Recent advances, key techniques and future challenges of knowledge graph

The Google knowledge graph is a knowledge base used by Google and its services to enhance the search engine's results with information gathered from a variety of sources. Since its inception by Google to improve users' quality of experience of the search engine, the knowledge graph has become a term that is recently ubiquitously used in medical, education, finance, e-commerce and other industries to promote artificial intelligence (AI), which evolves from perceptual intelligence to cognitive intelligence. As a branch of knowledge engineering, a knowledge graph is based on the semantic network of knowledge engineering, and it combines the latest advancements achieved in machine learning, natural language processing, knowledge representation, and inference. Both academia and industries are showing keen interest in AI, and several studies are in progress under promotion of big data. With its powerful semantic processing and open interconnection capabilities, the knowledge graph can break the data isolation in different scenarios, and can generate application value in intelligent information services such as intelligent search and recommendation, intelligent question answering, and content distribution networks, thereby making information services more intelligent. The state of the art of knowledge graph technologies is outlined by introducing a process of building a knowledge graph. A knowledge graph is a structured representation of facts, consisting of entities, relations and semantic descriptions. A comprehensive summary of the overall lifecycle technologies of the knowledge graph is provided, including knowledge extraction, knowledge fusion, knowledge reasoning, and knowledge application. But the focus is on knowledge fusion and knowledge reasoning. Entities, relations, attributes, and other knowledge elements can be extracted from existing structured, semi-structured, unstructured data sources, and websites given in encyclopedia using knowledge extraction. With knowledge fusion, the ambiguity between referential items such as entities, relations, and attributes can be eliminated, and a series of basic facts can be obtained. The final knowledge base is formed through ontology extraction, knowledge reasoning and quality evaluation. Following the three steps of knowledge extraction, knowledge fusion, and knowledge reasoning, it can iteratively update the knowledge graph and realize full process automation knowledge acquisition, such as realizing the automatic extraction, automatic association and fusion, automatic processing of fragmented Internet knowledge, and realizing automatic linking of entries and auxiliary functions of entry editing. Finally, the future directions and possible challenges of the knowledge graph are discussed.