An Ontology Roadmap for Crowdsourcing Innovation Intermediaries

Cândida Silva

and Isabel Ramos

School of Management and Industrial Studies, Polytechnic Institute of Oporto, Vila do Conde, Portugal

Information Systems Department, School of Engineering, University of Minho, Guimarães, Portugal

Center Algoritmi, University of Minho

Keywords: Ontology Building Methodologies, Crowdsourcing Innovation, Innovation Ontology, Ontology Enterprise.

Abstract: Ontologies have proliferated in the last years, essentially justified by the need of achieving a consensus in

the multiple representations of reality inside computers, and therefore the accomplishment of

interoperability between machines and systems. Ontologies provide an explicit conceptualization that

describes the semantics of the data. Crowdsourcing innovation intermediaries are organizations that mediate

the communication and relationship between companies that aspire to solve some problem or to take

advantage of any business opportunity with a crowd that is prone to give ideas based on their knowledge,

experience and wisdom, taking advantage of web 2.0 tools. Various ontologies have emerged, but at the best

of our knowledge, there isn’t any ontology that represents the entire process of intermediation of

crowdsourcing innovation. In this paper we present an ontology roadmap for developing crowdsourcing

innovation ontology of the intermediation process. Over the years, several authors have proposed some

distinct methodologies, by different proposals of combining practices, activities, languages, according to the

project they were involved in. We start making a literature review on ontology building, and analyse and

compare ontologies that propose the development from scratch with the ones that propose reusing other

ontologies. We also review enterprise and innovation ontologies known in literature. Finally, are presented

the criteria for selecting the methodology and the roadmap for building crowdsourcing innovation

intermediary ontology.

1 INTRODUCTION

Ontologies have proliferated in the last years, mostly

in Computer Science and Information Systems areas.

This is essentially justified by the need of achieving

a consensus in the multiple representations of reality

inside computers, and therefore the accomplishment

of interoperability between machines and systems

(Hepp, 2007).

Open innovation is a timely topic in innovation

management. Its basic premise is open up the

innovation process. The innovation process, in

general sense, may be seen as the process of

designing, developing and commercializing a novel

product or service to improve the value added of a

company.

This paradigm proposes the use of external and

internal ideas, and internal and external paths to

market, as means to reach advances in technology

used by companies (Chesbrough, 2006).

The World Wide Web, the open source

movement and the development of Web 2.0 tools

facilitates this kind of contributions, opening space

to the emergence of crowdsourcing innovation

initiatives.

Jeff Howe and Mark Robinson introduced the

term crowdsourcing, in an article in Wired Magazine

(Howe, 2006), as a way of using the Web 2.0 tools

to generate new ideas through the heterogeneous

knowledge available in the global network of

individuals highly qualified and with easy access to

information and technology. Although, this concept

has been used quite a time, the creation of the

Wikipedia and of many examples of free software,

like Linux, are examples of crowdsourcing activity.

Crowdsourcing is a form of outsourcing not directed

to other companies but to the crowd by means of an

open call mostly through an Internet platform.

Basically, the process is trying to solve a company

problem by an open call in the network. The

company posts a problem and a vast amount of

individuals offers the solution for evaluation. The

winning idea is awarded in some way and the

Silva C. and Ramos I..

An Ontology Roadmap for Crowdsourcing Innovation Intermediaries.

DOI: 10.5220/0005084800540063

In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2014), pages 54-63

ISBN: 978-989-758-050-5

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

company develops the idea. The crowd can be

defined as a large set of anonymous and

heterogeneous individuals, which may be composed

of scientists and experts in various fields, but also of

novices (Howe, 2008; Surowiecki, 2005).

A crowdsourcing innovation intermediary is an

organization that mediates the communication and

relationship between the seekers – companies that

aspire to solve some problem or to take advantage of

any business opportunity – with a crowd that is

prone to give ideas based on their knowledge,

experience and wisdom (Ramos et al., 2009).

For crowdsourcing innovation intermediary the

crowd is composed by groups of specialists in

different areas, such as individual researchers,

research team, labs, post-graduate students and

highly qualified individuals.

This paper makes a literature review on ontology

building, and analyze and compare ontologies that

propose the development from scratch with the ones

that propose reusing other ontologies. It also review

enterprise and innovation ontologies known in

literature. Finally, are presented the criteria for

selecting the methodology and the roadmap for

building crowdsourcing innovation intermediary

ontology.

