CDM-Core: A Manufacturing Domain Ontology in OWL2 for

Production and Maintenance

Luca Mazzola

∗

, Patrick Kapahnke, Marko Vujic and Matthias Klusch

I2S - Intelligent Information Systems Research Team, ASR- Agents and Simulated Reality Department,

German Research Center for Artiﬁcial Intelligence (DFKI) Stuhlsatzenhausweg 3, D-66123 Saarbruecken, Germany

Keywords:

CDM-Core, Applied Ontology, Semantic Annotation, Knowledge Engineering, CREMA H2020 RIA Project,

Ontology Quality Measurement.

Abstract:

Ontology engineering is known to be a complex, time-consuming, and costly process, in particular, if an on-

tology has to be developed from scratch, and respective domain knowledge has to be formally encoded. This

paper presents the largest publicly available manufacturing ontology CDM-Core in the standard formal on-

tology language OWL2

. The CDM-Core ontology has been developed within the European research project

CREMA in close collaboration with the user partners in order to sufﬁciently cover the CREMA use case do-

mains of metal press maintenance and automative exhaust production. CDM-Core makes use of many relevant

standard vocabularies and ontologies, with only about one ﬁfth of its size being CREMA use case speciﬁc.

The practical applicability of CDM-Core for semantic annotation of domain-related process models, sensor

data and services has been approved by the user partners, and its quality according to selected common cri-

teria of veriﬁcation and validation was successfully evaluated. From the public release of the CDM-Core, we

expect to cover the lack of a base common ontology for the manufacturing domain, thanks to feedbacks from

industrial reuse and improvements from the community.

1 INTRODUCTION

Developing ontologies is considered nowadays a stan-

dard activity in research project dealing with seman-

tics. Unfortunately, this is not a common result of

applied projects, where the effort and knowledge re-

quired to develop an ontology from scratch is con-

sidered not sustainable, in respect of the expected

beneﬁts. For this reason, in the context of the EU-

founded Horizon2020 CREMA

project, we decided

to develop the ”CREMA Data Model, Core mod-

ule” (CDM-Core), as a manufacturing ontology tak-

ing into account both the general manufacturing do-

main applicability and the speciﬁc project use cases

coverage. As a result, this is the ﬁrst publicly avail-

able applied manufacturing ontology (Mazzola et al.,

2016), composed by three different parts: a gen-

eral manufacturing-related ﬂat layer, a set of domain-

speciﬁc or standard-based vertical slices such as

”Conditional Monitoring” or ”Semantic Sensor Net-

∗

Corresponding author, mazzola.luca@gmail.com

Available at: http://sourceforge.net/projects/cdm-core/

CREMA is ”Cloud-based Rapid Elastic MAnufactur-

ing” and its website is http://www.crema-project.eu/

work”, and some use cases specialized segments (au-

tomotive exhaust production and metallic press main-

tenance), that can be a guidance for developing other

speciﬁc applications.

The rest of the paper is organized as follows: In

section 2 the CDM-Core requirements are presented,

together with the obtained results from the distributed

process for the ontology creation; some of the CDM-

Core usages for semantic annotation of process mod-

els, services and data streams are showcased in Sec-

tion 3; whether Section 4 closes the paper with an

analysis of the quality measures for the developed on-

tology.

2 THE CDM-Core ONTOLOGY

From the use cases description and the user partner

inputs, a set of high level requirements was designed,

and subsequently validated.

The elicited requirements include multifaceted as-

pects, such as the CDM-Core capability to represent

domain knowledge for both use cases, in order to al-

low annotation of process model, services and sensors

136

Mazzola, L., Kapahnke, P., Vujic, M. and Klusch, M.

CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance.

DOI: 10.5220/0006056301360143

In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 2: KEOD, pages 136-143

ISBN: 978-989-758-203-5

data; its expected (logical) consistency; the adoption

of selected relevant freely available standards; and the

usage of a standard W3C modelling language. Based

on these speciﬁcations, the development started with

the engineering phase, as brieﬂy described in the next

section.

2.1 Ontology Engineering

The CDM-Core is modeled in the standard W3C on-

tology language OWL2-DL (Consortium et al., 2012).

