1
Managing Service Innovations with an
Idea Ontology
Christoph Riedl1, Norman May2, Jan Finzen3, Stephan Stathel4,
Torsten Leidig2, Viktor Kaufman2, Roxana Belecheanu2, Helmut Krcmar1
1Technische Universität München, 2SAP CEC Karlsruhe, 3Fraunhofer IAO, 4FZI
As the importance of the service sector increases, so does the importance
of systematic approaches to develop new services. Two key activities in
service innovation are generation and evaluation of new service ideas.
Exchanging and analysing ideas across different software tools and re-
positories is needed to implement the concepts of open innovation and ho-
listic innovation management. In this paper, we introduce an ontology to
represent ideas for service innovations. The Idea Ontology provides a
common language to foster interoperability between tools and to support
the idea life cycle. The paper focuses on how such an ontology-based ap-
proach can be used to facilitate innovation management in the service
domain where special aspects of services impair innovation. The expected
benefits of a semantic approach such as semantic reasoning and auto-
matic analysis of ideas are highlighted.
1. Introduction
As the importance of the service sector increases, so does the importance of sys-
tematic approaches to develop new services (Arundel et al., 2007). Exchanging and
analysing ideas across different software tools and repositories is needed to imple-
ment the concepts of open innovation and holistic innovation management for ser-
vices (Chesbrough, 2006). There is a significant growth in the number of Web plat-
forms for idea development, showing the trend towards customer integration in both,
product and service innovation processes. Moreover, the rise of business value net-
works that foster inter-organisational collaboration for joint service delivery necessi-
tates innovation processes that overcome company boundaries (Stathel et al., 2008).
One basic prerequisite for such processes lies in a common data model that enables
data exchange and avoids misinterpretations. Through the use of Semantic Web
techniques this integration can be achieved and additional benefits like semantic rea-
soning and automatic analysis become available. Thus, we propose an ontology that
captures both, a core idea concept covering the 'heart of the idea' as well as further
vital concepts necessary for collaborative idea development, like rating, discussing,
tagging, and grouping ideas. This modular approach allows the Idea Ontology to be
complemented by additional concepts like customised evaluation methods.

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The remainder of this paper is structured as follows. In Chapter 2, we start with a
brief introduction of the field of innovation management with special emphasis on
service-related aspects. This leads to the fairly new trend of collecting ideas in Open
Innovation communities using Web-based idea development platforms. The growing
success of some of these platforms leads to more and more similar websites being
launched. This raises the question of interoperability. In Chapter 3, we address this
challenge with the design of a specialised Idea Ontology. After giving a brief introduc-
tion on ontologies, we describe the design criteria and the main concepts of our Idea
Ontology. We finish by discussing the results and giving an outlook to further planned
activities.
2. Innovation Management
The Oxford English Dictionary defines an idea as: “1 a thought or suggestion about a
possible course of action. 2 a mental impression. 3 a belief.” Contrary, an innovation
is defined as: “1 the action or process of innovating. 2 a new method, idea, product,
etc.” Rogers defines an innovation as “an idea, practice or object that is perceived as
new by an individual or other unit of adoption” (Rogers, 2003). This definition indi-
cates that an innovation is more than an idea. To become an innovation, an idea has
to be adopted. This concept is further developed by linking an idea or invention not
only to adoption but to the concept of commercialisation. Thus, Porter defines inno-
vation as “a new way of doing things (termed invention by some authors) that is
commercialized” (Porter, 1990).
In the context of providing tool support for the management of innovation processes,
these definitions are not fully adequate because they lack a specification of (1) what
information should be conveyed in an idea and (2) which methods or operations are
applied to ideas. Hence, for the purpose of developing a semantic representation of
the concept of an idea in innovation management applications we informally define
an idea:
An idea is an explicit description of an invention or problem solution with
the intention of implementation as a new product, service, or process
within an organisation.
This central concept of an idea which we term Core Idea can be supplemented with
various concepts that relate to feasibility and marketability, i.e., commercialisation.
Many of these concepts are used in the vast selection of tools that have been devel-
oped to support the different phases of idea generation and idea evaluation (van
Gundy, 1988; see Ardilio, Auernhammer, Kohn, 2004 for an overview).
2.1. Service-Specific Aspects and Challenges
Globalisation and the rise of information and communication technologies support
and require increasing customer centricity and shorter time-to-market. The Web 2.0
evolution brings more power to the customer, thereby forcing companies to increase
awareness of users' needs. This leads to an increasing formation of business value