To achieve this objectives we defined the

following main questions, which guided the

literature review: (i) What are the main concepts

guiding ontologies building?; (ii) What are the

existing ontologies about business and innovation?;

(iii) Which methodologies should be considered to

build an ontology?

To answer these questions, we started conducting

an exhaustive bibliography review of the authors

most relevant to the scientific area, identifying

curriculum authors, books, book chapters, papers

presented at conferences and published articles in

scientific journals. This literature review was

conducted by Scopus, Google Scholar, ISI Web of

Knowledge. The documents were collected through

the UM catalog, b-on; RCAAP, IEEExplore, Colcat.

Then, based on this extensive bibliography, we

proceeded to the identification of the most relevant

papers, gathering all those whose title refers to the

following combination of words: "ontologies",

“ontology development”; “ontology building”;

“innovation ontology”; “enterprise ontology”; and

“ontology methodologies”.

This paper is organized as follows. In section 2,

is made a literature revision of ontology concepts

such as its definition and features, classification of

ontologies by different authors, application areas,

and enterprise and innovation ontologies. Following,

in section 3, we review literature on ontology

methodologies. Finally, the conclusions of this work

are presented and the roadmap for building a

crowdsourcing innovation intermediary ontology.

2 STAT OF ART ON

ONTOLOGIES

There are several definitions of the concept of

ontology from where can be assemble that it has an

informal and formal notion associated to it. Gruber

(1995) definition clearly shows these – “An

ontology is a formal, explicit specification of a

standard conceptualization”.

An ontology is a conceptualization of world view

with respect to a given domain. This world view is

conceived by a framework as a set of concept

definitions and their interrelationships, that may be

implicit, existing only in someone’s head or tool, or

explicit which includes a vocabulary of terms and a

specification of their meanings.

The specification of that world view by means of

a formal and declarative representation, with

semantic interconnections, and some rules of

inference and logic, will perform the formal

ontology. The formal representation will facilitate

the interoperability between heterogeneous

machines and systems.

Ontologies have been developed with the

promise of providing knowledge sharing and reuse

between people and systems, by building a

conceptual framework of a given knowledge domain

to be represented. This framework will be

formalized through a specific ontology language

which will clearly express a controlled vocabulary

and taxonomy, as represented in Figure 1.

Figure 1: Ontology building features.

AnOntologyRoadmapforCrowdsourcingInnovationIntermediaries

The vocabulary is a list of terms or classes of

objects, respective definitions and relationships

between each other, provided by logical statements.

They also specify rules for combining the terms and

their relations to define extensions to the vocabulary.

The taxonomy or concept hierarchy is a

hierarchical classification or categorization of

entities in the domain of an ontology. The taxonomy

should be in a machine-readable and machine-

processable form in order to permit interoperability.

The full specification of an ontology domain

establishes a conceptual framework, composed by

the vocabulary and the taxonomy, for discussion,

analysis, and information retrieval in a domain.

Ontology development requires an effective

ontological analysis of the content the world view

domain that it intends to represent. This analysis will

reveal the terms and concepts of the domain

knowledge, their relations, organization and

hierarchy. Thus, they clarify the structure of domain

knowledge, so, it can be called a content theory

(Gasevic et al., 2006, p.53).

As the objective of ontologies is to facilitate

knowledge sharing and reuse between various

agents, regardless of whether they are human or

machines, then it can be said that ontologies are a

prerequisite and a result of a consensual point of

view on the world. It is a prerequisite for consensus

because to have knowledge sharing agents must

agree on their interpretation of a domain of the

world. And it is a result of consensus because the

model of meanings was built as result of a process of

agreement between agents on a certain model of the

world and its interpretations. Therefore, it is an

essential requirement that any ontology can progress

over the time (Fensel, 2004).

Briefly, an ontology provides an explicit

conceptualization that describes the semantics of the

data. As Fensel (2004) stated “ontology research is

database research for the 21st century where data

need to be shared and not always fit into a simple

table”.

2.1 Type of Ontologies

Over the years, researchers of this body of

knowledge, tried to clarify, classify and typify the

concept of ontology, in terms of its definition,

components, and application areas. Table 1 present a

summary of, what we considered being, the most

relevant contributions.

