ONTOLOGY MODELING TOOL USING CONCEPT

DICTIONARY AND INFERENCE

Yoichi Hiramatsu

Galaxy Express Corporation

1-18-16 Hamamatsucho, Minatoku,

Tokyo 105-0013, Japan

Keywords: Ontology Modeling, Editing Tool, Concept Dictionary, EDR, Lexical Dictionary, WordNet, Inference

Algorithm, Web service, Enterprise System, Common Lisp, I-S-A semantic link, Operation Support System

Abstract: The usefulness of ontology is strongly dependent on a knowledge representation policy and its maintenance.

The subject of knowledge representation and modeling tools has been one of the most exciting themes

among ontology scientists. Some ontology editing tools originated and developed in the field of expert

systems, and others were designed originally by ontology research groups. Key features of the newly

implemented tool are: (a) reference to a concept dictionary (EDR, WordNet) to ascertain word semantics,

and (b) use of an inference algorithm (MOP) provided by Schank et al. Satisfactory results were obtained in

the application of ontology modeled by the present tool. We will discuss how our tool was constructed and

describe applications using the tool to achieve solutions for enterprise integration. This work was developed

as a part of the project entitled “Operation-support system for large-scale system using information

technology” (Koide et al., 2003) for the Japanese Government IT Program, period 2002-2005.

1 INTRODUCTION

One of the design goals of the ontology modeling

tool is to provide an interactive and graphical facility

for constructing ontology representation files. A

graphical user interface (GUI) should work as an

advisory system wizard providing developer

guidance during the construction of class hierarchies

and relationships. This paper deals with one aspect

of these goals: that is, implementation of a modeling

tool (named “MOP Editor”) connected to a concept

dictionary and to an inference algorithm. Despite the

existence of attractive tools to model ontologies, we

have persisted in developing the necessary tool for

our specific requirements.

The category of modeling tools described herein

ould gain considerable importance for the

concretization of novel software architecture such as,

for example, those which model and generate

business-to-business (B2B) applications supporting

decentralized and dynamic electronic agents (Alloui

et al., 2003).

If we find a way to access an external source of

referen

ce to determine ontology, we can save time

during the editing phase. We have decided to use an

online concept dictionary (EDR, 2003) and a lexical

dictionary (WordNet, 2003) to refer to for concepts

of Japanese and English terms. And, if we devise a

way to interactively test functionality of the

“in-construction” ontology during the construction

phase, the ontology quality is improved significantly

in comparison with that obtained without interactive

tests. Thus, we have chosen an inference engine

known as MOP (Memory Organization Package)

algorithm introduced by Schank et al. (Riesbeck et

al., 1989; Schank et al., 1994) in order to execute

ontology evaluation tests. MOP is a kind of

frame-based knowledge formalism that holds class

hierarchy, in which concepts and instances are

represented by mop objects.

The effectiveness of MOP Editor and the

con

structed ontology were evaluated against

objective design criteria using three kinds of Web

services: message generation, synonym retrieval,

and ontology sharing. Web services are distributed

around the system and work collaboratively.

2 ONTOLOGY AND DOMAIN

FEATURES

Ontology is a set of conceptual building blocks of

225

Hiramatsu Y. (2004).

ONTOLOGY MODELING TOOL USING CONCEPT DICTIONARY AND INFERENCE.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 225-230

DOI: 10.5220/0002603002250230

 SciTePress

knowledge in a determined application area. It

provides a domain of discourse for knowledge

sharing among computers. Ontology enables a

number of machines to share their knowledge: for

example, for information retrieval systems.

Concretely, ontology enumerates concepts, attributes

of concepts, relationships among concepts, and

constraints on these relationships, thereby defining

the knowledge reference structure of the considered

domain.

Our domain is ground support equipments for

rocket launching at a rocket launch facility at

Tanegashima Space Center in Japan. Entry terms of

the domain are the names of the following objects:

fluid-pump, air-compressor, pipe, junction,

storage-device, electrical-cable, fuel-tank,

oxygen-tank, valve, actuator, fuel, oxygen, vaporizer,

sensor, pressure-controller, etc.

