Figure 2.

Pipeline of knowledge graph construction, representation, reasoning, and applications

To construct a knowledge graph, a huge volume of data should be processed, including unstructured, semi-structured, and structured data. Later, knowledge graphs can be constructed either manually or automatically, and the latter method mainly includes three components: knowledge extraction, knowledge fusion, and knowledge refinement. Constructed knowledge graphs can be further used for representation learning and reasoning to support various tasks, such as search, recommendation, and question answering.