Analyzing these table and the different views on the

classification of ontologies, we can organize them in

different types by the subject or issue of

conceptualization, and them, each of this type can

have different degrees of formality, purpose or

objective, and components.

Table 1: Ontologies' classification by researcher’s

perspectives.

Author Classification/Dimension

Guarino (1995) Informal conceptual system

Formal semantic account

Representation of a conceptual system

with a logical theory

Vocabulary used by a logical theory

Meta-level specification of a logical

theory

Mizoguchi et

al. (1995)

Content theory:

- Object ontology

- Activity ontology

- Field ontology

Task ontology

General or common-sense ontology

Uschold &

Gruninger

(1996)

Formality

- Informal, semi-formal, formal

Purpose

- Communication between humans

- Inter-operability among systems

- Systems engineering benefits

Subject matter

- Domain ontology

- Task/method/problem solving

ontology

- Representational/meta ontology

van Heijst et

al. (1997)

Amount and structure of the

conceptualization

- Terminological ontology

- Information ontology

- Knowledge modelling ontology

Subject of conceptualization

- Application ontology

- Domain ontology

- Generic ontology

- Representation ontology

Guarino (1998) Domain ontology

Meta-data ontology

General or common-sense ontology

Representational ontology

Method and task ontology

Lassila &

McGuinness

(2001)

Controlled vocabulary

Glossary

Thesaurus

Informal is-a hierarchy

Formal is-a hierarchy

Formal instances

Frames

Value restrictions

General logical constraints

Benjamins &

Gómez-Pérez

(n.d.)

Reusability

- Content ontologies: task, domain,

representation

- Issue of the conceptualization:

application, generic, representation,

domain

KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing

Table 1: Ontologies' classification by researcher’s

perspectives. (cont.)

Author Classification/Dimension

A. Bullinger

(2008)

Subject matter

- Application

- Task

- Domain

- General

- Representation

Formality

- Informal notation

- Semi-informal/semi-formal notation

- Formal notation

Expressiveness

- Taxonomy

- Theasaurus

- Topic map

- Lightweight vs heavyweight

ontology

So it seems some consensus that the types of

ontologies, by subject or content matter are:

• Domain or content ontology – represents the

knowledge valid for a given type of domain

(e.g. enterprise, medical, electronic,

mechanic).

• Meta-data ontology – provide a vocabulary

for describing informational content (e.g.

Dublin core describes on-line information

sources).

• General or common-sense ontology –

provides basic notions and concepts about

describing general knowledge about the

world and so they are valid across several

domains (e.g. time, space, state, event).

• Representational/frame ontology – ontologies

that provide representational entities without

stating what particular domain it represents.

Do not commit to any particular domain.

• Task/method/problem solving ontology –

provide terms specific for particular tasks and

problem-solving methods. It defines

primitives by which the problem solving

context can be described and domain

knowledge can be put into the problem

solving context.

2.2 Application Areas

Fensel (2004), in his book, classifies the main broad

areas where ontologies are of interesting application:

knowledge management, web commerce, electronic

business and enterprise application integration.

Gasevic et al. (2006) identified some high-level

activities where the utilization of ontology

technology applies perfectly, which are tasks that

fall, somehow, in all these application areas. After

all, those are the usual task for having knowledge

share and reusability:

• Collaboration – ontologies provide a unique

consensual knowledge framework that can be

used as a common, shared reference to

communicate and work with.

• Interoperation – ontologies enable

information conversation, transfer and

integration from different and heterogeneous

sources. However, to permit automatic

integration it is needed that all the sources

recognize the same ontology.

• Education – ontologies can be a reliable and

objective source of information to those who

want to learn more about a specific domain,

since it is expected that they result of a wide

consensus of the structure of the knowledge

domain they represent. So, they are also a

good publication medium and source of

reference.

• Modeling – the structure and hierarchy

established in the ontology will represent

important reusable building blocks, which

many specific applications should include as

predeveloped knowledge modules.

• E-commerce – Since ontologies enable

interoperability between machines and

systems, e-commerce can be considered an

application domain for ontologies. They can

be fully used in all the e-commerce tasks.

• Search engines – concepts and taxonomies

from ontologies can be used to support

structures, comparative, and customized

searches.

2.3 Enterprise Ontologies

Enterprise ontologies are usually created to define

and structure knowledge in business universe about

the processes, activities, organization and strategies.