Abstract concepts that correspond to the entry

terms are arranged in such a way as to compose a

class hierarchy in which superclass and class are

connected by an I-S-A semantic link. For example,

“pressure-controller is-a-kind-of controller”.

Ontology can be accurately represented by the class

hierarchy of abstract concepts plus actual instances.

Instances are tagged names of real objects, such as

sensor-AA, controller-101, controller-102, and so on.

Instances are attached to ending points of the leaf of

the class hierarchy. In general, the number of

instances is estimated to be one order greater than

the number of classes. In our present work, we have

selected a quantity of over a hundred for classes and

over a thousand for instances.

3 CURRENT ONTOLOGY

MODELING TECNIQUES

Free software tools and commercial products for

ontology modeling have evolved during the last few

years. Some examples are Ontolingua, Protégé2000,

OntoEdit and Hozo. These tools accept entry terms

and create an I-S-A semantic link for classes,

generating text-type files of class hierarchy. Most of

them are equipped with plug-in features for

importing other ontologies written in XML, RDF,

DAML, OWL, etc.

Ontolingua (Farquhar et al., 1996; Ontolingua,

2003) is a set of tools and services that supports the

process of achieving consensus on common shared

ontologies by geographically distributed groups.

Ontolingua makes use of the world-wide web to

enable wide access and provide developers with the

ability to publish, browse, create, and edit ontologies

stored on an ontology server.

On the other hand, the Protégé system (Gennari

et al., 2002; Noy et al., 2001; Noy and Musen, 2003)

is an environment for building knowledge-based

systems, mainly in the field of domain ontology.

Protégé runs on a variety of platforms, supports

customized user interface extensions, incorporates

the Open Knowledge Base Connectivity (OKBC)

knowledge model, and interacts with storage formats

such as relational database, XML and RDF.

Similarly, OntoEdit (Sure et al., 2002; OntoEdit,

2003; Maier et al., 2003) is another ontology editing

environment that supports the development and

maintenance of ontologies by graphical means.

OntoEdit is built on top of an internal ontology

model, enabling therefore as much neutral modeling

as possible for concepts, relations and constraints. It

is equipped with a GUI menu entry (in which

developers can choose namespace) and a back-end

inference engine.

Another interesting tool that we have

investigated is Hozo (Kozaki et al., 2002; Kozaki et

al., 2000). This has different features than

Ontolingua, Protégé or OntoEdit. Hozo is based on

an ontology theory of role-concept in which the tool

can distinguish concepts dependent on particular

contexts from the so-called basic concept, and can

manage the correspondence between a wholeness

concept and a relatioship concept.

The newly implemented MOP Editor differs

from the modeling tools described above in the

following points: (a) it is coded in Common Lisp

(CL) and Common Lisp Object System (CLOS),

thereby allowing capability for dynamic

maintenance, (b) it makes reference to the concept

dictionary to help developers to build as general an

ontological model as possible, and (c) it supports the

inference engine that will be used to check the

appropriateness of the built ontology.

4 IMPLEMENTED MODELING

TOOL

4.1 Configuration of Tool

Figure 1 is a process view of the MOP Editor. The

GUI facility was implemented using the Integrated

Development Environment (IDE-CG) of the CL.

The MOP algorithm was developed using a set of

functions of CLOS (Koide and Kitamura, 2002).

Although there are a lot of respectable programming

languages available, we think that CL is, in a sense,

the mother of all languages and is highly efficient.

The main reason for this statement is that CL

includes a complete theory of computation by

treating code and data within a single and uniform

system.

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Link characterization among concepts is based

on the I-S-A (is-a-kind-of) semantic link, as adopted

in EDR and WordNet.

4.2 Class Hierarchy

The class hierarchy mentioned here is a

classification hierarchy. Concepts are connected by

an is-a-kind-of relationship. This relationship can be

seen as the following: if X is a kind of Y, then X is a

specialization of Y, while Y is a generalization of X.

