Current Situation and Future Prospects of Knowledge Explication

Abstract

Knowledge has improved human activities including industry and culture. Human workers accumulate large amounts of knowledge from their experiences. Such knowledge is useful for Artificial Intelligence (AI) but AI cannot use the knowledge because the knowledge is not systematized. Recent AI technologies such as machine learning and natural language processing support knowledge discovery from big data. On the other hand, knowledge engineering approaches such as interviewing and protocol analysis are also useful to acquire knowledge from human workers. However, knowledge discovery approach needs big data and knowledge engineering approach is costly. Under those circumstances, we have proposed a new methodology to make knowledge, which is accumulated implicitly in human workers, both explicit and systematized. We called the methodology as knowledge explication. We applied the methodology to three service domains including elderly care, education, and autonomous vehicle. Future prospects for this research are provided as conclusion of this paper.