thesis, Department of Computer Science,[5]R. V. Guha.

column contains an attributive concept and its values inthose pages. Here ontologies are used as features of concepts

Stadford University, Stanford, CA, 1991.

when IICA extracts information form pages.

Report No. STAN-CS-91-1399-Thesis).

(Available as[6]P. P. Nayak. Representing multiple theories. In Proceed-

SUMMARY

ings of AAAI-94, pages 1154{1160, 1994.

We discussed ontologies from two points of view in this[7]G. Salton and M. McGill. Introduction to Modern Infor-

mation Retrieval. McGraw-Hill, Inc., 1983.

paper. One is how to formalize ontologies that can represent
multiple de nitions for concepts. We propose to formalize[8]Y. Umeda, H. Takeda, T. Tomiyama, and Y. Yoshikawa.

ontologies by aspects and discussed its nature. The other

Function, behaviour, and structure.

is how ontologies can work to deal with information. We

Applications of Arti cial Intelligence in Engineering V,

show ontology-based information navigation system calledvolume 1, pages 177{194, Berlin, 1990. Springer-Verlag.

IICA that can collect, classify, and extract information on
WWW. Ontologies here work as background knowledge tohandle enormous and heterogeneous information.
It is important how we can represent and handle various
information in a uni ed way, as we have shown, but it ismerely the

rst step to organize information according to

our intention. Further research should be done how we canexternalize new organization of information from existing
information.

|6|

In J. Gero, editor,