The first enterprise ontology (EO) project was

developed at the University of Edinburg with the

aim of promoting the common understanding

between people across enterprises, as well as to

serve as a communication medium between people

and applications, and between different applications

(Uschold et al., 1998). Its major role is to act as a

communication medium, ensuring effective

interchange of information and knowledge between

different users, tasks and systems.

This implies that besides technical

interoperability it is needed a semantic and

AnOntologyRoadmapforCrowdsourcingInnovationIntermediaries

pragmatic interoperability between applications and

users (Leppänen, 2007).

The main intended uses for EO, identified by

Uschold et al. (1998), were:

• “enhance communication between humans,

for the benefit of integration;

• serve as stable basis for understanding and

specifying the requirements for end-user

applications using the Tool Set which in turn

leads to more flexibility in an organization;

• to achieve interoperability among disparate

tools in an enterprise modeling environment

using the EO as an interchange format.”

To develop the EO, the authors used

brainstorming technique to identify the maximum of

potential terms that are relevant to enterprises. The

list of terms and phrases harvested were then

grouped by similar areas and established priorities to

include the terms in the ontology. The resultant list

of terms was categorized to identify the core and

specific terms of each area and define it. The core

concepts define the Meta-Ontology of EO.

The EO establish the following basic core terms

in a Meta-Ontology: Entity, Relationship, Role,

Attribute, State of Affairs, Achieve, Actor, Actor

Role, and Potential Actor. The specific terms

defined by EO were grouped into five working

areas: Activity, Organization, Strategy, Marketing,

and Time, as presented in Table 2.

First, the EO was defined in an informal way,

establishing its concepts in plain English and later,

in the formalization phase, the terms were encoded

into Ontolingua language. The Ontolingua has

already adequate primitives to cover what was

required to represent Enterprise Meta-Ontology,

namely: objects, relations, and functions. Thus, it

was evaluated the concepts that already are defined

by Ontolingua and imported to EO. The formal

Enterprise Meta-ontology become: Actor, Function,

Set, Thing, Potential Actor, Relation and State of

Affairs.

This ontology has been successfully used as a

mean to achieve inter-operation through a common

terminology used for specifying tasks, capabilities,

and agents; and to enhance communication between

humans by using terms in a consistent way.

Some of the failures of this project were the

difficulty to use formal definitions and to have

automatic interpretations; the lack of an interchange

format to other ontologies; the fact of being too

generic; and missing a graphic context for browsing

the list of terms.

Table 2: List of terms defined by Enterprise Ontology by

working area (Uschold et al., 1998).

Activity Activity Specification, Execute,

Executed Activity Specification, T-

Begin, T-End, Pre-Conditions, Effect,

Doer, Sub-Activity, Authority,

Activity Owner, Event, Plan, Sub-

Plan, Planning, Process Specification,

Capability, Skill, Resource, Resource

Allocation, Resource Substitute.

Organization Person, Machine, Corporation,

Partnership, Partner, Legal Entity,

Organizational Unit, Manage,

Delegate, Management Link, Legal

Ownership, Non-Legal Ownership,

Ownership, Owner, Asset,

Stakeholder, Employment Contract,

Share, Share Holder.

Marketing Purpose, Hold Purpose, Intended

Purpose, Strategic Purpose,

Objective, vision, Mission, Goal,

Help Achieve, Strategy, Strategic

Planning, Strategic Action, Decision,

Assumption, Critical Assumption,

Non-Critical Assumption, Influence

Factor, Critical Influence Factor,

Non-Critical Influence Factor, Critical

Success Factor, Risk.

Strategy Sale, Potential Sale, For Sale, Sale

Offer, Vendor, Actual Customer,

Potential Customer, Customer,

Reseller, Product, Asking Price, Sale

Price, Market, Segmentation

Variable, Market Segment, Market

Research, Brand Image, Feature,

Need, Market Need, Promotion,

Competitor.

Time Time Line, Time Interval, Time

Point.

The TOVE (TOronto Virtual Enterprise) project,

developed in the University of Toronto, came out as

an enterprise ontology that solves the problems

presented above.

TOVE aims to create a generic, reusable

enterprise model for a company. This model must

(1) provide a shared terminology; (2) defines the

meanings of each term in a precise and unambiguous

manner; (3) implements semantics in a set of

axioms, and (4) provide a graphical context for

depicting terms or concepts (Fox & Gruninger,

1998).