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networks that offer new opportunities and chances for innovation-related cooperation
(Janiesch, Ruggaber, Sure, 2008; Barros, Dumas, 2006; Riedl et al., 2009).
The shift towards business value networks and the adoption of appropriate innova-
tion management processes of course go along with a number of challenges that
have to be overcome, especially when it comes to services as the object of innova-
tion. In the following section we will discuss the most important challenges.
Intangibility of Services Services are largely “intangible” which means that they are
far more difficult to explain and display than products, "hence, their qualities are not
easily explained to the customer" (Hipp, Grupp, 2005). This issue becomes even
more severe, if it comes to actively engaging customers in the innovation process
and it makes community driven service idea development quite a difficult task. This
points to the need for elaborated methods and tools for customer integration. Howells
and Tether claim that “the lack of demanding and novelty seeking customers, who
are willing and able to pay for upgraded, improved or novel services, seems to be a
major and highly important barrier in service innovation which enterprises find difficult
to overcome” (Howells, Tether, 2004). The difficulty of describing intangible services
obviously transfers to the description of service innovations where a misinterpretation
of terms can pose problems in establishing a shared understanding of an innovative
idea.
Formation of Value Networks As a business network by definition involves a mul-
titude of partners, innovation processes naturally become more complex and de-
mands on management increase - and innovation networks do not make an excep-
tion. Van Haverbeke and Cloodt argue that the higher the number of actors, “the
more difficult it becomes to distribute the value created and to manage the value
constellation” (Van Haverbeke, Cloodt, 2006). The orchestration of innovation activi-
ties thus must be managed in a well-defined way. In particular the exchange of inno-
vation related information and innovation ideas between the actors involved in ser-
vice innovation plays an important role (Riedl et al., 2009).
Key Characteristics of Electronic Services Electronic services play an increasing
role in our economy. Today, an increasing proportion of services can be accessed
through electronic interfaces or are even fully delivered online. The electronic nature
of these services has significant influences on service innovation. Riedl, Leimeister
and Krcmar identified in particular five key factors influencing the development of
new electronic services:
• Low marginal costs of service delivery,
• High degree of outsourcing,
• Rapid development of new services,
• Transparent service feedback, and
• Continuous improvement and deployment (Riedl, Leimeister, Krcmar, 2009).
The intangible nature of services, the increasing formation of value networks and the
particular characteristics of electronic services that play an increasing role in today’s
businesses prove challenges for traditional service innovation. Service innovation
activities are increasingly opened for external contributors, in particular customers.

