Multi-aspect Ontology for Interoperability in

Human-machine Collective Intelligence Systems for Decision Support

Alexander Smirnov

, Tatiana Levashova

, Nikolay Shilov

and Andrew Ponomarev

St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences,

39, 14

line, St. Petersburg, 199178, Russia

Keywords: Human-machine Collective Intelligence, Semantic Interoperability, Multi-aspect Ontology, Decision

Support.

Abstract: A collective intelligence system could significantly help to improve decision making. Its advantage is that

often collective decisions can be more efficient than individual ones. The paper considers the human-machine

collective intelligence as shared intelligence, which is a product of the collaboration between humans and

software services, their joint efforts and conformed decisions. Usually, multiple collaborators do not share a

common view on the domain or problem they are working on. The paper assumes usage of multi-aspect

ontologies to overcome the problem of different views thus enabling humans and intelligent software services

to self-organize into a collaborative community for decision support. A methodology for development of the

above multi-aspect ontologies is proposed. The major ideas behind the approach are demonstrated by an

example from the smart city domain.

1 INTRODUCTION

Collective intelligence is an emergent property from

the synergies among data-information-knowledge,

software-hardware, and humans with insight that

continually learns from feedback to produce just-in-

time knowledge for better decisions than any of these

elements acting alone. A collective intelligence

system could help organize all these elements to

improve decision making (Glenn 2013). The

Decision 2.0 framework shifting to collective

decisions in the era of Web 2.0, postulates three

general types of approach to accomplish the decision

making objectives. They are outreach, additive

aggregation, and self-organization. The former two

types suppose involvement of various sources

providing ideas and information. The latter type, self-

organization, is mechanisms that enable interactions

among community members, which can result in the

whole being more than the sum of its parts (Bonabeau

2009). That is, self-organization is the mechanism

that can help to achieve the main goal of collective

https://orcid.org/0000-0001-8364-073X

https://orcid.org/0000-0002-9380-5064

https://orcid.org/0000-0002-9264-9127

https://orcid.org/0000-0002-1962-7044

intelligence, that is to provide more knowledge than

any individual element provides.

In truly intelligent decision making systems the

elements above are interoperable only with a shared

understanding of the task, the context, and each

other’s perspectives and capabilities (van den Bosch

and Bronkhorst 2018). There are four levels of

interoperability (European Commission 2017):

technical, semantic, organizational and legislative.

Semantic interoperability is understood as shared

semantic interpretation of knowledge presented using

meta-models. The problem of shared knowledge

faces many obstacles in human-machine

environments. Namely, different meanings for terms

(Gruber 2008), diverse data formats, diverse

ontologies reflecting different contexts and area of

practice, diverse classification systems, diverse

folksonomies emerging from social tagging in

various social media (Halpin, Robu and Shepherd

2007), and multiple natural languages (Lévy 2010).

All these obstacles exist when heterogeneous teams

are aiming at providing collective intelligence.

458

Smirnov, A., Levashova, T., Shilov, N. and Ponomarev, A.

Multi-aspect Ontology for Interoperability in Human-machine Collective Intelligence Systems for Decision Support.

DOI: 10.5220/0008356304580465

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 458-465

ISBN: 978-989-758-382-7

In 2008, T. Gruber addressed the issue of

collective intelligence in the Web, where humans and

machines contribute actively to the resulting

intelligence, each doing what they do best (Gruber

2008). The Semantic Web was supposed the

technology enabling to provide interoperability

between humans and machines by utilizing

ontologies.

Most of the research on the human-machines

activities use multiple ontologies as a mechanism

enabling interoperability. Each ontology is a domain

representation reflecting specifics of a particular

problem this ontology was built for. Difficulties lie in

the necessity to operate not only with different

terminologies but also with different formalisms used

to describe different views. The terminologies and

formalisms, in turn, depend on the tools used for

efficient solving domains’ tasks. The problem of

heterogeneity of these ontologies can be addressed

through having multiple aspects within a common

multi-aspect ontology. The multi-aspect ontology is

defined as an ontology that specifies different

interrelated aspects (facets, constituents,

perspectives) of a complex problem domain. The

multi-aspect ontology provides for the common

vocabulary enabling the interoperability between

different decision-making processes and ontologies

supporting these, and it makes it possible to preserve

internal notations and formalisms suitable for

efficient support of these processes.

