![](bg6.png)
Some of the results of the experiments are shown
in Figure 2. We remark that there are two types of
projects. Sometimes, we obtain only one similar gen-
eralized feature for each specific feature (projects 1
and 2). But there are times, where two same values of
semantic similarity are obtained for the same specific
feature (projects 3, 4 and 5).
The results are encouraging if we compare with
the real solving process. For the first type of projects,
we can obtain the exact inventive principles obtained
manually by the students. For the second kind, we get
more inventive principles compared with the manual
work.
As stated above, we verify that our method can
facilitate the task of looking for inventive principles
efficiently and accurately.
Figure 2: The results of the experiments.
6 CONCLUSIONS
The gradual development of inventive design tech-
niques provokes that numerous knowledge sources
are available for experts to solve inventive problems
in different technical and non-technical fields. In real-
world problems, most of the times, the problems are
established in terms of parameters that are inherent
to the artefact that is being developed, and there is
a semantic gap to fill between those parameters and
the generalized ones. An abstraction effort needs to
be provided to choose the best generalized parame-
ter, and in this way, be able to use the contradiction
matrix.
In this paper, we present the inventive principles
ontology we have established as a support for our ap-
proach. According to this ontology, we propose to
measure the semantic distance between the parame-
ters intervening in the contradiction and the 39 gen-
eralized parameters, to help the user fill that semantic
gap and facilitate the process of using the contradic-
tion matrix.
In the future research, we need to improve our
method of semantic similarity calculation to adapt
to the semantic mapping among different knowledge
sources, such as inventive principles and inventive
standards.
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