Embedded to Interpretive: A Paradigm Shift in Knowledge Discovery to Represent Dynamic Knowledge

This position paper purports a novel extension for knowledge extraction and interpretation by exploring the existence of knowledge via interdisciplinary routes. The existing knowledge discovery mindset operates in the embedded paradigm which encompasses the premise that knowledge is embedded in data and should be discovered. Hence, at present, data representation and computational approaches use structural properties of data to discover new knowledge. The limitation of this perspective is that it leads to finding a possible existence rather than possible knowledge within a context. As a solution, we propose a new perspective to knowledge discovery, the interpretive paradigm. In this approach, we argue that knowledge in its true definition is interactive, even though the structural properties play a significant role in data representation and transformation. Thus, knowledge is nonsensical in the existence of absolute nature. Knowledge is a construct by the existence of a schema of associated other constructs. Given this premise, data becomes a signal to an interpreter but not the interpretation itself. Hence, multiple interpretations can be accommodated from the same data depending on the schema that is used to interpret them. The knowledge of the interpretive paradigm is in constant evolution as it is constructed (as opposed to mining in the embedded paradigm) at the interaction of the signal and the interpreter. We believe that the proposed paradigm will bring a new perspective to knowledge discovery methods. This will enable systems to adopt diversified knowledge that is unique to a variety of representations of the knowledge the society, such as different stages of an individual, groups, cultures, and so on.