The CDM-Core has been developed by the task part-

ners according to the distributed ontology engineer-

ing (OE) methodology DILIGENT (Tempich et al.,

2005). Coherently with the literature (Sure et al.,

2009), (Simperl et al., 2010) there are some common

steps, that were followed also in this case.

Initially, the Domain analysis concerns the elici-

tation of requirements from use cases, the identiﬁca-

tion of relevant information sources and the ranking

and adoption of relevant semantic data models which

are available for public reuse. Then there is the Con-

ceptualisation of knowledge, where these inputs are

tranformed into of a semi-formal conceptual model

of objects and concepts with taxonomic relationships.

Following there is the Formalisation of the concep-

tual model where the conceptual model is translated

into a knowledge representation language with formal

semantics. Eventually, the Evaluation of the formal

ontology phase analyzes the sufﬁcient coverage and

description of the domains by users and domain ex-

perts and the syntactic correctness, consistency and

normalisation of the ontology by the ontology engi-

neer. At the end of each phase, an iteration loop can

happen, if some of the involved professionals ask for a

reﬁnement based on the reached results. In our partic-

ular case, we observed multiple iteration, in particular

triggered by the user partners.

As result, the requirement that CDM-Core repre-

sents knowledge of the CREMA use cases was even-

tually achieved to a satisfactory degree. In fact, the

semantics of the given process models and sensor data

can be basically described using the ontology.

The distributed engineering of the shared CDM-

Core ontology has been performed by task part-

ners according to the DILIGENT methodology (Pinto

et al., 2004).

The role of ontology engineer is manifold and in-

cludes (a) the support of domain analysis and concep-

tualisation of knowledge, (b) the formalisation of the

conceptual model in OWL2, and (c) the technological

evaluation of the CDM-Core. A set of selected part-

ners plays the role respectively of domain experts and

users of the CDM-Core. All task partners are mem-

bers of the control board for ontology analysis, revi-

sion and evaluation.

The CDM-Core ontology engineering process is

cyclic. It is based on four main steps with controlled

iterations: the ﬁrst step is Build where the ontology

engineering team builds a very small and basic con-

sensual version of the CDM-Core ontology. These

initial activities are carried out by the domain experts

intensively supported by the ontology engineer. The

second operation is a Local reﬁnement where each

domain expert performs an in-depth reﬁnement of the

shared CDM-Core version at the local site, towards

a reﬁnement of the conceptual model per use case.

These activities are carried out concurrently and at ge-

ographically dispersed sites. Every local ontologies

is evaluated by domain experts and ontology engi-

neer, and then formalised. As third step, the Analysis

and revision requires that the control board analyses

the locally reﬁned ontologies and revises the shared

CDM-Core ontology accordingly, by means of identi-

ﬁcation of similarities and their respective alignment.

Eventually, after a new release of the ontology, a Lo-

cal update is performed by domain experts before ini-

tiating further local reﬁnements (i.e. 2

operation).

The user partners informed that for the considered

processes and sensor data in the use cases currently

no standard data models were used at their sites.

The result of the initial search and assessment of

relevant non-semantic standard data models carried

out by the task partners is shown in Table 1. In par-

ticular, the main domains of listed data models ac-

cording to their public description are shown in the

column Domain Coverage; in column Public Reuse

the availability of these data models for their transla-

tion (e.g. to OWL2) and inclusion into the publicly

available semantic data model CDM-Core is shown.

Instead, in Table 2 are presented the initial set of con-

sidered semantic data models and extensions in the

OWL2 language.

2.2 Result

The distributed development based on the presented

requirements guided the creation of the ontology. In

Table 3 the actually reused public ontologies in CDM-

Core are stated, together with their role and their

characteristics (numbers of classes, properties and ax-

ioms). The main result, beside the ontology itself, is

its usage for covering the identiﬁed requirements, ex-

amples of which are presented in the next section.

CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance

137

Table 1: Selected non-semantic standard data models for CREMA use cases.