This paper addresses the problem of semantic

interoperability support in human-machine collective

intelligence systems through application of multi-

aspect ontologies. The main research contribution is

a methodology for the above ontology development.

The paper is structured as follows. Section 2 presents

a review of related research. The developed

methodology is described in Section 3. It is followed

by the example of the ontology developed for the

smart city domain. Main results are summarized in

the Conclusion.

2 STATE-OF-THE-ART

The Section outlines various approaches to develop

ontologies representing different knowledge

perspectives. The most suitable ones are considered

in detail and analysed.

Ontologies support the formalization of rational

and intuitive decision behaviour in the Pi-Mind

technology (Terziyan, Gryshko and Golovianko

2018). This technology offers a compromise between

human-driven decision-making and machine-driven

decision-making with regard to Industry 4.0. Pi-Mind

captures the best humans’ decision models and

embeds them into decision making processes of

machines. As a result, the machines become able to

make decisions without any human accompany in

situations that are similar to the situations for which

humans’ decision models have been captured. The

technology relies upon three kinds of ontologies:

upper ontology providing basic means for describing

decisions and decision-making; Pi-Mind specific

ontology, describing a value based model of decision-

making; and domain ontologies describing the

structure of decision scenarios for specific domains.

In the automating design domain where

intelligent human–machine interaction is supposed,

different approaches aiming at modelling the

automatic design knowledge represent different

aspects of design in their ontologies. Examples of

such aspects are process, function, physical product

and issue (Ahmed, Kim and Wallace 2005);

requirement ontology, product finish ontology and

machine motion ontology (Darlington and Culley

2008). The most recent approach (Yin et al. 2015)

distinguishes two aspects: the design ontology to

describe the product and the design process, and the

resource ontology to provide an integrated

representation of human and computer knowledge for

automating design.

The authors of a model-driven interoperability

framework for technical support of co-evolution

strategy of products and manufacturing systems

(Lafleur et al. 2016) address the interoperability

problem by connecting ontologies through

establishing “connector framework” matching these.

This framework connects ontology subclasses

representing product modules, manufacturing

alternatives, and operations. Interoperability between

the product life management tool and the production

capability tools is supported by the ontologies, that

are queried for assessment of the plant capabilities.

Ontology matching (Smirnov and Shilov 2013)

seems to be one of the solutions to the interoperability

problem. But in reality, automatic ontology matching

is still not reliable enough while manual ontology

matching takes too much efforts and time.

Two main and most promising approaches can be

distinguished among the studies on multiple domain

representations using ontologies. They are

multilingual ontology (Espinoza, Montiel-Ponsoda

and Gómez-Pérez 2009) and granular ontologies

(Calegari and Ciucci 2010).

The goal of multilingual ontologies is to resolve

terminological issues that arise due to usage of

different natural languages. Such ontologies are built

Multi-aspect Ontology for Interoperability in Human-machine Collective Intelligence Systems for Decision Support

459

as an ontology comprising language-specific

fragments with relationships between terms.

However, a multilingual ontology is formulated in a

single formalism and collecting together, for

example, knowledge about motivation strategies and

about structure of the problem under consideration

would not be possible without losing certain

semantics.

Granular ontologies rely on the integration of

ontology-based knowledge representation with the

concept of granular computing. Granular computing

is around the notion of granule that links together

similar regarding to a chosen criteria objects or

entities. However, different decision support

processes often overlap in terms of used information

and knowledge. This means that there exist multiple

processes that assume collaboration and usage of the

same information and knowledge. Granular

ontologies cannot solve the problem of terms having

different meaning in different processes.

3 MULTI-ASPECT ONTOLOGY

BUILDING

An analysis (Fernández-López and Gómez-Pérez

2002) of various ontology development

methodologies allows ones to distinguish 5 general

steps in this process: 1) identification of the purpose

and scope of the ontology; 2) identification of

concepts and relationships, and terms to name these

concepts and relationships; 3) ontology engineering;

4) ontology verification; 5) ontology validation.

These steps serve as the guide to develop the multi-

aspect ontology for interoperability support in

human-machine collective intelligence systems.

The multi-aspect ontology is proposed to

comprise three levels: local, aspect, and global. The

local level represents concepts and relationships

observed only from one view. Each aspect can be

represented by specific formalism. The aspect level

represents concepts and relationships from local level

that are shared by two or more aspects. This level

provides a uniform shared ontology representation.