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These challenges are addressed through the development of new innovation ap-
proaches such as Open Innovation and new methods and tools (Chesbrough, 2003).
2.2. Involving Customers and Creative End-Users
A recent trend in innovation management is openly accessible idea Web portals as
one form of user innovation toolkits (von Hippel, 2005). Especially for companies that
sell products directly to the end-customer, Web platforms offer a promising commu-
nication channel to interact with a large audience. Current idea platforms can be di-
vided into two basic categories: On the one hand there are general idea market
places that try to bring together providers and customers. On the other hand we find
platforms that are offered by a single big company to involve their customers in help-
ing improve the company’s product and service portfolio.
Probably the two most frequently-used platforms are Starbucks Idea Force and Dell
Idea Storm which both fall into the latter category. Those two have several aspects in
common: They technically base on Salesforce technology and use similar workflows
(users enter ideas, users rate ideas, best ideas get (sometimes) implemented. Dell
calls this principle “View-Post-Vote-See” while Starbucks summarises it as “Share-
Vote-Discuss-See”. Both companies do not even have to offer “real” incentives to
their users – it seems that the perspective of seeing one’s own idea being actually
implemented and the chance of seeing one’s own name on one of the “most popular”
ideas is enough. Both companies are directly serving the end-customer – and seem
to have established their own “listening platform” very successfully. If we take a
closer look at the idea portfolio it becomes clear that a very large part of it is about
services: In the case of Starbucks, the top level categorisation is divided into “prod-
ucts”, “experience”, and “involvement”, the letter two containing mostly ideas of how
to improve the service level. As of August 2009, about 45.000 ideas are related to
products, while only 19.000 are classified under “experience” – but even within the
product category, a good deal of the uttered ideas have more to do with services
than with actual products. Even in the case of Dell we find a lot of the most prominent
ideas dealing rather with services than with products: Users are asking for selectable
software installation options (e.g., select the operating system, the word processor,
etc.), they like to have compatibility check lists offered, or make suggestions to im-
prove the support by establishing national call centers.
Not only the listening sites operated by a single company seem to be successful to-
day. Also mediator platforms like Springwise, Global Ideas Bank, and Why Not have
grown constantly in the last years. Those platforms are either offered for the common
good or build on a more complex business model: They try to bring together solution
solvers and seekers - the former normally being creative individuals and the latter
companies. Most mediator platforms we found contain ideas related to both, products
and services. It seems that the number of platforms is constantly growing. While the
oldest platform we visited stemmed from 1995, most of the others went online within
the last three years.

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2.3. Collecting and Exchanging Ideas
As more and more idea and innovation platforms appear on the Web, it becomes de-
sirable to exchange information between platforms and tools to prevent ideas from
residing in silos. At the same time, enterprises start to understand the potential bene-
fit from open innovation systems and feel the need to open up their internal innova-
tion processes and to integrate innovation management tools. The semantics of an
organisation’s specific working context is captured by its local or private ontology
which serves the purposes of the particular organisation (Ning et al., 2006). Thus, the
need for a common language, i.e., a common idea data interchange format or a
shared ontology to support the interoperability and to improve cross-enterprise col-
laboration, becomes evident, for „people can‘t share knowledge if they don‘t speak a
common language.” (Davenport and Prusak, 1997). Most platforms offer RSS feeds
that facilitate simultaneously keeping track on the multitude of idea sources. It thus
seems promising to collect the ideas of many platforms and integrate them into one
homogenous database. Nevertheless there is no common structure within the data –
messages from one platform do not necessarily resemble that of other’s. We ana-
lysed idea Web platforms with regards to the information used to describe and man-
age ideas by looking at the input fields and search criteria. Table 1 shows a selection
of the results.

Name Com-
ments
Rating Classes Tags Status Model
Starbucks
(US)1
Yes Thumb
up
Single No New, Under Review,
Reviewed, Coming
soon, Launched
Dell (US)1 Yes Thumb
Up/Dow
n
Multiple No Already Offered, Im-
plemented, In Pro-
gress, Partially Im-
plemented, Reviewed,
Under Review
Crowdspirit
(FR)1
Yes Thumb
Up/Dow
n
Single Yes Incubator (Ongoing,
Evaluated, Rejected),
Elevator, Idea in Mar-
ket
ErfinderProfi
(DE)1
Yes Scale 1
to 10
Single Yes -
Incuby (US)1 Yes 5-Star Single Yes With patent (Pending,
Provisional, Full, Pat.-

1 http://mystarbucksidea.force.com , http://www.dellideastorm.com, http://www.crowdspirit.com, ,
http://www.erfinderprofi.de, http://www.incuby.com, , https://www.atizo.com,
http://www.ideaconnection.com, http://www.globalideasbank.org, http://springwise.com,
http://ideajam.net/, https://mix.oracle.com/, Dell and Starbucks are based on the same Salesforce
Ideas software but use a different configuration