Therefore, our ontological representation is based on

the classification hierarchy.

4.3 Data Organization

MOP Editor organizes the developer’s data in a form

of project unit (file named *.mprj). Each project

contains all the information that developers have

typed in, that is; the ontology file (named *.mont)

and the instances file (named *.mins). The ontology

file and instance file are composed of Lisp

S-expressions for mop objects and mop instances,

respectively. The following are examples of

ontology files and instance files:

(defmop mop-object (superclass superclass …)

(role1 filler1)

(role2 filler2)

(role3 filler3)

(role4 filler4)

…)

(definstance mop-instance (superclass)

(has-a mop)

(part-of mop)

…)

The schematic shown in Figure 2 clarifies the

input and output files of MOP Editor. Projects file

*.mprj can be loaded dynamically by developers

during ontology maintenance services, without any

necessity for system shutdown.

Fig

4.4 Utilization of Concept Dictionary

MOP Editor utilizes two kinds of online dictionaries

as a reference source to build the top, domain and

task ontologies. The flowchart in Figure 3 shows

how developers use the parser software and

dictionaries to add new concepts. Developers can

switch between the two dictionaries according to

their needs when looking for new concepts.

EDR

EDR is a machine tractable dictionary of

Japanese words used in the research field of natural

language processing. The words resource is

organized in the form of data records. The main

feature of EDR is that its sub-dictionaries are

interconnected via concepts using concept ID tags.

In this work, we have used three sub-dictionaries

of EDR: (a) a Japanese Word Dictionary, (b) a

Concept Classification Dictionary, and (c) a

Headconcept Dictionary. The role of the Japanese

Word Dictionary is to describe the correspondence

ure 1 : Process View of MOP Editor

Web Service

(server)

Ontology Server

(front-end)

Web Service

(client)

Socket

Web Service

MOP

Editor

E D R

WordNet

(defmop

…)

(definstance

…)

Ontology Server

(back-end)

Developer

Saving data

Loading data

reference

Figure 2 : Schematic of Input / Output

entry.txt

*.mprj

(*.mont , *.mins)

Input file

Output file

（loop applied for existent projects）

*.mprj

(*.mont , *.mins)

MOP

Editor

Figure 1: Process view of MOP Editor

Figure 2: Schematic of Input/Output

Figure 3 : Flowchart

Technical

Documentation

Parser

Stop

Class hierarchy

Start

Edit slots

*.mprj

*.mont

*.mins

“class” &

“instance”

Dictionary of Concept:

Text file

reference

E D R

WordNet

Figure 3: Flowchart

ONTOLOGY MODELING TOOL USING CONCEPT DICTIONARY AND INFERENCE

227

between Japanese words and the concepts

represented by these words, and also to provide

grammatical information for the word when used

with a given meaning. Each record is composed of

the following fields: record number, headword,

invariable portion of the headword and pair of

adjacent attributes, Kana notation, pronunciation,

syntactic category (noun, verb and particle),

syntactic tree, conjugational information, surface

case information, aspect information, word function

information, concept ID, English headconcept,

Japanese headconcept, English concept explanation,

Japanese concept explanation, usage, frequency, and

management information.

Thus, the purpose of the Concept Classification

Dictionary and the Headconcept Dictionary is to

provide concepts for the Japanese Word Dictionary.

The first contains a classification of concepts having

a super-sub relation, and the data record is composed

of the following fields: record number, concept ID of

the superconcept, concept ID of the subconcept, and

management information. The second one gives a

description of each concept in words, and the record

is composed of the following fields: record number,

concept ID, English headconcept, Japanese

headconcept, English concept explanation, Japanese

concept explanation, and management information.

Both dictionaries contain about 400,000 concepts.

MOP Editor uses a concept ID to connect

concepts among these sub-dictionaries. The ontology

developer would display the superclass and class of

concepts when he is looking for a relationship that

can better represent the subsumption relationship (or

I-S-A semantic link) in the treated domain.