Data Model Modeling Lan-

guage

Domain Coverage Public

Reuse

ISO 13372:2012 UML Txt/Tab Condition monitoring and diagnostics of machines, pre-

dictive maintenance

(Y)

ISO 10303 (STEP)

APs

UML EXPRESS

EXPRESS-G

AP214 in AP242:2104 - Core data for automotive me-

chanical design processes

AP239 - Product Life Cycle Support

AP224 - Mechanical product deﬁnition for process plans

AP240 - Process plans for machined products

ISO STEP PDM

Schema V1.2

Graphical notation,

Txt/Tab

Product data management (common subset extracted

from STEP APs 214, 203, 212, 232)

(Y)

UN/CEFACT CCL,

UNTDED-ISO 7372

Graphical notation,

Txt/Tab, XML(S)

Supply chain and cross-border trading transaction mes-

sages for buy, ship and pay business processes

ASD S-2000M V6.0 UML Txt/Tab Material management incl. spare parts, focus on

aerospace industry

ISA-88 UML Txt/Tab

B2MML

Batch control conﬁguration and communication between

components in batch manufacturing plants

ISA-95 UML Txt/Tab

B2MML

Business logistics and manufacturing control incl. pro-

duction scheduling, maintenance management - at the

level of enterprise, site, area

ISO 3166 , ISO 4217 Txt/Tab, XML Country and currency codes Y

: only the Informative sections

Table 2: Selected semantic data models and extensions in OWL2 language for CREMA use cases.

Ontology Type Relevant Domain Coverage Standard Public

Reuse

SSN (extends DUL) Domain Sensory, Sensor Networks W3C Y

MASON Upper Manufacturing Y

DUL (DOLCE+DnS

Ultralite)

Upper Concepts of physical and social context, tem-

poral and spatial relations

W3C Y

GeoNames Domain Geolocation W3C Y

ONTO-PDM

[PDC12]

Upper Manufacturing product data IEC 62264,

ISO 10303

SCORVoc Domain Supply chain operations reference (SCOR) APICS (Y)

CM (extends SSN) Domain Condition Monitoring, Machinery Mainte-

nance

ISO/IEC

13372

: the original N answer is overrided by the written permission received by APICS

Table 3: Publicly available ontologies (or non-semantic standards translated) used inside CDM-Core release.

Ontology Type

Characteristics

Classes Properties Axioms

MASON (Lemaignan et al., 2006) Upper Ontology 224 40 370

SSN (Compton et al., 2012) Domain 52 55 127

ConditionMonitoring (G

unel et al., 2013) Domain 182 41 363

vCard (Iannella and McKinney, 2013) Domain 62 83 679

Org (Reynolds, 2014) Domain 15 37 662

Time (Hobbs and Pan, 2006) Upper Ontology 13 41 181

TimeLine (Raimond and Abdallah, 2006) Upper Ontology 26 60 350

DUL (Gangemi, 2012) Upper Ontology 76 109 517

SCORvoc (Petersen et al., a) (Petersen et al., b) Domain 279 297 7657

KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development

138

3 USAGES

This section presents examples of semantic annota-

tions of process models, services, and sensor data of

the CREMA use cases based on the CDM-Core. This

is complemented with examples of how these seman-

tic annotations can be used by other CREMA compo-

nents in the use cases, for example for the automatic

implementation and optimization of process model

with services (SOA paradigm).

3.1 Process Model Annotation

In CREMA, the process models are described in the

standard BPMN (Business Process Model Notation).

The formal semantics of a process model in BPMN

can be described by means of its annotation with el-

ements of formal ontologies. Such annotation al-

lows applications to semantically reason on them in

general, and assist process managers in their seman-

tic service-based implementation in particular. Ser-

vices can be more precisely selected and automat-

ically composed for implementing a given process

model based on its semantic annotation. This is in

line with the semantic SOA paradigm

and reference

model (MacKenzie et al., 2006).

In CREMA, a component will perform an optimal

service-based implementation of given process mod-

els with advanced means of semantic service selection

and planning. That requires the semantic annotation

of both process models and available services. The

semantic annotation of process models based on the

CDM-Core is manually done by the process manager

with the help of some specialized interfaces.

There is no standard format for the semantic an-

notation of process models in BPMN yet (Boissel-

Dallier et al., 2015). On an abstract level, the se-

mantics of processes and services can be basically de-

scribed in terms of their input (I), output (O), precon-

dition (P) and effect (E) of their execution. In particu-

lar, the task partners ﬁrst informally described the ba-

sic IOPE semantics of all process tasks of given pro-

cess models in the CREMA use cases. These infor-

mal semantic description consists of text, including

relevant main concepts which were either identiﬁed

in or newly added to the CDM-Core for this purpose.

