Abstract
Automatic creation of concepts is important for various situations. Previous re-
searches in the conceptual blending and the concept invention proposed the cognitive
models which represent the process by which people combine concepts and those rela-
tionships. However, those researches do not allow one to create new concepts automat-
ically in the real world, where there are innumerable notions and the meanings of them
are time-varying. Because the previous models can not discover which notions should
be combined to create successful concepts, it is necessary for a user to find an appro-
priate combination of notions. There are approximately 50 million combinations in the
business domain. Therefore, we propose a novel model representing concept creation
processes, which makes automatic creation of new and successful concepts possible even
in such a real world setting. We formalize the concept creation process as discovering
new connections between existing concepts and it can be mathematically represented
using the chronological change of the semantic networks. The data of the input and
output of this process can be built using a large document set. Hence, machine learning
technique can reveal a law underlying the concept creation process. After extracting
such a law, the machine learning model can provide new concepts in accordance with
its law. In experiments, we evaluated the validity of this approach using real successful
concepts and document sets, and created new concepts in food category.
