A similarity for new meanings

Abstract

Extraction is a way to get knowledge from information space, such as social networks. One efficient and concise tool for expressing knowledge related to meaning is to involve the concept of similarity. There are several similarity formulations to approach the same thing from the objects but have different tasks according to their functions. However, the measurement results reveal different meanings, although they remain in a mutually supportive position. Therefore, this paper aims to express the different meanings besides new meanings of the similarity function, which are proven based on the difference between the divisors of the similarity formulation. Different measurements of similarities produce different and new meanings with supporting simulation and clustering to manage big data.