Next, these semi-formal descriptions were manually

transformed into the formal semantic IOPE-based an-

notations with CDM-Core.

Semantic SOA is considered key to the development

of semantics-empowered intelligent applications for the fu-

ture Internet in various domains including manufacturing

4.0, and supported by an increasing number of industrial

stakeholders such as Software AG, SAP, IBM, Siemens.

Figure 1 shows an example of a semantic IOPE-

based annotation of one process task of one process

model. In particular, this process task is concerned

with the resource allocation of a suitable robot. Given

a production schedule containing a list of orders to

be fulﬁlled, this task identiﬁes a robot cell and corre-

sponding robot, and leases it for the schedule. A robot

must be able to perform welding operations and has to

be equipped with particular clamps to hold a certain

type of exhaust as speciﬁed in the tasks of the sched-

ule. The requirements described above are speciﬁed

through different parameters, including the produc-

tion schedule input, the robot cell and robot outputs,

and a range of internal variables. These semantic an-

notations (such as the ones in Fig. 1) were made rely-

ing on domain-speciﬁc and upper ontologies together

with the use case speciﬁc part of CDM-Core.

The following Listing provides an example of the

semantic annotation of the process task in part A of

the Figure 1 as extension in the BPMN XML source

code. Additional attributes named inputs, outputs,

preconditions and effects are used to attach the seman-

tic annotations to the standard BPMN deﬁnitions.

<?xml version="1.0" encoding="UTF-8"?>

<bpmn:definitions ...

xmlns:tco=http://<URI1>/wp8/tco.owl#

xmlns:mas="http://<URI2>/mason.owl#">

...

<bpmn:task id="Task_1w5d3zt"

name="Select Robot Cell"

inputs="tco:ProductionSchedule :PS"

preconditions="tco:includes(:PS,:T)

and tco:ProductionTask(:T)

and tco:includes(:T,:OP)

and mas:requiresTool(:OP,:TOOL)

and tco:Welder(:TOOL)

and tco:produces(:OP,:EX)

and tco:Exhaust(:EX)"

outputs="tco:RobotCell :CELL

(tco:Robot

and tco:supports some tco:ExhaustClamp

and tco:supports some tco:Welder) :R"

effects="tco:isAllocatedFor(:CELL,:PS)

and tco:includes(:CELL,:R)

and tco:supports(:R,:TOOL)">

...

</bpmn:task>

<bpmn:incoming>SeqFlow_0egvn5w</bpmn:incoming>

<bpmn:outgoing>SeqFlow_03haiqx</bpmn:outgoing>

</bpmn:definitions>

3.2 Service Annotation

Semantic services are services whose functional and

non-functional semantics are described with formal

ontology-based annotations. On an abstract level,

a semantic service description includes a semantic

IOPE-based proﬁle and a semantic process model that

CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance

139

describe what this service does and how it actually

works (Klusch, 2008a), (Klusch, 2008b).

According to Semantic SOA, process models can

be implemented with executable services which se-

mantics are described with a shared formal ontology.

The implementation of each step or task of a process

model with a relevant single or composite service by a

process designer can be supported by means of auto-

mated high-precision semantic selection and planning

of annotated services, either in fully automatically or

in semi-automatically (with user interaction).

Since the CDM-Core is deﬁned in OWL2, one nat-

ural choice of the semantic service description format

would be OWL-S (Web Ontology Language for Web

Services (Martin et al., 2004)) which allows a ground-

ing of semantic services in WSDL or REST services

(Lathem et al., 2007). As mentioned above, seman-

tic service descriptions will be used to determine a

functionally optimal assignment of services to given

annotated process models.

3.3 Data Stream Annotation

In CREMA, CDM-Core can also be used to semanti-

cally annotate sensor data of the use cases. The user

partners provided general information about the metal

press system, the robots, their components and the

attached sensors, as well as the relevant sensor data

schema. The given data schema for the stream of

time-stamped and sequentially ordered data buckets

can be in different format, such as CSV, TSV (Tab

Separated Values) or JSON (Gray et al., 2011).

