=Paper=
{{Paper
|id=Vol-1080/owled2013_4
|storemode=property
|title=Improving Industrial Collaboration with Linked Data, OWL
|pdfUrl=https://ceur-ws.org/Vol-1080/owled2013_4.pdf
|volume=Vol-1080
|dblpUrl=https://dblp.org/rec/conf/owled/HladikCHG13
}}
==Improving Industrial Collaboration with Linked Data, OWL==
https://ceur-ws.org/Vol-1080/owled2013_4.pdf
Improving Industrial Collaboration
with Linked Data and OWL
Jan Hladik1 , Conny Christl4 , Markus Graube2 , Frank Haferkorn3 , Johannes
Pfeffer2 , Leon Urbas2 , and Reinhard Willfort5
1
SAP Research Dresden, jan.hladik@sap.com
2
Institute of Automation, TU Dresden,
{markus.graube,johannes.ziegler,leon.urbas}@tu-dresden.de
3
RST Industrie Automation GmbH, F.Haferkorn@RST-Automation.de
4
Innovation Service Network, conny.weber@innovation.at
5
Dresscode 21 GmbH, reinhard.willfort@dresscode21.com
Abstract. We present a product-centric collaboration platform com-
prising partner companies and their customers. By employing the Linked
Data principles it enables them to easily publish and retrieve informa-
tion about products and to include user-generated content as well as
publicly available information from the Linked Open Data web. Infor-
mation about products is modelled as OWL ontologies, which makes it
more useful and easier to understand for application developers. Self-
explanatory data structures also lower the threshold for new partners
interested in joining the network and contributing their products. The
versatility of this approach is illustrated by two application scenarios.
1 Introduction
Companies often form data silos: their information is stored in proprietary for-
mats that are difficult to understand without expert knowledge that can only be
obtained from the system developers and operators. The meaning of data struc-
tures is contained implicitly in the procedures operating on these structures, and
the vocabulary that is employed is not usually understandable for an outsider.
Inclusion of information from outside the company or sharing of information
with partner companies therefore happens rarely and involves a significant ef-
fort. For a customer without IT expertise, it is utterly impossible to connect his
personal web information with the corresponding company.
However, especially for small and medium-size companies, collaboration with
partner companies often is a requirement in order to offer a satisfactory product
range to the market, since a small company does not have the same large area
of expertise as a large competitor. Interlinking with other (small) companies can
lead to more attractive products. In order to achieve visibility in the web, it
is also important to include customer recommendations and to meet potential
customers in places where they spend their time online, e.g. social networks.
This paper presents first results from the project ComVantage6 (Collabora-
tive Manufacturing Network for Competitive Advantage), which is funded by the
EU FP7 programme and comprises thirteen collaborating companies and uni-
versities. It aims at developing a collaboration platform integrating information
from partner companies, end-customers, and public sources, and at offering this
information in such a way that it can be easily consumed by lightweight applica-
tions running on various devices, including mobile phones and tablet computers.
In order to achieve this aim, the platform uses OWL and RDFS ontologies and
the Linked Data (LD) principles [4]. The motivation for using semantic technolo-
gies is that capturing a company’s knowledge in an ontology makes its meaning
explicit and thus easier to understand for people and machines. Moreover, a large
amount of publicly available data is represented in the Linked Data format.
ComVantage ensures the usefulness of its results for practical applications
by employing a sophisticated business modelling approach [6], by using an in-
tegrated security concept, and by including application scenarios from very dif-
ferent business areas, namely automotive plant engineering and commissioning,
machine maintenance, and tailor-made clothing. In this paper, we present re-
sults based on the first prototype finished after the first year of the project, and
we focus on the aspects relevant for semantic technologies within the two latter
application scenarios.