TOVE was implemented with two formal

languages: C++ for the static part and Prolog the

axioms. The ontology implementation started with a

generic ontology for enterprises, but additionally, it

has been created more specific ontologies covering

enterprise subareas, like, business and project

KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing

process, organization, logistics, transport ant store,

scheduling, and information resources (Gómez-

Pérez et al. 2004).

2.4 Innovation Ontologies

Ning et al. (2006) presented a system architecture

that combines ontology, inference and mediation

technologies to create a semantic web of innovation

knowledge, which they called Semantic Innovation

Management (SIM). The framework of the system

was based on metadata harvesting and RDF access

technologies.

Bullinger (2008), in her PhD thesis, develop

OntoGate, ontology to manage idea assessment and

selection of the innovation process.

Also Riedl et al. (2009) proposed an ontology to

represent ideas of the innovation management

process. It defines the core idea concept that is

enriched by other concepts like collaborative idea

development, including rating, discussing, tagging,

and grouping ideas.

They classified the ontology as an application

ontology because it provides a description of a

technical architecture to represent complex ideas

evaluations along various concepts. It offers a

common language to idea storage and exchange for

the purpose of achieving interoperability across

innovation tools.

The ontology was built following the

methodology proposed by Noy & McGuinness

(2001), and reused other existing ontologies, as

suggested.

The Table 3 presents the main classes of idea

ontology, and the source of each class. When the

class is new, the source ontology will be Idea

Ontology. It was also reused the Enterprise

Ontology to model the descriptive attributes of an

idea.

Table 3: Idea Ontology terms and related source ontology.

Class Source

CoreIdea Idea Ontology

Document Friend of a Friend (FOAF)

Item Semantically-Interlinked Online

Communities (SIOC)

Resource Resource Description Framework (RDF)

Origin Idea Ontology

Rating Rating Ontology

Person FOAF

Tagging Tag ontology

Concept Simple Knowledge Organization

Ontology (SKOS)

3 ONTOLOGY BUILDING

METHODOLOGIES

Ontology methodologies comprises a set of

established principles, processes, practices, and

activities used to design, formalize, implement,

evaluate, and deploy ontologies, for which uses

some development tools. These development tools

include ontology representation languages, graphical

ontology development environment, and ontology-

learning tools.

To develop an ontology it must be first answered

questions like: what is the scope of the ontology?

Who is interested in it? Who will use and maintain

it? Which methods and methodologies can be used

to build ontologies? Which activities are performed?

Which tools gives support to the ontology

development process? Which ontology language can

be used to implement ontologies? Which

methodology, tool and language should be used to

develop and to implement an ontology for

crowdsourcing innovation intermediaries?

Noy & McGuinness (2001) gave some basic advices

in seven steps for the process building of your first

ontology, and that helps to answer these questions:

1. Determine the domain and scope of the

ontology: This step should help to define the

knowledge domain covered, to limit the scope of

the model, and the users and maintainer of the

ontology.

2. Consider reusing existing ontologies: Before

starting to create an ontology from scratch, it is

worth to check if there exist any ontology that

can be refine or extend that cover our particular

domain or task. Reusing ontologies specially

considered if our system needs to interact with

other applications that have already committed to

particular ontologies or controlled vocabularies,

in order to reduce the translation effort.

3. Enumerate important terms: Ontology

development should start by listing all the terms

we thing important or like to explain to user, and

describe them briefly.

4. Define the classes and their hierarchy: This

step and the following are taken by turns.

5. Define the properties of the classes: Expresses

the internal structure of concepts by explicating

their extrinsic properties (name, duration, and

use), intrinsic properties (weight, colour, etc),

parts, and relations to other classes and

individuals in those classes.

6. Define the characteristics classes’ properties:

Defining things like attributes type, domain and

AnOntologyRoadmapforCrowdsourcingInnovationIntermediaries

range allowed values, cardinality, and other

features.

7. Create instances: Creating individual instance

of classes in the hierarchy, filling in the attributes

values.

Over the years, several authors have proposed

some distinct methodologies, by different proposals

of combining practices, activities, languages, etc,

according to the project they were involved in.

Fernández-Pérez & Gómez-Pérez (2002) and Corcho

et al. (2003) described some of these methodologies

and compared their degree of maturity. They

selected, at the time, the best-known approaches for

both building from scratch and reusing ontologies,

which are summarize in Appendix.