In particular, each sensor observes or measures

one property such as pressure or temperature. The se-

mantic annotation of streamed sensor data will be au-

tomatically done usign the CDM-Core concepts. That

requires the mapping of the sensor measurement la-

bel to the concept in the ontology, which deﬁnes the

formal semantics of this label in XML-RDF encoded

OWL2. As a result, the data item is described by a

set of RDF triples as an instance of the corresponding

concept in the ontology. This mapping table and re-

spective naming of sensor classes and measurements

is given by the partner generating the stream itself.

Figure 2 presents an example of semantic anno-

tation of multi-variate sensor data from multiple sen-

sors attached to the hydraulic drive system of a metal

press. The semantic annotation of sensor data with

the CDM-Core allows for a qualitative, that is domain

knowledge-based, rather than quantitative statistics-

based data interpretation.

Figure 1: Usage of the CDM-Core ontology for (A) Process

Task inside the BPMN semantic annotation, (B) Service se-

mantic Annotation, and (C) a matching using the plug-in

approach (Paolucci et al., 2002) between the semantic an-

notation on top and middle of this example.

KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development

140

Figure 2: Example of annotation for a data stream schema, in the context of a CREMA project use case.

4 QUALITY MEASURES

Ontology evaluation is the task of measuring the qual-

ity of an ontology according to given criteria. It is ”a

technical judgment of the content of the ontology with

respect to a frame of reference during every phase and

between phases of their lifecycle” and can be classi-

ﬁed into ontology veriﬁcation and validation (G

omez-

erez et al., 2003). There are several approaches,

methods and tools for ontology evaluation available,

but no best practices and guideline for the selection of

measures for ontology quality criteria.

The result has been evaluated according to a se-

lected subset of ontology quality criteria and mea-

sures deﬁned in (Vrandecic, 2010). In particular, the

selected criteria subsume the given requirements for

the CDM-Core. Since there is no publicly available

ontology for the manufacturing domain, our CDM-

Core could not be evaluated against some gold stan-

dard as a reference. For this reason it was performed

by all task partners with user partners as stakeholders.

In summary, all requirements for the CDM-Core

are satisﬁed. The individual results are described in

the following in the context of the selected criteria.

Veriﬁcation was concerned with evaluating if CDM-

Core speciﬁcation is formally correct and meaningful

in terms of syntactic validity, and logical consistency.

Syntactic validity refers to the syntactically correct

encoding of the ontology speciﬁcation, which can be

tested with appropriate validation tools such as the

OWL validator

, SWOOP

, CityPulse Ontology Val-

idator

, Eyeball

, OBO-Edit

, and OOPS!

Logical Consistency requires that the ontology speci-

ﬁcation does not include or allow for any logical con-

tradiction. In other words, an ontology is inconsistent

if it does not allow any formal model to satisfy its

axioms. Checking of consistency can be done with

a classical ontological reasoner such as Pellet (Sirin

et al., 2007). The above mentioned validation tools

support consistency checking, but they differ in the

extent they check for common problems or pitfalls.

Results: The CDM-Core is syntactically valid and

consistent. The XML-RDF encoded speciﬁcation of

the CDM-Core ontology in OWL2 was successfully

tested with the tool OOPS! (also, advanced evaluation

option): it does not contain critical problems such as

circular class hierarchies, redundant axioms, incon-

http://mowl-power.cs.man.ac.uk:8080/validator/

http://semanticweb.org/wiki/Swoop

http://iot.ee.surrey.ac.uk/SSNValidation/

https://www.w3.org/2001/sw/wiki/Eyeball

http://oboedit.org/docs/index.html

http://oops.linkeddata.es/

It is is a kind of inconsistency (Flouris et al., 2006).

CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance

141

sistent naming schemes, or other logically inconsis-

tent deﬁnitions of concepts and relations.

Validation was concerned with evaluating if the on-

tology is practically useful for the targeted stakehold-

ers in terms of its accuracy and completeness of both

covering the use case domains and supporting the

tasks for which the ontology has been designed, its

computational efﬁciency, and its adaptability to man-

ufacturing domains and tasks of other stakeholders

(additional criterion that the task partners identiﬁed

later).

Accuracy checks if the speciﬁcation complies with the

knowledge of the stakeholders. Correctness in this

case means compliance to the gold standards” of use

case descriptions and respective conceptual models.