2 The ComVantage architecture
The aim of our platform is to facilitate collaboration among various organisations
involved in industrial supply chains, enabling an efficient flow of information in
value generating processes, without introducing a high level of complexity. In
order to achieve this, our goals are:
– Making relevant information from business software accessible as LD
– Making factory-level information from device middleware accessible as LD
– Maintaining access control for LD between partners of virtual factories
– Supporting business end user interaction with LD on mobile devices
Due to the dynamic nature of the envisioned virtual factories, the technology
for sharing the data needs to be highly de-centralised, i.e. it must not rely on a
central managing partner with a centralised platform. Furthermore, the ability
to quickly integrate new partners needs to be supported by light-weight, easy to
implement approaches for the necessary data exchange formats and interfaces.
As such dynamic collaborations evolve over time, the formats and interfaces also
need to be designed for extensibility.
Since we want to avoid disrupting existing workflows, we do not require a
complete move of all existing data to the OWL/RDF format and into a triple
store, but we include the existing systems by transforming their contents into
RDF syntax on the fly [11]. Figure 1 shows this approach: next to the company’s
6
http://www.comvantage.eu
Fig. 1. ComVantage architecture
triple store at the bottom left, which contains the new OWL data models and ad-
ditional information, there is the legacy database, whose content is transformed
into RDF by the Linked Data adapter sitting on top of it. This procedure, which
is also called semantic lifting, is comparably easy for static data sources such as
databases, and software performing this task is readily available [3]. When deal-
ing with live sensor data from a machine middleware, as on the bottom right, a
more complex approach is required to deal with the stream nature of the data.
One advantage especially for small companies is that existing public ontolo-
gies, both for terminologies and for instance data, can be included easily in the
data model because of the common data format, as can be seen on the far right.
Another advantage is having a common data format that is valid for the entire
network and thus makes including a new partner easier. Even if this partner’s
data sources require an extension of the existing ontology, this is feasible by
introducing new classes and properties. A tool for extending an existing ontol-
ogy is currently under development [5]. If the built-in annotation features like
rdfs:comment and rdfs:description are used extensively, applications can be
developed without contacting the owner of the underlying data sources.
Access to the information repositories is controlled by the Domain Access
Server (DAS), which extends the access policies for the legacy systems to the
semantically extended versions. The DAS is also responsible for mapping entities
from different sources and for distributing complex queries to the respective data
sources. The data is presented to the users by a set of task-centric, small and
easy to use applications, which fulfil their requirements as an ensemble (this
approach is described in detail in Section 3).
2.1 Employing existing software
A smooth management of multiple datasets is necessary for spanning a virtual
factory with the described architecture. This includes the creation and modifica-
tion of common vocabularies, the seamless integration of existing software and
data and the interlinking between prior isolated information spaces. However,
most of these tasks are already well supported by appropriate tools. The LOD2
project, for example, provides a software stack of “aligned tools which support
the whole life cycle of Linked Data from extraction, authoring/creation via en-
richment, interlinking, fusing to maintenance” [2]. ComVantage adapted this list
of tools for the purposes of virtual factories. An excerpt of the chosen tools will
be presented in this section.
Relational databases are frequently used in today’s companies for storing
data in a structured and well supported format. The stakeholders of virtual com-
panies have to expose some of this information as Linked Data for their partners
to allow value-added services across multiple stakeholders. Within ComVantage
it was decided to use the D2RQ platform [3] as a Linked Data adapter for this
task (e.g. in the use case of customer-oriented production as described in Sec-
tion 4.2). The D2RQ platform reads data from a relational database, converts
it into RDF and provides this data both as HTTP-representations in RDF or
HTML and through a SPARQL endpoint (which Triplify7 as major alternative
does not provide). Since D2RQ uses JDBC drivers it can be connected to a wide
range of relational databases. The conversion is applied according to a set of
rules which logically contains the semantics of the database.