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No.), Without Patent,
Concept or Idea
Atizo (CH)1 Yes Thumb
up
- Yes Open, In Evaluation
Idea Connec-
tion (CAN)1
Yes Thumb
Up/Dow
n, 5-Star
Single No -
Global Ideas
Bank (UK)1
Yes Scale 0
to 10 on
multiple
facettes
Single No New, Seed (Initial
concept, Plant (in de-
velopment), Tree
(reached maturity)
Springwise
(NL)1
Yes - Single No -
IdeaJam (US)1 Yes Thumb
Up/Dow
n
Single Yes Open, In Progress,
Complete, Rejected,
Withdrawn
Oracle Mix
(US)1
Yes Thumb
up
Multiple Yes -
Table 1: Analysis of a sample of publicly available idea portals
Based on this analysis we identified the following aspects to be included in a com-
mon data format for innovation management:
• Comments and discussions help to identify shortcomings within the original
idea and develop it towards the users’ needs (Franke, Saha, 2003; Piller,
Walcher, 2006). Thus, open and interactive forums are key requirements
within company-internal innovation management, e.g., employee suggestion
systems (Fairbank, Williams, 2001; Fiarbank, Spangler, Williams, 2003), as
well as in idea development Web portals.
• Ratings are widely used to estimate user acceptance of ideas. Within innova-
tion management, many different rating mechanisms are generally applied.
The methods differ substantially in (a) the rating subject (who is allowed to
rate), (b) the rating object (what aspects are rated), and (c) the rating scale.
Note that Table 1 names six similar websites applying four different rating
scales. Apart from explicit rating kinds some sites also offer implicit rating val-
ues by providing information about how often an idea has been just clicked on.
• Grouping and clustering methods help to keep track of idea submissions, es-
pecially within large idea portfolios. The two main approaches to group ideas
are hierarchical classification systems and tagging mechanisms. Our findings
show that most often both methods are used in parallel. The categorisation
schemes are usually highly domain-specific and additionally differ in aspects
like granularity, depth, and multi-selectability.

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• Status: Besides content-related classifications, organisational aspects are of-
ten applied to arrange the idea portfolio: many idea portals assign an explicit
development state to each idea, e.g., “ongoing”, “evaluated”, “rejected”, etc.
Furthermore, patents and trade marks are widely used to protect innovations
or to exploit them commercially (e.g. via licensing contracts). Thus, patent and
copyright information are highly important within idea management.
3. An Ontological Approach
Today, most existing idea portals on the Web are restricted to capabilities like tag-
ging and ordinal ratings as the basis for idea analysis. However, we believe that
more powerful tools and methods in idea portals cannot reveal their full potential until
an agreement on the basic concepts of an idea is reached. The use of semantic
techniques brings with it the possibility to improve end-user efficiency by means of
automated processing, and to cope with advanced analytical processing of idea
metadata through reasoning. Then, one can profit from:
• clustering ideas by similarity or relatedness,
• analysing contributions and contributors,
• integrating idea repositories for content management,
• information integration and data exchange across tools and platforms, and
• attaching to social networks and facilitating collaboration.
We further analysed literature to define a core concept of an idea suitable for repre-
sentation of ideas. We combined the results with the demands of three innovation
management projects developed by various research groups and thereby deduced
the requirements for the data model. We analysed existing related standard ontolo-
gies for rating, social interconnections of people (e.g., FOAF, SIOC) and developed a
idea management ontology on top of them. This way we reuse well-established
knowledge standards and at the same time achieve a very powerful knowledge rep-
resentation model. We introduce our proposed ontology in the next sections.
3.1. Ontology Foundations
Ontologies are logical tools to support knowledge representation and retrieval. They
can support innovation management for services by providing a structured represen-
tation of innovation related information such as ideas, ratings, and comments and
thus establishing a unified vocabulary that can be shared across tools and organisa-
tions. The following section introduces ontologies and their potential in service inno-
vation. In order to ensure a common understanding of the characteristic elements of
an ontology, the most important definitions, principals, and classification of ontologies
are introduced.
Ontologies are widely used for different purposes such as natural language process-
ing, knowledge management and the Semantic Web. Various definitions of ontolo-