Completeness verify that the use case domains are

not only accurately but fully covered by the ontology,

and that semantic annotations of given use case pro-

cess models, services, and sensor data are sufﬁciently

supported. It also covers the structural quality of the

ontology for which measurement the following mea-

sures were selected:

(TD) maximal class Taxonomy Depth (Lozano-Tello

and G

omez-P

erez, 2004), i.e. the maximum sub-

sumption path length of the CDM-Core. It covers the

intuition that a high TD reﬂects a more detailed con-

cept knowledge represented by the ontology.

(RR) Relationship Richness (Vrandecic, 2010), i.e.

the ratio between the number of property names and

the number of class names and property names of the

CDM-Core. It reﬂects the diversity of relations in the

ontology and cover the intuition that detailing of ex-

isting classes would increase the relational richness of

the ontology.

(AR) Attribute Richness (Tartir et al., 2005), i.e. the

average number of properties (attributes) per class. It

suggests the intuition that high attribute richness indi-

cates more information about each class on average.

Computational efﬁciency is the reasoning complexity

(RC) of CDM-Core, i.e. the complexity that applies

to the common reasoning tasks for the OWL2 frag-

ment that is actually being used in the CDM-Core.

Adaptability represents an indicator for the effort ex-

pected for effectively reusing the developed ontology

in different cases inside the same domain.

Results: CDM-Core is accurate: as a consequence

of the approval of its sufﬁcient matching with the un-

derlying conceptual model by the stakeholder, deﬁni-

tions and descriptions in CDM-Core are correct.

It is also complete according to the stakeholders. As

a result of its joint engineering, the CDM-Core was

eventually approved by the stakeholders to represent

all relevant instances, concepts and relations of the

conceptual model. Moreover, it allowed the annota-

tion of each of the given process models with con-

cepts and properties; all sensor measurements of the

given data stream schema were semantically mapped

to corresponding elements in it; and every used sen-

sors, robot cells and metal press is represented by an

appropriately designed individual in CDM-Core.

The structural quality factors of the CDM-Core are:

TD = 7, RR = 0.3993, AR = 0.8156. Our interpre-

tation of these values is that the developed CDM-

Core features a very high number of domain-speciﬁc

classes with very high attribute richness (AR), which

indicates a high amount of detailed information about

each class on average. Its class hierarchy is of the

same moderate maximal depth (TD) as, for example,

the generic manufacturing ontology MASON and the

standard W3C SSN ontology, it partly builds upon.

The worst case reasoning complexity (RC) is com-

puted as the intersection of the complexity of the dif-

ferent OWL2 fragments in the CDM-Core, and is

equivalent to SROIQ(D). Anyway no deﬁnition of

CDM-Core covers jointly all the operators of this

complexity, which indicates that the reasoning com-

plexity in practice might be of some magnitude lower.

Adaptability is limited due to its focus on covering the

use case domains described (CREMA

consortium,

2016a), (CREMA consortium, 2016b) and allow-

ing the tasks of annotating the given process mod-

els, services and sensor data. However, the CDM-

Core in particular builds on and includes generic and

standard-based ontologies. These generic parts can

serve other stakeholders to model knowledge of dif-

ferent manufacturing domains and tasks. The normal-

ized proportion of the generic to the CREMA use case

domain-speciﬁc parts is 21.09%.

The CDM-Core is speciﬁed in OWL2-DL and can

be in principle extended and specialized monotoni-

cally, i.e. without the need to remove axioms.

5 CONCLUSIONS

In this paper we presented the ﬁrst publicly available

ontology for the manufacturing domain, together with

the process for its development. It is one intermediate

result of a shared effort of different organizations in

the context of a collaborative project. The validation

showed its capability for covering the requirements

elicited as prerequisite for the ontology engineering

phase. Additionally, some measures for the struc-

tural quality of the produced CDM-Core ontology are

presented, together with its applications for process

model, services, and data stream annotations. The re-

sulting ontology is released for public reuse and we

expect that industries can reuse it, provide feedbacks

KEOD 2016 - 8th International Conference on Knowledge Engineering and Ontology Development

142

and ask for improvement to the community.

This work was partially supported by the Com-

mission of the European Union within the CREMA

H2020-RIA project (Grant agreement no. 637066).

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