A Proposal for a Standard With Innovation Management System

Ontologies Enable Innovation
Jussi Kantola 1 and Hannu Vanharanta 2
1 Department of production, University of Vaasa, Vaasa, Finland
2 Industrial Management, Tampere University of Technology, Pori, Finland
Jussi.kantola@uwasa.fi
Hannu.vanharanta@tut.fi
Abstract: In this article we explore a new kind of way to look at innovation based on the idea of “knowledge
increments”. We use an existing ontology-based and fuzzy logic based approach as the guideline to exploring
what kind of knowledge increments there are and how we can systematically expand our knowledge base on
several levels using these knowledge increments. We attempt to show that these knowledge increments enable
us to innovate on different levels, i.e. to introduce something new or better than before. The implication of this
new proposed approach could be that we can develop systematic multi-knowledge-layer support systems for
innovation processes and for innovators.
Keywords: knowledge asymmetry, innovation scope, knowledge increment, evolute system, ontology, fuzzy
logic
1. Introduction
Innovation is about creating something new or better than before, i.e. a process, product, service,
technology or a combination of them. Typical classifications of innovation are by type or category, see
for example (Subramanian and Nilakanta, 1996). In this article, we focus on the knowledge that is the
base for innovation. Nonaka and Takeuchi have described how interplay between tacit and explicit
knowledge contributes to new knowledge creation (c.f. Nonaka and Takeuchi, 1995). The assumption
in this article is that by systematically expanding the knowledge base, we ultimately increase the
chance of innovation. We examine the scope of “knowledge increments” and show how these
increments enable us to innovate. As the guideline for formulating this examination, we have used the
Evolute system (Kantola et al., 2006; Kantola, 2009), which is a generic system for developing and
utilizing ontologies for management and development purposes in different contexts. We attempt to
show how knowledge increments expand the knowledge base, and to explain how innovation is
enabled on different knowledge levels. We hope that this will allow us to understand and manage
innovation from a knowledge base perspective on different levels simultaneously. We examine the
different scopes of innovation based on different aspects of ontology, the ontology development
process and ontology-based resource management. Basically, the innovation scope varies from small
detailed innovation to large system level innovation. The knowledge increments enable us to link the
knowledge pieces together and thus expand the knowledge base to innovate more widely. In the
future, we may need a systematic multi-knowledge-scope management support for innovation.
First, we explain the theory needed to understand these scopes according to the Evolute approach.
The first step is to describe ontology and how it can be used to specify any knowledge domain. The
second step is to make the domain ontology available to people by presenting it using natural
language that can be perceived and understood by everybody. This can be done mathematically with
the help of fuzzy sets and fuzzy logic. The third step is to integrate different viewpoints to the domain
ontology using the Evolute system. The fourth step is to make the approach continuous to see the
dynamics in knowledge domains. In the following sections we will describe these steps in more detail
with practical examples, and finally we will discuss why and how this kind of approach can ultimately
contribute to knowledge creation and innovation. We will also discuss the limitations and further
research into this proposed approach.
2. Ontologies
Ontology explicitly specifies the conceptualization of a domain (Gruber, 1993). Conceptualization is
an idea of (part of) the world that a person or a group of people may hold (Gomez-Perez, 2004).
Ontologies define the common words and concepts (meanings) that describe and represent an area
of knowledge (Orbst, 2003). They thus represent a method of formally expressing a shared
understanding of information (Parry, 2004). The main components of an ontology are classes
(concepts), relations (associations between the concepts in the domain) and instances (elements or
individuals in the ontology) (Gomez-Perez, 2004). Ontologies have become important in many fields,
such as knowledge management, information integration, co-operative information systems,
358