# Research of Personalized Recommendation Technology Based on Knowledge Graphs
## 2 Related Works  
Knowledge graph: In the 1970s, expert systems appeared. They use knowledge and reasoning processes to solve difficult and professional problems. This is the earliest knowledge graph. Subsequently, with the continuous development of linked open data, there are many knowledge acquisition methods based on information extraction technology, such as open domain information extraction OIE and Nell [6]. Google first put forward the concept of knowledge graphs in 2012, and applied knowledge graphs in its search business in the same year, which achieved good results. After that, Sogou in China also applied the knowledge graphs to its own search business. At present, knowledge graphs are constantly applied in various fields, which has brought far-reaching influence, such as Freebase, DBpedia, YAGO, etc. Recently, the knowledge graphs of COVID-19 has been jointly constructed by NCBI, OpenKG and a number of scientific research institutions in China. It has been made public to the world, which provides great help for the global virus research and treatment [7]. At present, the key technologies of knowledge graphs involve information retrieval, data mining and natural language processing. In recent years, the methods are mainly divided into knowledge driven and data driven. Knowledge driven combines the knowledge and experience of experts to build knowledge systems in a specific field, and expands to other open fields through accumulation. Data driven takes mathematical statistics as the theoretical basis. It takes large-scale data as the driving force, and automatically obtains relevant knowledge through machine learning, data mining and other technologies. The construction of knowledge graphs is divided into several steps, which involve entities recognition, relations extraction and attribute correction. Among them, the object of entity recognition is to recognize entity information from text data. At present, there are three main methods to complete the task, namely, rule-based method, machine learning method and hybrid method. The purpose of relations extraction is to connect all entities and extract their association information to form a complex knowledge network. In general, knowledge graphs contain a large amount of valuable information. Through the organization and management of unstructured data, the value of data can be deeply explored. Furthermore, the knowledge network can be vividly displayed by combining machine learning algorithms. Therefore, more and more experts begin to do research on knowledge graphs. Meanwhile, with the rapid development of machine learning, entity recognition based on machine learning methods has become a research focus in recent years. As it was very time-consuming and labor-intensive to extract relationships by manual methods, more and more researchers now use machine learning methods to replace manual labor, which greatly improves the efficiency of knowledge graph construction. The knowledge graph has a wider range of applications. Recommendation system: The recommendation system was born in the 1990s. At that time, experts led by [PERSON] proposed a user based collaborative filtering algorithm to recommend news to users. Then Xerox company improved it and applied it to the email filtering system. After that, the recommendation system has been widely used in industry. Early collaborative filtering algorithms can be divided into two categories: user-based and item-based [8; 9]. At that time, PHOAKS, Typestry, etc., which were very popular at the time, all used this method to build recommendation systems [10]. In recent years, as deep learning algorithms have made remarkable achievements in computer vision, autonomous driving, virtual reality and other fields, recommendation models based on deep learning have become a major research hotspot in the recommendation field.