The global level is the common part of the multi-

aspect ontology represented using the representation

provided by the aspect level. The concepts

represented at this level are associated with those in

the aspects.

Development of the multi-aspect ontology

follows the proposed here methodology. At first, the

purpose and scope of the ontology are identified.

Then, the aspects of the ontology are defined based

on the information acquired at the first step and its

logical continuation. Next, ontologies for each of the

aspects are developed. These aspects are integrated

and “global level” is formed out of the concepts that

are considered to be common for the most of aspects.

The steps of verification and validation finalize the

ontology development.

3.1 Identification of the Purpose and

Scope

The purpose of the ontology is determined by the

research problem, i.e., support of interoperability in

human-machine collective intelligence systems

intended for decision support.

The ontology scope is identified based on the

information requirements specified with regard to the

ontology purpose. They include requirements

common for both humans and machines and

requirements having special importance for humans.

Common requirements for interoperability:

 Motivation to participate in decision support.

Motivation is a precondition of success of the

collaboration. Moreover, the motivation

influences decision-making process.

 Clarity of the problem. The decision support

problem must be clearly represented. The

representation must give to the community

members clear understanding of what they are

expected to do in the current situation (to provide

information, to choose an alternative, to perform

some computations, to do some activities, etc.)

As well, the information based on that decisions

are made must be understandable for the

members. That is, data, alternatives, constraints,

preferences, etc. must be explicitly represented.

 Competences accounting. The competences of

the community members must be taken into

account to ensure appropriate decisions.

 Negotiation patterns. In complex systems with

heterogeneous members, negotiation patterns

facilitate information/knowledge exchange and

especially useful to organize such exchange

between humans and machines.

Requirements important for humans:

 Representations for the problem and associated

information must be human-readable.

 Machines are expected to provide support for

complex (e.g., computational) tasks. They are

supposed to self-organize for human support.

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3.2 Aspects Definition

The ontology scope is the source for aspects

definition. The set of questions has been formulated

to distinguish these aspects:

 Which subproblems of the self-organization of a

community providing collective intelligence are

to be solved with the help of the ontology being

developed?

 Which of the subproblems can be solved

separately, and which are inseparable?

 Which formalisms are usually used for solving

identified subproblems?

As a result, identified subproblems form aspects

of the multi-aspect ontology, with inseparable ones

being integrated in one aspect, and others (especially

those, that use different knowledge models) into

separate ones.

The present research distinguishes two types of

aspects in the multi-aspect ontology supporting

interoperability in human-machine collective

intelligence systems intended for decision support.

They are basic and specific. The basic aspects are

usually task-independent. They represent concepts

and relationships needed to organize a community

supporting decisions in any domain. The specific

aspects are always task-dependent and make the

community task-oriented.

The set of basic aspects comprises Motivation,

Problem, Competence, and Negotiation protocol

(Figure 1).

Motivation is the reason for participation in the

decision support activity.

Competence is a quality made up of skill and

knowledge needed to successfully complete a task.

Negotiation protocol is a set of rules for

communication of negotiating parties towards

achievement of a desired final outcome.

Figure 1: Ontology aspects.

Problem is the decision support problem to be

solved in the current context. The corresponding

concept is used to represent conventional decision

support problems (situation awareness, problem

identification, development of alternatives, choice of

a preferred alternative, and decision implementation)

and the problem of community self-organization.

Besides, this concept include domain-specific tasks,

i.e. the user tasks for which the community provides

support.

The category of specific aspects is represented by

two concepts Input/Output and Task. The concept

Task represents the user task and the tasks related to

it. For instance, this concept is used for representation

of subtasks when the user task is decomposed.

Input/Output is intended to represent data and

information used at different stages of a decision

support process (context, alternatives, criteria,

preferences, constraints, etc.).

3.3 Development of Aspect Ontologies

At this step ontologies for each of the aspect are

developed. This can be done based on any ontology

development methodology since the aspects are

generally independent (i.e., they can be implemented

using different formalisms and representation

languages). Obviously, the ontology reuse is

beneficial for more or less typical subproblems that

have already been paid significant attention form the

research community (e.g., negotiation protocol

ontology); however, development of ontologies from

scratch is also possible if no appropriate existing

ontologies are found. Aspect ontologies are proposed

to be reused and further developed. Although, here,

the issue of development of these ontologies is not

considered, some thoughts which ontologies can be

reused to form the aspects described in the previous

Section are provided for.