The management of ontologies is another important task because every con-
sistent model should have well-defined vocabularies. This is becoming even more
important when considering multiple stakeholders which have to understand,
use, and maintain the vocabularies of their partners. For ComVantage the ex-
pressiveness of OWL-Lite is sufficient for providing the possibilities of declaring
hierarchies, identity and some major restrictions. However, we opted to use OWL
rather than RDFS since the stricter OWL syntax provides better guidance and
because its reasoning capabilities can provide a sanity check for newly developed
ontologies. Since the LOD2 stack lacks an ontology engineering tool, ComVan-
tage uses Protégé, because it exactly focuses on the creation and modification
of OWL ontologies and has a broad user community [10].
The vocabularies as well as other information that cannot be dynamically
generated have to be stored in a persistent triple store. ComVantage decided to
follow the suggestion of LOD2 and use Virtuoso [7]. It supports a fine-grained ac-
cess control and access interfaces via configurable SPARQL endpoints. Virtuoso
additionally provides possibilities for content negotiation allowing the delivery of
resource information in different formats. Besides several serialisations of RDF,
7
http://triplify.org
this includes different HTML representations of the stored RDF data, which
enables easy debugging.
Every stakeholder usually has different information sources. These datasets
can be interlinked during the conversion process utilising known attributes of the
entities. However, the full power of Linked Data comes into play when connecting
information from different stakeholders as the scenario descriptions will show in
Sections 4.1 and 4.2. There, datasets usually have neither common vocabularies
nor common keys that can be used for effortless linking. Thus, a tool is necessary
that can find appropriate links between two entities. Their attributes are only
described by ontologies and can need transformation before comparison with
each other. Silk [16] supports this task very well and works directly on the
provided data via access through SPARQL endpoints.
2.2 Developing ontologies
For the creation of the ontologies for our application scenarios, we considered
three ontology engineering methodologies: the TOVE methodology [12], the En-
terprise methodology [15], and Methontology [9]. The Enterprise methodology
turned out to be best suited for ComVantage because it provides enough guid-
ance to steer the ontology development process (TOVE does not differentiate
clearly between the different phases; see also [8]) without introducing unneces-
sary overhead regarding the production of documents or running several tasks
in parallel (Methontology involves a complex set of activities and extensive doc-
umentation). The Enterprise methodology specifies the following phases:
1. Identifying the purpose: determining why the ontology is being built, who
will use it, and for which aim.
2. Building the ontology:
(a) Capturing: identifying the key concepts, producing definitions for these
concepts, and agreeing on names for these concepts.
(b) Coding: representing the conceptualisation produced in the previous
stage formally in an ontology language.
(c) Integrating existing ontologies: finding usable terms from other ontolo-
gies and connecting them with the terms from the newly developed on-
tology.
3. Evaluation: making a technical judgement of the ontology with respect to
the requirements specification or the real world.
4. Documentation: recording all important assumptions and decisions.
Several features of the Enterprise methodology turned out to be helpful for
our purposes, since they helped in keeping the discussion focused, and since they
improved the communication between ontology engineers and domain experts.
For example, in the capturing phase, the authors recommend agreeing on the
definition before deciding on the term to be used for the concept because people
working in different areas tend to have different association with terms, which
makes it difficult to reach an agreement if the term is chosen first. Moreover,
unlike most other methodologies for software or ontology engineering, the Enter-
prise methodology suggests neither a top-down approach (going from the most
general to more specific terms) nor a bottom-up one (in the opposite direction),
but rather goes middle-out, i.e. it starts from the most frequently used concepts,
which normally are located at the middle height in the ontology hierarchy.
3 User interface
In a collaboration environment, different information spaces from various stake-
holders need to be interconnected to be able to support complex workflows.
While the individual information spaces have a high level of internal connec-
tivity and may be semantically well enriched, external connections (i.e. to in-
formation spaces of other stakeholders) are sparse. Apps8 perfectly match these
circumstances because they support a certain enclosed task (such as browsing
a list, analysing a diagram or sending a report) and usually rely on only a sin-
gle information space. Linked information in other information spaces usually
model different aspects and thus should be handled by other appropriate apps.
That allows application developers to concentrate on a specific information space
exploiting the full power of underlying OWL ontologies.