4 CONCLUSIONS

The ontologies are presented as a conceptual model

for the systematization and formalization of

consensual knowledge in a field of knowledge. This

conceptualization is rendered concrete with the

definition of terms and concepts from the domain of

knowledge in analysis, their relationships,

organization and hierarchy, and allows the sharing

and reuse by different people and systems of such

knowledge (Corcho et al., 2003; Corcho et al., 2002;

Fensel et al., 2000; Hepp, 2007; Smirnov et al.,

2005; Tang et al., 2006).

Some of the difficulties of sharing knowledge

and reusing ontologies are (Gasevic et al. 2006): the

existence of several different languages to

representing ontologies, and tools may not support

the language used to develop the ontology; there are

many diverse ontologies that have been developed to

describe the same topic or domain, resulting of using

different competing methodologies and working

groups. To build an ontology by combining some of

them may require a lot of manual adjustments

because of deep differences between them, and the

resulting ontology may still inadequate to fulfill all

the requirements; and difficulties on ontology

maintenance, since all parts of knowledge evolve

over time.

Various ontologies have emerged,

particularly in the areas of business and enterprise.

Ning et al. (2006), Bullinger (2008), and more

recently Riedl et al. (2009b), proposed ontologies for

the process of innovation management, but they

represent only the component relating to the process

of generating ideas. Not the best of our knowledge,

the existence of any ontology that represents the

entire process of creating an intermediate value of

crowdsourcing innovation. Thus, an ontology of

crowdsourcing innovation intermediaries will be an

instrument to understand this phenomenon and thus

will also be a facilitator for the emergence of such

intermediaries.

The roadmap for building this ontology

comprises two main phases: in the first phase is

being conducted an empirical study with innovation

intermediaries that rely on innovation and

crowdsourcing to develop some of their tasks or

solve problems. The result of this study will be a

model of knowledge for innovation intermediaries

with crowdsourcing. This model of knowledge will

be the basis for the ontology development. The

second stage will involve the development of the

ontology itself. First will be developed a domain or

content ontology that represent the entire taxonomy

of concepts and their hierarchy of the underlying

knowledge model of an innovation intermediary

with crowdsourcing. After it will be developed a

meta-data ontology to provide a descriptive

vocabulary of this knowledge area. With these two

artefacts we intend to contribute to the

standardization of concepts in this area of

knowledge and to enhance the emergence of such

intermediaries.

The ontology development project will be

performed using the NeOn methodology (cf. table in

appendix), as this is a very complete methodology,

which provides guidance in all phases of project

development, allowing the use of ontological and

not ontological resources, collaborative

development, and evolution and maintenance of the

ontology network. Also, the use of the Web

Ontology Language, which is a suitable language for

developing web ontologies, widely used, and also it

provides a tool to support the development of

ontology, but without being mandatory the use of

this tool.

ACKNOWLEDGEMENTS

This work is funded by FEDER funds through the

Operational Program for Competitiveness Factors -

COMPETE and National Funds through FCT -

Foundation for Science and Technology under the

Project: FCOMP-01-0124-FEDER-022674.

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APPENDIX

Table 4: Ontology building methodologies.

Aim Method Phases Activities Language Tool

Build

coope-

ration

Cyc KB -

Knowledge

Base

(Lenat &

Guha, 1990)

Capture a large

portion of what

people normally

considered

consensus

knowledge about

the world

From scratch:

bottom-up

1. Manual extraction of common

sense knowledge; 2.

Codification: Computer aided

extraction of common sense

language; 3. Computer managed

extraction of common sense

knowledge

Implementation;

Knowledge

acquisition;

Documentation

CycL, an augmentation

of first-order predicate

calculus, with extensions

to handle equality,

reasoning, skolemisation,

and some second-order

features

Uschold and

King

(Uschold &

King, 1995)

Enterprise

modeling

processes

From scratch:

middle-out

1. Identify purpose; 2. Building:

Capture; Coding; Integrating

3. Evaluation; 4. Documentation

Requirements;

Implementation;

Knowledge

acquisition;

Verification and

validation;

Documentation

Ontolingua

Onto-

lingua

Server

Grüninger and

Fox

(Grüninger &

Fox, 1995)

Business

processes and

activities

modeling; support

design-in-large

scale projects

From scratch

1. Capture of motivating

scenarios; 2. Formulation of

informal competency questions;

3. Specification of the

terminology of the ontology

within a formal language; 4.

