Towards Granular Knowledge Structures: Comparison of Different Approaches

Nowadays, it is becoming essential to extract knowledge from diverse, large scale data-sources. An effective approach to make knowledge accessible and providing the necessary means for efficient reasoning to take place is through the use of knowledge graphs. The process of building knowledge graphs is usually focused on generating meaningful representations. Hence, applying structure to it, which takes into account the existence of different knowledge domains, their depth and breadth is mostly disregarded. This particular shortcoming leads to a loss of valuable information that could else be harnessed to provide various additional functionalities to an application. In other words, enhancing knowledge graphs in such a way that they can be explored similar to how Google Maps presents the world to us. By zooming in and out, different relevant aspects become visible while unnecessary noise is blended out. Granular computing by itself is more of a theorem that highlights potential benefits from the application of fuzzy and hierarchical structures. Little is said on how a potential granular knowledge graph can be built and which existing clustering algorithms can be used for this task. As such, this paper aims to provide (1) an in-depth view of which critical requirements need to be met by an algorithm to establish a granular structure, (2) the process for how different commonly used algorithms are coping with them, as well as (3) an overview that outlines the different steps in the process of establishing a granular knowledge structure. Two approaches are identified as the most promising ones: for low dimensional data, a Growing Hierarchical Self-Organizing Map (with its adaptive behaviour) and, in case of data with high dimensionality, one approach from the projective clustering family, thanks to their capability of finding strong correlation in sub-spaces of the original dimensions.