However, for supporting industrial workflows the tasks supported by single
apps have to be connected. This is possible because within ComVantage apps
can rely on a common business model and associated business processes. It is
clear that whole sets of apps may be necessary in order to accomplish complex
tasks. They have to be used in the right order, must have access to associated
information and must be adapted to the context of use.
For these reasons, we argue that in the industrial context a semi-automatic
orchestration of apps is more feasible than individual app selection and man-
agement by each user [17]. Therefore we have developed an innovative concept
called Mobile App Orchestration that allows for building mobile applications
which support complex workflows and leverage inter-organizational collabora-
tion spaces. It consists of three major steps. Select and Adapt are executed at
the design time of the applications, while the Manage step reaches into run time.
During the Select step, apps are selected from a repository according to the
workflow that shall be supported. The selection of appropriate apps is achieved
through reconciliation of the app description with the workflow model, according
to semantic similarity. The level of automation is dependent on the availability
and extent of task, data, context and workflow models that the collaboration
stakeholders provide. Ideally, all required apps can be found in the App Pool. If
this is not the case the respective task is omitted and an appropriate app can
later be added to the ensemble.
In order to satisfy all needs for industrial usage the selected apps need to be
adapted to the context of use. Basic adaptation can be achieved by parametrising
the data acquisition, setting a style sheet and choosing app parameters. The
8
Applications that run on mobile devices and are task-centric, context-aware and well
adapted to a specific platform and context of use
adaptation of the information retrieval takes the data model, the used ontologies
and access rights into account and thus heavily relies on self-explanation of
information and a good ontology engineering process as described in Section 2.2.
Furthermore, the visual appearance may be adapted to comply with corporate
design or presentations rules or the interaction may be configured for specific
modalities, such as voice or gesture interaction.
During the Manage step, a run time component is created which is responsi-
ble for the management of the adapted apps. Therefore the navigation design is
derived from the workflow model. The run time component and the adapted apps
form an ensemble which can be deployed to the mobile device. At run time this
component loads the navigation design and manages inter-app-communication,
app switching and data access. Users can begin the workflow by logging into
the collaboration network and are then guided from app to app until they have
completed the entire workflow.
4 Application scenarios
4.1 The Mobile Maintenance scenario
The Mobile Maintenance scenario is concerned with accessing data about pro-
duction lines and their machines. This data has to be integrated from various
sources, like the producer of the machine, its owner, and the service company.
The use of modern mobile devices is key to simplifying the training on the job
and to improving tools for the maintenance staff, since a consistent user interface
for all machines reduces the training efforts. The data presented to the service
personnel can be either static, e.g. describing the structure of a machine, or
transient, i.e. changing quickly (within the magnitude of milliseconds), e.g. from
sensors for environmental data like temperature or pressure. For the diagnosis,
access to the current data is vitally important. Integrating transient data within
our environment requires additional effort since entering each value into a triple
store is not feasible due to the high updating rate and the large number of avail-
able sensors in a factory. We therefore developed an LD adapter for the machine
middleware, which provides the sensor data.
There exist approaches for handling large networks of sensors, like the Linked
Sensor Middleware9 with over 100000 sensors worldwide or the Semantic Sensor
Network10 . However, these networks have significantly longer update intervals
(in the order of magnitude of minutes), and thus their results are not applicable
within this scenario.
The Data Harmonisation Middleware Adapter. The Data Harmonization
Middleware Adapter or DHM-Adapter performs the following tasks in order to
identify and access live data: firstly, it identifies the sensor using the sensor’s
9
http://lsm.deri.ie/ and http://code.google.com/p/deri-lsm/
10
http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/
Fig. 2. Architecture of the DHM
URI and the ontology containing the machine semantics. Then it accesses the
sensor’s current data by performing an HTTPS request for this URI. In Figure 2
the functionality of the DHM-Adapter is shown in detail:
The Middleware (like OPC11 or GAMMA V12 ) controls one or more ma-
chines, which contain the sensors that should be accessed.