WordNet

WordNet is an online lexical resource of English

language used by linguistic scientists. WordNet

consists of lexicographer files that organize nouns,

verbs, adjectives and adverbs into groups of

synonyms called synsets (synonym sets) and

relationships between this synset and other synsets.

Nouns and verbs are organized into a hierarchy

based on the hypernymy/hyponymy and

holonymy/meronymy relationships between synsets.

Here, hypernymy and hyponymy mean, respectively,

superordinate and subordinate relations. For

example, Y is a hypernym of X, if X is a (kind of) Y.

Holonym is the name of the whole of which the

meronym names a part. For example, Y is a holonym

of X, if X is a part of Y. WordNet shows how each

word is linked to others, as for example when the

developer types in the word valve, he gets not only

the synonyms definition, but the hypernyms (a valve

is a kind of what?), meronyms (what are the parts of

a valve?), and more.

4.5 Behavior of Inference Engine

MOP is a frame-based formalism based on the

Schank’s theory about how human memory is

organized, and attempts to remind computers the

way people are reminded.

In MOP algorithm, we use a global symbol named

mop to represent a CLOS object. The mop object can

have multiple superclasses and a set of attributes

represented by slots, such as (role1 filler1), (role2

filler2), (role3 filler3), ... The role is a slot-name

and the filler is a slot-value. The filler takes values

of the type string, integer, double-float and own mop

object. The class hierarchy is a representation of

abstract concepts using mop objects. When

developers create a new mop object, MOP Editor

evaluates this mop object in order to avoid eventual

definition errors of the mop object. MOP Editor still

checks whether the attributes represented by the pair

of role and filler are consistent inside the ontology

file and instances file.

A new mop object is created if all the fillers have

been approved through this error checking

evaluation test. Note that if the filler already exists,

then the previous defined filler is used for the

evaluation test. On the other hand, if the filler does

not exist, then the evaluation test uses the filler of

the present mop object. Therefore, MOP algorithm

differs from the frame-based system in the following

points: (a) the newly created filler behaves as a mop

object, (b) the filler is also structured hierarchically,

representation becomes more and more detailed

every time new mop objects are added to the class

hierarchy. MOP uses slots to place the new instance

at an appropriate location inside the class hierarchy.

Figure 4 shows a view of the implemented MOP

Editor.

Figure 4 : A View of MOP Editor

Figure 4: A view of MOP Editor

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5 EVALUATION OF TOOL AND

ONTOLOGY

Functionality of MOP Editor and appropriateness of

the constructed ontology were evaluated using three

kinds of Web services: message generation service,

synonym retrieval service, and ontology sharing

service. Web service is a software system designed

to support interoperable machine-to-machine

interaction in a distributed and collaborative

environment, equipped with an interface described

in a machine-processable format under the SOAP

protocol.

5.1 Message Generation Service

Message generation is one of the simplest Web

services. The Web client of a message generation

service queries for generation of messages

comprehensive to humans by sending a set of

keywords in direction to the ontology server. The

ontology server uses an adequate template and sends

a reply message to the client. The role of the

ontology is to provide a reference base of

information retrieval to compose grammatically

correct messages that better fit the client queries.

For example, for a given set of keywords such

as: part = controller, device = LNG drain control

valve and event = abnormal closing, the generated

message would be “Because of malfunction of the

controller at LNG drain control valve, abnormal

closing was detected”.

This message is sent back to the Web client. As

shown in Figure 5a and Figure 5b, we confirmed

that our ontology is capable of generating messages

comprehensive to humans for several contextual

situations in the range of computer simulation.

(defmop Context (Root) ())

(defmop Cause-Candidate (Context) ())

(defmop Device-Part-Abnormal-Cause-Candidate (Cause-Candidate)

((device

Device)

(part

Part)

(event

Abnormal-Event)

(phrase ‘(Because of the malfunction of the (part) at

(device), (event) is possible.))))

