Specifying Knowledge Graph with Data Graph, Information Graph, Knowledge Graph, and Wisdom Graph

1. Introduction

There are different kinds of discrete data in the real world we live in. The data cannot be used if they exist only in the discrete form. However, this is not worth worrying as we can simply make the data meaningful by giving a specific environment. Data are processed to be useful and presented to us in the form of information, then we can get a lot of fragmented expressions. With these fragmented expressions, that is, the conception “information” we mentioned above, we can combine multiple information to answer more complex questions about how to do it. By abstracting and converting information and data in a given context and the application of data and information (Bellinger & Castro, 2004), knowledge shows up. Furthermore, comprehensive knowledge of the same category can be use of making favorable judgments, precisely predicting, and smartly planning. Obviously, the utilization of vested knowledge is beyond its literal meaning of the category, which is what we say, “wisdom”. Figure 1 shows the progressive relationship among data, information, knowledge and wisdom. Data existing as discrete elements have no semantics. Information is data after procession of conceptual mapping and relational connection. Users access to information after filtering valuable information and internalize those information into knowledge. When information is adequately assimilated, it produces knowledge which modifies an individual’s mental store of information and benefits his/her development and that of the society in which he/she lives.

Figure 1.

Progressive relationship among data, information, knowledge and wisdom

In our previous work (Duan et al., 2017), we clarified the architecture of Knowledge Graph as a whole and extended the existing concept of Knowledge Graph into four aspects including Data Graph, Information Graph, Knowledge Graph and Wisdom Graph. Shao et al. (2017) proposed to answer the Five Ws problems through constructing the architecture of Data Graph, Information Graph and Knowledge Graph. We clarify the architecture of knowledge graph from Data_DIKW, Information_DIKW, Knowledge_DIKW and Wisdom_DIKW aspects respectively. Correspondingly, we propose to extend the existing expression of knowledge graph in a progressive manner as four basic forms including DataGraph_DIKW, InformationGraph_DIKW, KnowledgeGraph_DIKW and WisdomGraph_DIKW. We propose a DIKW approach to support dynamic semantic modeling through a progressive hierarchy of DataGraph_DIKW, InformationGraph_DIKW, KnowledgeGraph_DIKW and WisdomGraph_DIKW. We define the resources and the four graphs as follows:

• Definition 1: Resource elements (Elements_DIKW).

• Elements_DIKW: = <Data_DIKW, Information_DIKW, Knowledge_DIKW, Wisdom_DIKW >;

• Definition 2: Graph_DIKW. We extend the concept of existing knowledge graph into four parts: DataGraph_DIKW, InformationGraph_DIKW, KnowledgeGraph_DIKW and WisdomGraph_DIKW.

• Graph_DIKW: = (DataGraph_DIKW), (InformationGraph_DIKW), (KnowledgeGraph_DIKW), (WisdomGraph_DIKW).