The Machine Semantics is an OWL ontology representing hierarchical de-
scription of the factory using terms like enterprise, site, area, or module,
down to single sensors. It conforms with the physical model of ISA-88/IEC
61512 standard13 and thus is suitable in many environments.
Linked Data Publisher (LDP) collects the content of the Machine Semantics
about all available sensors from the machine’s configuration.
The Linked Data Server (LDS) accesses the Machine Semantics from the
LDP component and provides a SPARQL endpoint for the SupportApp.
The SupportApp provides the user interface for the maintenance staff. It re-
trieves all relevant semantic information about the machine from the LDS.
It allows browsing through the machine hierarchy and reading sensors.
The Access Control component restricts the access to the DHM-Adapter.
The Live Data Access component (shaded in grey in Figure 2) receives a
sensor’s URI, issues a call to the machine middleware, transforms the return
value into RDF and returns this current value to the caller.
11
http://www.opcfoundation.org
12
http://www.rst-automation.de
13
http://www.isa-88.com
4.2 The Customer-Oriented Production scenario
The customer-oriented production scenario is focused on a small network-based
company offering tailor-made business shirts over the internet. In the fashion
industry, end-customers’ requirements are rarely integrated with the design and
production processes, while on the other hand there is an increase of requests
for individualization of products. The unique selling proposition (USP) of the
customer-oriented production scenario aims at being different both from the
producer’s and the customer’s point of view. Thus, the ordering and production
process is managed completely virtually while the customers have high degrees
of personalization.
Challenges for the customer-oriented production scenario. As a result of
the project we envision a prototype for mobile devices whose interface allows bet-
ter collaboration between suppliers and customers. Currently the virtual supply
chain works via e-mail coordination or already existing communication between
partners and a low involvement of the customer. The ComVantage customer and
producer application shall involve the web shop in a virtual collaboration space
with different stakeholders and customers all over the world. Thus, the chal-
lenges for our Linked Data and OWL based ComVantage platform range from
allowing real-time decisions within the network, to providing in-time supplier
substitutes, up to integrating end-customers in production processes following
open innovation concepts.
For this purpose, we will develop two main applications: The aim of the mo-
bile customer application is to improve customer involvement following an open
innovation/open design approach; e.g. enabling the customers to access style
recommendation services, shirts designed by the crowd and product information
via social media platforms as well as in-time change of shipping modalities or
in-time integration of user feedback into the production etc. As soon as the shop
confirms an order, the producer application can dispatch the order information
to the appropriate stakeholders within the collaboration space (according to the
stakeholder’s resources, price, and location). Furthermore, the open nature and
easily understandable meaning of the Linked Data and OWL based infrastruc-
ture allows all interested stakeholders (e.g. persons or organizations providing
design, sewing, delivery, etc.) within the collaboration network to join the value
chain by simply downloading the mobile producer application and relevant guide-
lines. Linking different data and systems allows high personalization and flexible
and individually composed production processes. Thus, a small company can
become a part of a virtual factory and join a flexible cooperative network.
Addressing key requirements with mobile and semantic technology.
Based on stakeholder and customer interviews we identified two key require-
ments to be addressed by taking advantage of mobile and semantic technology.
The idea is that everyone who wants to join the network can do so simply by
downloading a mobile app. The Linked Data and OWL approach allows high
flexibility e.g. to re-use and share already existing ontologies of the textile do-
main. By focusing on a clear user interface complex technology in the background
should be easy to use. Thus, also micro companies without any background in
semantic technologies can provide, consume and exchange relevant information
in the virtual collaboration network. The identified key requirements can be
summarized as follows:
– Lightweight and affordable infrastructure for application in technological un-
aware environments. Especially small and micro companies working for the
textile industry, e.g. sewer companies in Slovenia, lack a sophisticated IT en-
vironment. However, to realize the envisioned application scenario as well as
to enhance the competitiveness of such companies, end-to-end transparency
about processes is required. Additionally, joining the virtual factory network
should not require high costs.
