An Ontology Roadmap for Crowdsourcing Innovation Intermediaries
Cândida Silva
1
and Isabel Ramos
2
1
School of Management and Industrial Studies, Polytechnic Institute of Oporto, Vila do Conde, Portugal
2
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
54
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
Copyright
c
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.
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55
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
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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
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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
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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
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59
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
No
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
No
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
No
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
US
toolkit
No
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
gy
Design
Environ
ment)
and
WEB-
ODE
No
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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
it
No
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
No
(KA)
2
(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
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