Results obtained in the research on modelling the

motivation domain in Enterprise Architecture

(Azevedo et al. 2011) and on development of

ontologies to represent human emotional, cognitive,

and motivational processes (López-Gil, Gil and

García 2016) can give some ideas of what concepts

and relationships can be used to represent Motivation.

Sources for an ontology to model Competences

can be found in the competence management domain.

Examples of such sources are the ontology for skill

and competence management (Fazel-Zarandi and Fox

2012), the competence model (Miranda et al. 2017),

the competence ontology (Brandmeier et al. 2017),

etc.

Basic

Specific

Aspect

Motivation

Negotiation

protocol

Competence

Problem

Input/Output

Task

is-a

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461

There are several efforts on development of

ontologies supporting negotiations. An ontology for

automated negotiation in open environments (Tamma

et al. 2002) and its future application to e-commerce

(Tamma et al. 2005) provides different aspects of

negotiation protocol. The negotiation ontology

(Wang, Wong and Wang 2013) supports an ontology

based approach to organize the multi-agent assisted

supply chain negotiations. The mentioned efforts as

well as some others can be used to model the concept

of negotiation protocol.

The concepts of Task and Input/Output are

domain specific and out of the research scope.

3.4 Aspect Integration

At this step, the aspects are analysed with regard to

common concepts that need to be identified and often

taken to the common part of the multi-aspect

ontology. It is useful to write down a list of all such

concepts and then to form a “global level” out of

these. After that, these terms are associated with those

in the aspects. Besides, horizontal relationships

should also be defined at this step for concepts that

are common for two or more aspects, but which are

not high-level enough to be taken into the global

level. A common formalism to represent the common

concepts and the horizontal relationships is defined.

This step is partially described in Section 4.

3.5 Verification

The goal of this step is to ensure the internal

consistency of the developed global level as well as

internal consistencies of the separate aspects taking

into account their relations to other aspects. The step

of ontology verification involves special techniques

and is out of the paper scope.

3.6 Validation

Validation usually takes place during the usage of the

developed multi-aspect ontology in a real-life or

modeled environment. The accumulated issues are

collected, analyzed, and the corresponding

modifications are introduced into the ontology.

Currently, this step is going on and its results will be

available upon completion of this activity.

4 CASE STUDY

New information technologies enable various new

possibilities enhancing our lives. One of products of

this development is appearance of the notion of

“smart city” (Dustdar, Nastić and Šćekić 2017). There

is no common definition of this notion, however, its

common understanding is a coherent urban

development methodology heavily relying on

information and communication technologies to

gather necessary input and provide information for

decision making. Intelligent decision support

collecting information related to the current situation

analysis and assisting in solving various typical

problems becomes essential since otherwise, one can

sink in the ocean of the available today information

and problems to be solved (Anagnostopoulos et al.

2007; Gallacher et al. 2014). Thus, the ontology

purpose is defined as support of interoperability in

human-machine collective intelligence systems

intended for decision support in the smart city

domain.

The scope and aspects do not depend on a

particular domain and are as described in Section 3.

Several representation formalisms for multi-

aspect ontologies have been analysed. The most

progress in this direction is achieved by M. Hemam

who in co-authorship with Z. Boufaïda proposed in

2011 a language for description of multi-viewpoint

ontologies – MVP-OWL (Hemam and Boufaïda

2011) extended in 2018 with probability support

(Hemam 2018). In accordance with this notation, the

OWL-DL language was extended in the following

way (only some of the extensions are listed here; for

the complete reference, please, see (Hemam and

Boufaïda 2011)). First, the viewpoints were

introduced (in the current research they correspond to

ontology aspects). Classes and properties were split

into global (observed from two or several viewpoints)

and local (observed only from one viewpoint).

Individuals could only be local. However, taking into

account the possibility of multi-instantiation, they

could be described in several viewpoints and at the

global level simultaneously. Also, four types of

bridge rules were introduced that enable links or

“communication channels” between viewpoints (only

the bidirectional inclusion bridge rule stating that two

concepts under different viewpoints are equal is used

in the example below, indicated with the symbol ↔̅

The presented below ontology is based on

integration of several existing ontologies. Due to the

space restrictions, only three aspects are considered

to illustrate the developed multi-aspect ontology

(Figure 2): Competences, Negotiation Protocol, User

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462

Figure 2: Multi-aspect ontology for three viewpoints.