– Flexible and usable solutions supporting an open virtual factory spirit. For
allowing a designer or a self-employed sewer to easily join the virtual network
regardless of place and time, the technical solution has to be very flexible
and easy to use.
A Visionary Scenario for customer-oriented production. Manuel is a
customer ordering a business shirt with the web shop’s app on his tablet PC.
He selects the style from a designer who won the shop’s latest design com-
petition and enters his body measurements. As sustainability, fair trade, and
corporate social responsibility (CSR) are very important values for Manuel, he
chooses this additional option for his shirt. In the next ordering step, a sketch
of the whole value chain opens. For each step in the chain, different suppliers
are recommended and partner descriptions, ratings and their social activities
are provided. Manuel can select suppliers for each step individually throughout
the whole chain and receives the required information regarding shirt prices and
shipping in runtime. After completing his order, the shop system checks the data
and sends the request to all involved suppliers. As availability of capacities has
been checked before, the order can be processed successfully. All involved sup-
pliers use the production application on their smartphones or tablet computers
in the production environment to track and indicate the individual production
steps. Thus, Manuel can follow the process with his smartphone and still perform
any modifications if desired. When the shirt arrives, he takes a photo and posts it
on his facebook account, which is linked from his customer account. Afterwards,
he receives a facebook comment from the designer: “Thanks for buying my shirt
design.” Another comment comes from the sewer: “Enjoy it, I sewed it.”
First Results: From a vision to a ComVantage prototype. The devel-
opment of ontologies is motivated by scenarios that arise in the applications. In
particular, such scenarios may be presented by industrial partners as problems
which they encounter in their enterprises. The motivating scenarios are story
problems or examples which are not adequately addressed by existing ontolo-
gies. A motivating scenario also provides a set of intuitively possible solutions to
the scenario problems. These solutions provide an informal intended semantics
for the objects and relations that will later be included in the ontology.
The main results achieved so far from the beginning of the project cover the
refinement of the use cases with respect to their business model by elaborating
in detail the personas, the scenarios and use cases as well as a set of initial
requirements for the target system. Based on these results, an ontology repre-
senting the data structures of the scenario has been created by domain experts
and knowledge engineers according to the methodology described in Section 2.2.
In a second step, the content of the existing shop database was integrated with
this data model by using the D2R adapter described in Section 2.1, which uses
the vocabulary from the FOAF14 and vCard15 ontologies and also includes links
with DBpedia [1] entries for the fabric used. This allows the the shop owner to
provide the customer with a large amount of additional information about the
product without having to maintain this data himself. In the future, our plan is
to also integrate the eClass [13] and GoodRelations [14] ontologies with the shop
ontology in order to make the shop offers more accessible for semantic search
engines.
5 Conclusion and Outlook
In this paper, we have demonstrated how we use OWL ontologies and the Linked
Data principles to facilitate collaboration, integration and end-customer inter-
action within business contexts. For the different application scenarios, we have
shown how we created ontologies and integrated the existing infrastructure into
the Linked Data web. Based upon this, the ComVantage integration platform
can support different kinds of interaction between companies, partners and cus-
tomers. The ideas underlying our ontology development concept and the user
interfaces have been explained.
This paper describes the status of the project after one year of its planned
three-year runtime. Consequently, some parts of the platform are still in an ex-
perimental stage, e.g. the integration of Linked Open Data, and we cannot yet
provide extensive experimental results. Some other aspects have been omitted
due to space restrictions; e.g. the Plant Engineering and Commissioning sce-
nario. In addition to these topics, the development of the app suites for the
different scenarios is among our goals for the next phase of the project. We also
plan to use the OWL reasoning capabilities to find modelling errors in ontologies
and to make knowlege that is contained implicitly in the ontology explicit and
thus usable.