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gies can be found in the area of Information Science. The most widely cited is that of
Gruber:
“An ontology is an explicit specification of a conceptualization.” (Gruber, 1993)
In this context, a conceptualisation refers to an abstract model of how people think
about things in the world, which is usually restricted to a particular subject area
(Uschold, Gruber, 2004). An explicit specification means that the concepts and rela-
tionships in the abstract model are given explicit names and definitions. A very simi-
lar definition is given by Borst (1997):
“An ontology is a formal specification of a shared conceptualization..”
Formal means that the abstract meaning is encoded in a language whose formal
properties are well understood. This usually refers to some logic-based languages.
Finally, shared implies that the main purpose of an ontology is to be used and reused
across different applications and communities. The terms explicit (Gruber) and formal
(Borst) refer to a declarative representation of the world of interest in which the most
crucial terms are completely defined for mutual understanding (Fensel, 2003,
Mizoguchi, 2003, Uschold, 1996).
The logical theory is composed of vocabulary or concepts to describe the things of
interest. Concepts/vocabulary are used as building blocks of an information process-
ing system (Mizoguchi, 2003). The vocabulary of an ontology is typically contained in
a taxonomy which already holds classes, simple relations and axioms (Mizoguchi,
2003, Krcmar, 2009). The role of an ontology is to provide vocabulary for metadata
description with computer-understandable semantics. Thus, ontologies’ aim is to es-
tablish a shared understanding between parties and make the metadata interoper-
able.
Things are represented in an ontology as classes (also called concepts) and are typi-
cally arranged in taxonomy of classes and subclasses. Classes are typically associ-
ated with various properties (or called roles) that describe features or attributes of the
class. Concrete instances of a class are individuals. Together with an ontology, in-
stances constitute a knowledge base (Uschold, Gruninger, 2004).
Ontologies can be classified anywhere on a continuum ranging between a highly
specific application ontology to a most general representation ontology, depending
on the usage scenario that the ontology is intended for. For the application in the
business context two well-known ontologies are the Enterprise Ontology (Uschold et
al. 1998) and the Toronto Virtual Enterprise (Fox, 1992). They aim at capturing and
analysing the key aspects of an enterprise and thus help to communicate, integrate
and represent the various aspects of an enterprise (Bullinger, 2009).
Possibly the most important general ontology is Dublin Core. The Dublin Core Meta-
data Element Set, Version 1.1,2 is a vocabulary of fifteen properties for use in re-
source description and is maintained by the Dublin Core Metadata Initiative.3 Dublin
Core assigns fixed semantics to properties related to the description of resources

2 http://dublincore.org/documents/dces/
3 http://dublincore.org (last accessed 2009-08-21)

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and provides the possibility to link to several documents. Table 2 provides some
exemplary entities of Dublin Core.

Entities of Dublin Core
Title: A name given to the resource.
Creator: An entity primarily responsible for making the resource.
Subject: The topic of the resource.
Description: An account of the resource.
Date: A point or period of time associated with an event in the lifecycle of
the resource.
Type: The nature or genre of the resource.
Format: The file format, physical medium, or dimensions of the resource.
Identifier: An unambiguous reference to the resource within a given context.
Table 2: Exemplary entities of Dublin Core (source: http://dublincore.org/documents/dces/).
Due to its simplicity Dublin Core is popular for cataloguing and discovering resources
(Krcmar, 2009).
3.2. Classification of Ontologies
As the examples of enterprise ontologies and Dublin Core illustrated, there exist nu-
merous ontologies that differ according to their degree of expressiveness and in-
tended type of application. To structure different ontologies they can be classified by
the subject matter for which the ontology has been developed and can be distin-
guished by their degree of specialisation. Figure 1 displays the classification of on-
tologies proposed by Guarino (1998).