Formulation of formal

competency questions using the

terminology of the ontology; 5.

Specification of axioms and

definitions for the terms in the

ontology within the formal

language; 6. Establish conditions

for characterizing the

completeness of the ontology

Requirements;

Design;

Implementation;

Knowledge

acquisition;

Verification and

validation;

Documentation

KIF (Knowledge

Interchange Format),

first-order logic

KACTUS

(Bernaras,

Laresgoiti, &

Correa, 1996)

Complex

technical systems

development

From scratch:

up-down;

modifying

other existing

ontologies of

application

development

1. Specification of the

application; 2. Preliminary

design based on relevant top-

level ontological categories; 3.

Ontology refinement and

structuring

Requirements;

Design;

Implementation;

Maintenance

CML (Chemical Markup

Language); Express;

Ontolingua

KACT

toolkit

METHONTO

LOGY

(Fernández-

López,

Gómez-Pérez,

& Juristo,

1997)

Support

application

development

process

Re-engineering

1. Project management activities

(Schedule; Control; Quality

assurance); 2. Development-

oriented activities (Specification;

Conceptualization;

Formalization; Implementation;

Maintenance); 3. Support

activities (Knowledge

acquisition; Integration;

Evaluation; Documentation;

Configuration management)

Project monitoring

and control;

Requirements;

Design;

Implementation;

Maintenance;

Knowledge

acquisition;

Verification and

validation;

Ontology

configuration

management;

Documentation

OWL,DAML+OIL;

RDF; XML; OCML

ODE

(Ontolo

Design

Environ

ment)

and

WEB-

ODE

KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing

Table 4: Ontology building methodologies. (cont.)

Aim Method Phases Activities Language Tool

Build

coope-

ration

On-To-

Knowledge

(Fensel, Van

Harmelen,

Klein, &

Akkermans,

2000)

Knowledge

management of

heterogeneous

sources in the

internet

From scratch

1. Kick-off: requirements capture

and specification; 2. Refinement;

3. Evaluation; 4. Maintenance

Project initiation;

Project monitoring

and control;

Ontology quality

management;

Concept

exploration;

Requirements;

Design;

Implementation;

Maintenance;

Knowledge

acquisition;

Verification and

validation;

Ontology

configuration

management;

Documentation

OIL (Ontology-based

Inference Layer); XML;

RDF

OntoEd

SENSUS

(Swartout et

al., 1997)

Natural language

processing for

developing

machine

translators

From scratch:

up-down

1. Identify seed terms; 2. Link

seed terms to SENSUS by hand;

3. Include nodes on the path to

root; 4. Add some complete sub-

trees

Requirements;

Implementation

DL: LOOM

Ontosa

urus

(KA)

(Knowledge

Annotation

Initiative of the

Knowledge

Acquisition

Community)

To model the

knowledge

acquisition

community using

ontologies

developed in a

joint effort of

people at different

locations using

the same

templates and

language, by

annotating WWW

documents, in a

distributive

ontological

engineering

process

From scratch Implementation Implementation Frame Logic

Ontobr

oker

Yes

NeOn

(Gómez-Pérez

& Suárez-

Figueroa,

2008)

It provides

guidance for all

key aspects of the

ontology

engineering

process, that is,

collaborative

ontology

development,

reuse of

ontological and

non-ontological

resources, and the

evolution and

maintenance of

networked

ontologies,

through nine

scenarios.

Re-engineering

1. Initiation (Requirements

specification; Scheduling;

Evaluation); 2. Reuse (Non-

Ontological Resource (NOR)

Reuse; Search; Reuse;

Statements Reuse; Evaluation);

3. Merging (Aligning;

Evaluation); 4. Re-engineering

(NOR Reengineering;

Modularization; Evaluation); 5.

Design (Conceptualization;

Evolution; Localization;

Evaluation); 6. Implementation

(Evaluation; Maintenance;

Evaluation)

Project monitoring

and control

Requirements

Design

Implementation

Maintenance

Knowledge

acquisition

Verification and

validation

Ontology

configuration

management

Documentation

OWL (Web Ontology

Language)

NTK –

NeOn

Toolkit

Yes

AnOntologyRoadmapforCrowdsourcingInnovationIntermediaries