References
1. S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. G. Ives. Dbpedia:
A nucleus for a web of open data. In ISWC/ASWC, volume 4825 of Lecture Notes
14
http://www.foaf-project.org
15
http://www.w3.org/TR/vcard-rdf/
in Computer Science. Springer, 2007.
2. S. Auer, L. Bühmann, C. Dirschl, O. Erling, M. Hausenblas, R. Isele, J. Lehmann,
M. Martin, P. N. Mendes, B. V. Nuffelen, C. Stadler, S. Tramp, and H. Williams.
Managing the life-cycle of linked data with the LOD2 stack. In International
Semantic Web Conference (2), volume 7650 of Lecture Notes in Computer Science.
Springer, 2012.
3. C. Bizer and R. Cyganiak. D2R server—publishing relational databases on the
Semantic Web (poster). In 5th International Semantic Web Conference, 2006.
4. C. Bizer, T. Heath, and T. Berners-Lee. Linked data - the story so far. International
Journal on Semantic Web and Information Systems, 5(3):122, 2009.
5. M. Brade, F. Schneider, A. Salmen, and R. Groh. OntoSketch: Towards digi-
tal sketching as a tool for creating and extending ontologies for non-experts. In
Proceedings of the 13th International Conference on Knowledge Management and
Knowledge Technologies (i-Know). ACM, 2013. (submitted).
6. R. Buchmann and D. Karagiannis. Modelling collaborative-driven supply chains:
The ComVantage method. In Proceedings of the 2013 IFAC conference on manu-
facturing modelling, management, and control, 2013. (submitted).
7. O. Erling. Virtuoso, a hybrid RDBMS/Graph column store. IEEE Data Eng. Bull,
35(1):3–8, 2012.
8. M. Fernández López. Overview of Methodologies for Building Ontologies. In
Proceedings of the IJCAI-99 Workshop on Ontologies and Problem Solving Methods
(KRR5) Stockholm, Sweden, August 2, 1999, 1999.
9. M. Fernández López, A. Gomez Perez, and N. Juristo. Methontology: from onto-
logical art towards ontological engineering. In Proceedings of the AAAI97 Spring
Symposium, Stanford, USA, March 1997.
10. J. H. Gennari, M. A. Musen, R. W. Fergerson, W. E. Grosso, M. Crubzy, H. Eriks-
son, N. F. Noy, and S. W. Tu. The evolution of Protégé: an environment for
knowledge-based systems development. International Journal of Human-Computer
Studies, 58(1):89–123, 2003.
11. M. Graube, J. Pfeffer, J. Ziegler, and L. Urbas. Linked data as integrating tech-
nology for industrial data. International Journal of Distributed Systems and Tech-
nologies (IJDST), 3(3):40–52, 2012.
12. M. Gruninger and M. S. Fox. Methodology for the design and evaluation of ontolo-
gies. In Workshop on Basic Ontological Issues in Knowledge Sharing, IJCAI-95,
Montreal, 1995.
13. M. Hepp. eClassOWL: A fully-fledged products and services ontology in OWL
(Poster). In 4th International Semantic Web Conference (ISWC2005), 2005.
14. M. Hepp. Goodrelations: An ontology for describing products and services offers on
the web. In EKAW, volume 5268 of Lecture Notes in Computer Science. Springer,
2008.
15. M. Uschold and M. King. Towards a methodology for building ontologies. In
Workshop on Basic Ontological Issues in Knowledge Sharing, held in conjunction
with IJCAI-95, 1995.
16. J. Volz, C. Bizer, M. Gaedke, and G. Kobilarov. Discovering and maintaining links
on the web of data. In The Semantic Web - ISWC 2009, volume 5823 of Lecture
Notes in Computer Science. Springer Berlin / Heidelberg, 2009.
17. J. Ziegler, M. Graube, J. Pfeffer, and L. Urbas. Beyond app-chaining: Mobile
app orchestration for efficient model driven software generation. In 17th interna-
tional IEEE Conference on Emerging Technologies & Factory Automation, Krakau,
Poland, 2012.