Fig. 1: Ontology classification (adapted from Guarino, 1998).

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• Top-Level ontology (also upper or generic) – describes general knowledge
such as time and space.
• Domain ontology – captures domain knowledge in a generic way. For exam-
ple, it describes a domain such as medicine, enterprises, or trade goods.
• Task ontology – these ontologies describe how a specific task is performed,
for example the assembly of certain parts to form a larger unit.
• Application ontology – have been developed for a specific application, for ex-
ample, to manage services. They contain concepts that depend on both both,
a particular task and a particular domain.
The Idea Ontology introduced in this research is an application ontology for IT sup-
ported innovation management for services. Consequently, it describes concepts that
depend on the general task of “innovation” as well as concepts that depend on the
domain of “services”.
3.3. Idea Ontology
This section introduces the Idea Ontology and gives a detailed explanation of the
innovation and generic concepts it uses. Figure 2 depicts the ontology’s main mod-
ules.

Fig. 2: Overview of the elements of the Idea Ontology.
Modularity is a key requirement for large ontologies, as it facilitates reusability, main-
tainability, and evolution. Hence, one of our central design goal was to create a
highly modular ontology. We achieved this by incorporating established ontology
specifications to represent the more general meta-data concepts that are associated
with an idea. We re-used for example established ontologies for the representation of
people (Friend of a Friend (FOAF), Brickley, 2007) an entries in blogs or forums
(Semantically-Interlinked Online Communities (SIOC), Bojars, Breslin, 2007). Fur-
thermore, we generalised ideas, documents and comments as more generic innova-
tion resources. Thus, we are able to specify relations to various common meta-
information which are then reused for all innovation-related resources. Specifically,
every such innovation resource has the following generic relations:
• Origin: the application that the resource originates from,
• Rating: a rating mechanism that allows rating of the resource,

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• Person: the creator of the resource,
• Tagging: folksonomy tagging of the resource, and
• Concent: definition of a subject matter of the resources that allows
grouping of ideas or comments by topic.
However, it is important to note that an CoreIdea is the central object that defines an
innovation project and for that purpose draws on other innovation resources such as
documents and community discussions.
3.3.1. Innovation Concepts
Core Idea: To achieve a generic and versatile representation of ideas we chose a
hierarchical design with three layers of textual descriptions for an CoreIdea: a title
which would represent a very short description of an idea (usually no more than 10
words), an abstract which could be a bit longer, and a description that could contain
several pages of text providing a full description of the idea. Thus, our ontology is
able to support very simple tools such as electronic brainstorming where an idea
usually consists of no more than one sentence, up to more advanced tools that allow
longer descriptions. It is also possible to extend the description with resources such
as images, screenshots, or process diagrams. Furthermore, every CoreIdea has an
associated creation date and a version number attached to it. Figure 3 shows the
complete CoreIdea class and its relationships.

Fig. 3: The CoreCore element of the Idea Ontology.
Discussions and Collaboration: Discussions and collaboration, both within and across
organisations, are an important means for developing ideas (cf. e.g. Ahuja, 2000,
Gemunden; Solomo; Hölzle, 2007). With increasing adoption of open innovation
processes and the integration of users into the innovation process the ability to sys-
tematically support discussions and collaboration becomes a key functionality (Ches-
brough, 2006, West, Lakhani, 2008). Consequently, the ability to support comments
has been added to our ontology.
Status: In order to track an idea’s progression throughout a submission, evaluation,
and implementation process it is necessary to have states associated with an idea.
Instances of the status class thus allow tracking ideas. Different status types can be
introduced. For example, “none”, “under review”, “in process”, “implemented”, “al-
ready offered” or others depending on the area of application and the innovation
process in place.
Idea Realisation: To support the full innovation life cycle and to allow for incremental
innovations of existing products and services the link between ideas and their result-