Generated Message

“Because of the malfunction of the controller at

LNG drain control valve, abnormal closing is possible.”

(slots->mop ‘((device

5.2 Synonym Retrieval Service

Synonym retrieval is a Web service for information

retrieval. The Web client queries for a synonym that

has the same or similar meaning as a given word; for

example, for liquid-oxygen the synonym would be

oxidant, for temperature-sensor the synonym is

thermocouple or thermometer, and so on.

Furthermore, in specialized fields engineers use

different technical terms to indicate identical or

equivalent physical objects, such as

fuel-feeding-system and launching-tower, or even to

differentiate one particular physical hazard from

another more general one, such as fatigue-crack and

crack. Since operational tasks for rocket launching

comprise a large-scale system embracing hundred of

engineers of different divisions and companies, a

Web-searching service for adequate synonyms gains

significant importance during diagnostics.

5.3 Ontology Sharing Service

Use of a fixed source of reference such as EDR or

WordNet makes the built ontology transferable to

other database servers distributed on the network.

Thus, sharing of ontology becomes possible because

I-S-A link and hypernymy/hyponymy or

holonymy/meronymy relationships can be

considered “invariable” during a long period of time.

The Web client of an ontology sharing service

queries for a copy of one part or a whole ontology

stored in the ontology server. A copy of class

hierarchy will be shared with other data servers. In

this work, we have tried a sharing service between

an ontology server and a so-called large-scale

multimedia filing data server. A sharing service is

used for a rapid keyword-based search of

multimedia data (documentation, drawings, logs,

sound, pictures and movies) during operational tasks

and diagnostics at the rocket launch facility.

FCV-5201)

Controller)

Close-Abnormal))

(part

(event

‘(Context) nil)

This input automatically

creates an instance of

Device-Part-Abnormal-

Cause-Candidate.

Then, generate message…

Figure 5 b : Message Generation

Input Data

：Cause-Candidate

：

(defmop Device-Role (Root) ())

(defmop Part (Device-Role) ())

(defmop (Part)

((phrase ‘(controller))))

(defmop Event (Root) ())

(defmop Abnormal-Event (Event) ())

(defmop (Abnormal-Event)

((phrase ‘(abnormal closing))))

(defmop Object (Root) ())

(defmop Device (Object) ())

(defmop Valve (Device) ())

(defmop Flow-Control-Valve (Valve) ())

(definstance (Flow-Control-Valve)

((phrase ‘(LNG drain control valve))))

Fi n

context

device

part

event

Controller

Close-Abnormal

FCV-5201

Controller

Close-Abnormal

FCV-5201

Figure 5b: Message Generation

gure 5 a : Message Generatio

Figure 5a: Message Generation

ONTOLOGY MODELING TOOL USING CONCEPT DICTIONARY AND INFERENCE

229

6 SUMMARY AND FUTURE

WORK

MOP Editor demonstrated ability in constructing

ontology applicable to Web services for message

generation, synonym retrieval, and ontology sharing.

We have confirmed throughout our computer

simulation that the tool can be used for graphically

modeling class hierarchy and semantic links.

Furthermore, we have assured that ontological

models provided by EDR and WordNet work

relatively well for the presented Web services.

Future work is needed to enhance GUI facility

and MOP functionality to incorporate coming

themes such as text extraction from any sources, task

ontology, ontology distinguishing, integration of

different ontologies, and plug-in for database

connectivity.

ACKNOWLEDGEMENT

This work has been supported in part by the Ministry

of Education, Culture, Sports and Technology

(MEXT) of Japan. The authors are grateful to

Professor Dr. R. Mizoguchi and Research Associate

Dr. K. Kozaki of Osaka University for providing

helpful comments on ontology methodology. Many

thanks to the technical group of Franz Incorporated

for valuable advice on Allegro’s IDE-CG. Many

thanks also to all the colleagues of Galaxy Express

Corporation for their enthusiasm and contributions

in developing the project.

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