Task. The three aspects are aimed at different tasks

and, as a result, they use different formalisms (below,

these are described with the most illustrative

concepts).

The task considered in the Negotiation Protocol

aspect is providing agents with ability to

communicate and reach the desired result. Inference

rules are defined on top of the negotiation ontology to

guide agents’ reasoning ability. The negotiation

protocol aspect makes agents’ negotiation behaviours

more adaptive to various negotiation environments

utilizing corresponding negotiation knowledge, that

does not need to be hard-coded in agents, but it is

represented by an ontology (Wang, Wong and Wang

2013), (Tamma et al. 2005). The formalism used in

this aspect is OWL, and the example classes are

Community Member (agent (representing software

service) or human participant of the community

providing for collective intelligence; subject of the

negotiation process), Human (subclass of Community

Member), Agent (subclass of Community Member),

Strategy (outlines the overall coordination method

governing the set of negotiation interactions), Utility

Function (specifies the method to evaluate a proposal

comprising multiple negotiation issues), Parameter

(various variables affecting the negotiation process)

and Role (played by the community members

involved in a negotiation process).

The User Task aspect (category Task in Section

3) is aimed at definition of the user tasks in the

considered domain (in the given case study the

domain is the smart city user information support),

their interdependencies and subtasks, as well as

functional dependencies between their parameters.

Multi-aspect Ontology for Interoperability in Human-machine Collective Intelligence Systems for Decision Support

463

The formalism of object-oriented constraint networks

makes it possible to define functional dependencies

(represented by constraints) between different

parameters of the smart city environment then process

these via a constraint solver when a particular situation

takes place. As a result, the internal representation

basically consists of entities, their parameters and

constraints defined between them. However, for the

interoperability reasons, the following connecting

classes are defined at the aspect level: Entity, Social

(subclass of Entity), Physical (subclass of Entity),

Cyber (subclass of Entity), Parameter, Domain,

subclasses of the Domain class (e.g., Healthcare,

Education, etc.), Rule.

The third example aspect is Competence where

competences of the members of the human-machine

community. The competences are organized into a

hierarchy for facilitating tasks of matching between

competences and tasks to be solved. The following

classes are considered in this aspect: Community

member, Competence, Domain, Competence Level,

Competence Statement (a more detailed description of

this ontology can be found in (Brandmeier et al. 2017)).

In this aspect, an OWL ontology is used.

In accordance with (Hemam and Boufaïda 2011)

the following ontology elements have been defined:

Aspects: Competence, Negotiation Protocol, User

Task.

Global classes: Thing, Parameter, Community

Member, Role, Domain.

Local Classes:

Negotiation Protocol: Human, Agent, Strategy, Utility

Function

User Task: Entity, Social, Physical, Cyber, Rule,

Healthcare, Education, etc.

Competences: Competence, Competence Level,

Competence Statement

Bridge Rules are presented in Figure 3.

When the aspects and bridge rules are defined, one

can use any required formalism inside each of the

aspects. Besides, the existing models can be integrated

into such a multi-aspect ontology without significant

modification.

5 CONCLUSIONS

The paper suggests a methodology for building multi-

aspect ontologies for interoperability support in a

collective intelligence community aimed for decision

support. The suggested methodology consists of six

steps: interoperability requirements definition, aspect

definition, development of aspect ontologies, aspect

integration, verification, and validation.

Parameter ↔

Parameter

NegotiationProtocol

Parameter ↔

Parameter

UserTask

Parameter ↔

CompetenceLevel

Competences

CommunityMember ↔

CommunityMember

NegotiationProtocol

CommunityMember ↔

Entity

UserTask

CommunityMember ↔

CommunityMember

Competences

Role ↔

Role

NegotiationProtocol

Role ↔

Role

UserTask

Domain ↔

Domain

UserTask

Domain ↔

Domain

Competences

i.e., the Roles from different aspects are the same

roles, and Entity from the User Task aspect is

Community Member from the Negotiation

Protocol aspect.

Figure 3: Bridge Rules.