12
ing realisations must be preserved. Moreover, the back-link from a realisation to the
original idea allows evaluating various performance measures. For example, it would
be possible to identify authors of highly successful ideas.
3.3.2. Generic Concepts
User: Each innovation resource has an owner who created the idea, comment or
document. Users are described in the Idea Ontology through a first name, last name,
email address and other fields such as home page or pictures.
Tagging: Tags are keywords or terms associated with or assigned to a piece of in-
formation -in our case innovation resources. Due to their popularity in online commu-
nities and apparent benefits for information browsing (Mathes, 2004, Golder,
Huberman, 2005) a tagging concept has been added to our ontology.
Grouping: Through the use of semantic concepts the topic that is addressed by an
innovation resource can be specified. Thus, it is possible to group resources by a
certain topic and perform topic-oriented searches on the data pool. Through the use
of topic structures such as “narrower” or “broader” it is possible to infer subclasses of
topic.
Tracking the Origin of Contributions: As one of our Idea Ontology’s main goals is to
foster interoperability between various innovation management tools it is necessary
to keep track of the application that a given resource originates from. The Origin
class can be used for this purpose. An Origin describes the tool which an idea has
been generated in, e.g., through the use of an URL. In this way it can be stated that
an idea originates e.g., from a brainstorming tool, an idea portal on the Web, or an-
other application.
Rating: A rating is used to associate values of appraisal given by users for a re-
source. In the innovation domain, rating is of utmost importance as it is a necessary
step for idea evaluation and selection (van Gundy, 1988). A great variety of idea
evaluation and selection methods has been proposed (e.g., van Gundy, 1988) and
new concepts like information markets are investigated for their suitability for idea
evaluation (Stathel et al., 2008). Hence, the rating concept is required to be configur-
able with respect to the rating method and the range of values.
3.4. Benefits of the Ontology-Based Approach
Uschold and Grüninger identify as the three main goals for the use of ontologies (i)
communication between people and organisations, (ii) interoperability between sys-
tems, and (iii) the achievement of reusable components (Uschold, Grüninger, 1996).
This complies very well with our intention: Our Idea Ontology’s main goal is to allow
exchanging knowledge concerning ideas and innovations across system boundaries
as well as to facilitate reasoning based on the ideas collected. The semantic-based
approach offers the following advantages:
1. It formalises the concept of an idea and its associated aspects in a machine-
processable way but, at the same time, provides a not-too-technical view that
still complies well with a human understanding of what makes up an idea.

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2. It provides a data model that can easily be extended with further concepts,
e.g., to meet requirements of further software tools.
3. It provides means to integrate heterogeneous data structures. For example,
different rating schemes can be mapped to each other by defining appropriate
rules.
4. By adding background knowledge about concepts, semantic reasoning can be
applied: Say, we formalise that recycling is related to environment protection –
then we can easily subsume each idea tagged with “recycling” as “environ-
ment protection idea”.
As the number of ideas collected in a database grows, keeping the overview be-
comes more difficult. This holds especially true for open platforms where hundreds of
users provide thousands of ideas. On the other hand the huge amount of ideas offers
new possibilities for innovation management: Lead users can be identified by the
number of ideas provided or the positive feedback these ideas are given by the
community. By mining the idea database’s content, trends monitoring and event de-
tection becomes possible (“since a few month ideas concerning the eco-friendliness
of our product packages are significantly rising”). The semantic approach supports
complex queries in a much more elegant way than traditional databases do by incor-
porating background knowledge about the relations between concepts and instances
in the ontology.
4. Conclusion
This article highlighted the benefits of using an Ontology for innovation management
in services. We argue that open innovation approaches like the introduced idea Web
portals are especially promising within the service sector where a high degree of end-
customer involvement is given per-se and the customer satisfaction plays a role for
continuing business success that cannot be overrated. In detail, this article presented
the design of the Idea Ontology that could be used for this purpose. The primary goal
of an ontology-based approach is to facilitate interoperability between the various
tools and platforms and to support the full life cycle of an idea. Furthermore, the use
of semantic techniques enables advanced management functions. We see benefits
especially in the following areas:
1. clustering ideas by similarity or relatedness,
2. analysing contributors and contributions,
3. integrating idea repositories for content management,
4. information integration and data exchange across tools and platforms, and
5. attaching to social network and facilitating collaborative tools.
Particular emphasis has been put on the support for various community-related fea-
tures such as commenting, tagging, and flexible rating mechanisms. The Idea Ontol-
ogy can act as an enabler for open innovation processes because it provides a tech-

14
nical basis by means of which ideas can systematically be generated, refined, and
evaluated across a wide set of tools and actors within a community.
Future research will exploit the reasoning capabilities of semantically related sub-
jects. In a broader sense the Idea Ontology proposed in this article is a means of
supporting collaborative working environments at the semantic infrastructure layer as
well as a key to further open up innovation processes. Continuing this work we will
relate our work on open innovation to collaboration systems where we have already
defined a reference architecture (Stathel et al., 2008).
Finally, we plan to extend and enhance the ontology presented in this paper as new
requirements emerge. As we want to share this development with the community, the
Idea Ontology as well as sample instances for testing and evaluation purposes are
available at www.ideaontology.org.
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Authors:
Christoph Riedl
Technische Universität München (TUM)
Boltzmannstr. 3
D-85748 Garching
riedlc@in.tum.de
Norman May, Dr.
SAP Research CEC Karlsruhe
Vincenz-Priessnitz-Strasse 1
D-76131 Karlsruhe
norman.may@sap.com
Jan Finzen
Fraunhofer-Institut für Arbeitswirtschaft und Organisation (IAO)
Nobelstrasse 12
D-70569 Stuttgart
jan.finzen@iao.fraunhofer.de
Stephan Stathel
FZI – Forschungszentrum für Informatik
Information Process Engineering (IPE)
Haid-und-Neu-Str. 10-14, D-76131 Karlsruhe
stathel@fzi.de
Torsten Leidig
SAP Research CEC Karlsruhe
Vincenz-Priessnitz-Strasse 1
D-76131 Karlsruhe
torsten.leidig@sap.com
Viktor Kaufman, Dr.
SAP Research CEC Karlsruhe
Vincenz-Priessnitz-Strasse 1
D-76131 Karlsruhe
viktor.kaufman@sap.com
Roxana Belecheanu
SAP Research CEC Karlsruhe
Vincenz-Priessnitz-Strasse 1
D-76131 Karlsruhe
roxana.belecheanu@sap.com
Helmut Krcmar
Technische Universität München (TUM)
Boltzmannstr. 3
D-85748 Garching
krcmar@in.tum.de

18
This research was funded by the German Federal Ministry of Economics and Technology under the promotional reference
01MQ07012 and the German Federal Ministry of Education and Research under grant number 01IA08001A. Part of this re-
search has also been funded under the Laboranova project (IST-5-035262-IP). The responsibility for this publication lies with
the authors.
The information in this document is proprietary to the following THESEUS consortium members funded by means of the Ger-
man Federal Ministry of Economy and Technology: Technische Universität München (TUM), SAP Research CEC Karlsruhe,
Fraunhofer IAO, Forschungszentrum für Informatik (FZI). The information in this document is provided “as is”, and no guarantee
or warranty is given that the information is fit for any particular purpose. The above referenced consortium members shall have
no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may
result from the use of these materials subject to any liability which is mandatory due to applicable law. Copyright 2009 by Tech-
nische Universität München (TUM), SAP Research CEC Karlsruhe, Forschungszentrum für Informatik (FZI), Fraunhofer IAO.