At the current stage of the research, the developed

methodology has proved its eligibility to building

multi-aspect ontologies supporting interoperability in

collective intelligence communities. However, the

“validation” step is currently going on and its results

will be available upon completion of this activity. After

that an analysis of the strong points and weaknesses of

the developed methodology and multi-aspect ontology

for interoperability support in a collective intelligence

community will be performed.

ACKNOWLEDGEMENTS

The research is funded by the Russian Science

Foundation (project # 19-11-00126).

REFERENCES

Ahmed, S., Kim, S. and Wallace, K.M., 2005. A

Methodology for Creating Ontologies for Engineering

Design. In: Volume 3: 25th Computers and Information

in Engineering Conference, Parts A and B. 2005 ASME.

pp. 739–750. DOI: https://doi.org/10.1115/DETC2005-

84729.

Anagnostopoulos, A., Broder, A.Z., Gabrilovich, E.,

Josifovski, V. and Riedel, L., 2007. Just-in-time

contextual advertising. In: Proceedings of the sixteenth

ACM conference on Conference on information and

knowledge management - CIKM ’07. 2007 New York,

New York, USA: ACM Press. p. 331. DOI:

https://doi.org/10.1145/1321440.1321488.

Azevedo, C.L.B., Almeida, J.P.A., van Sinderen, M.,

Quartel, D. and Guizzardi, G., 2011. An Ontology-Based

Semantics for the Motivation Extension to ArchiMate.

In: 2011 IEEE 15th International Enterprise Distributed

Object Computing Conference. August 2011 IEEE. pp.

25–34. DOI: https://doi.org/10.1109/EDOC.2011.29.

KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development

464

Bonabeau, E., 2009. Decisions 2.0: The power of collective

intelligence, MIT Sloan Management Review, 50(2): 44–

53.

van den Bosch, K. and Bronkhorst, A., 2018. Human-AI

cooperation to benefit military decision making. In:

Proceedings of Specialist Meeting Big Data & Artificial

Intelligence for Military Decision Making. 2018 p. DOI:

https://doi.org/10.14339/STO-MP-IST-160.

Brandmeier, M., Neubert, C., Brossog, M. and Franke, J.,

2017. Development of an ontology-based competence

management system. In: 2017 IEEE 15th International

Conference on Industrial Informatics (INDIN). July 2017

IEEE. pp. 601–608. DOI: https://doi.org/10.1109/

INDIN.2017.8104840.

Calegari, S. and Ciucci, D., 2010. Granular computing

applied to ontologies, International Journal of

Approximate Reasoning, 51(4): 391–409. DOI:

https://doi.org/10.1016/j.ijar.2009.11.006.

Darlington, M.J. and Culley, S.J., 2008. Investigating

ontology development for engineering design support,

Advanced Engineering Informatics, 22(1): 112–134.

DOI: https://doi.org/10.1016/j.aei.2007.04.001.

Dustdar, S., Nastić, S. and Šćekić, O., 2017. Smart Cities.

Cham: Springer International Publishing. DOI:

https://doi.org/10.1007/978-3-319-60030-7.

Espinoza, M., Montiel-Ponsoda, E. and Gómez-Pérez, A.,

2009. Ontology localization. In: Proceedings of the fifth

international conference on Knowledge capture - K-CAP

’09. 2009 New York, New York, USA: ACM Press. pp.

33–40. DOI: https://doi.org/10.1145/1597735.1597742.

European Commission, 2017. New European

Interoperability Framework: Promoting seamless

services and data flows for European public

administrations. 2017.

Fazel-Zarandi, M. and Fox, M., 2012. An ontology for skill

and competency management. In: Formal Ontology in

Information Systems. Frontiers in Artificial Intelligence

and Applications. IOS Press. p.

Fernández-López, M. and Gómez-Pérez, A., 2002. Overview

and analysis of methodologies for building ontologies,

The Knowledge Engineering Review, 17(2): 129–156.

DOI: https://doi.org/10.1017/S0269888902000462.

Gallacher, S., Papadopoulou, E., Abu-Shaaban, Y., Taylor,

N.K. and Williams, M.H., 2014. Dynamic context-aware

personalisation in a pervasive environment, Pervasive

and Mobile Computing, 10: 120–137. DOI:

https://doi.org/10.1016/j.pmcj.2012.11.002.

Glenn, J.C., 2013. Collective Intelligence and an Application

by The Millennium Project, World Futures Review, 5(3):

235–243. DOI: https://doi.org/10.1177/19467567

13497331.

Gruber, T., 2008. Collective knowledge systems: Where the

Social Web meets the Semantic Web, Journal of Web

Semantics, 6(1): 4–13. DOI: https://doi.org/10.1016/j.

websem.2007.11.011.

Halpin, H., Robu, V. and Shepherd, H., 2007. The complex

dynamics of collaborative tagging. In: Proceedings of the

16th International WWW Conference. 2007 p.

Hemam, M., 2018. An Extension of the Ontology Web

Language with Multi-Viewpoints and Probabilistic

Reasoning, International Journal of Advanced

Intelligence Paradigms, 10(1): 1. DOI: https://doi.org/

10.1504/IJAIP.2018.10003857.

Hemam, M. and Boufaïda, Z., 2011. MVP-OWL: a multi-

viewpoints ontology language for the Semantic Web,

International Journal of Reasoning-based Intelligent

Systems, 3(3/4): 147. DOI: https://doi.org/10.1504/

IJRIS.2011.043539.

Lafleur, M., Terkaj, W., Belkadi, F., Urgo, M., Bernard, A.

and Colledani, M., 2016. An Onto-Based Interoperability

Framework for the Connection of PLM and Production

Capability Tools. In: PLM 2016: Product Lifecycle

Management for Digital Transformation of Industries.

2016 Springer. pp. 134–145. DOI: https://doi.org/

10.1007/978-3-319-54660-5_13.

Lévy, P., 2010. From social computing to reflexive collective

intelligence: The IEML research program, Information

Sciences, 180(1): 71–94. DOI: https://doi.org/

10.1016/j.ins.2009.08.001.

López-Gil, J.-M., Gil, R. and García, R., 2016. Web

Ontologies to Categorialy Structure Reality:

Representations of Human Emotional, Cognitive, and

Motivational Processes, Frontiers in Psychology, 7: .

DOI: https://doi.org/10.3389/fpsyg.2016.00551.

Miranda, S., Orciuoli, F., Loia, V. and Sampson, D., 2017.

An ontology-based model for competence management,

Data & Knowledge Engineering, 107: 51–66. DOI:

https://doi.org/10.1016/j.datak.2016.12.001.

Smirnov, A. and Shilov, N., 2013. Ontology matching in

collaborative recommendation system for PLM,

International Journal of Product Lifecycle Management,

6(4): 322–338. DOI: https://doi.org/10.1504/

IJPLM.2013.063210.

Tamma, V., Phelps, S., Dickinson, I. and Wooldridge, M.,

2005. Ontologies for supporting negotiation in e-

commerce, Engineering Applications of Artificial

Intelligence, 18(2): 223–236. DOI: https://doi.org/

10.1016/j.engappai.2004.11.011.

Tamma, V., Wooldridge, M., Blacoe, I. and Dickinson, I.,

2002. An Ontology Based Approach to Automated

Negotiation. In: Agent-Mediated Electronic Commerce

IV. Designing Mechanisms and Systems: AAMAS 2002

Workshop on Agent-Mediated Electronic Commerce.

2002 pp. 219–237. DOI: https://doi.org/10.1007/3-540-

36378-5_14.

Terziyan, V., Gryshko, S. and Golovianko, M., 2018.

Patented intelligence: Cloning human decision models

for Industry 4.0, Journal of Manufacturing Systems, 48:

204–217. DOI: https://doi.org/10.1016/j.jmsy.

2018.04.019.

Wang, G., Wong, T.N. and Wang, X., 2013. An ontology

based approach to organize multi-agent assisted supply

chain negotiations, Computers & Industrial Engineering,

65(1): 2–15. DOI: https://doi.org/10.1016/j.cie.

2012.06.018.

Yin, Y.H., Nee, A.Y.C., Ong, S.K., Zhu, J.Y., Gu, P.H. and

Chen, L.J., 2015. Automating design with intelligent

human–machine integration, CIRP Annals, 64(2): 655–

677. DOI: https://doi.org/10.1016/j.cirp.2015.05.008.

Multi-aspect Ontology for Interoperability in Human-machine Collective Intelligence Systems for Decision Support

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