Knowledge plays a vital role in human existence and development. Learning and representing human knowledge is crucial in AI research. While humans can understand and analyze their surroundings, AI systems require additional knowledge to obtain the same abilities and solve complex tasks in realistic scenarios. To support these systems, we have seen the emergence of many approaches for representing human knowledge according to different conceptual models. In the last decade, knowledge graphs have become a standard solution in this space and a research trend in academia and industry.  

Knowledge graphs are defined as graphs of data that accumulate and convey knowledge of the real world. The nodes in knowledge graphs represent the entities of interest, and the edges represent the relations between the entities. The proposed knowledge graphs have recently been widely employed in various AI systems, such as recommender systems, question-answering, and information retrieval. They are also widely applied in many fields, such as education and medical care to benefit human life and society. 

Knowledge Graphs for AI Systems 

Knowledge graphs bring a wide set of advantages for improving the functionalities of AI Systems. However, the application of knowledge graphs in these systems is not widespread. Mentioned following are the advantages that knowledge graphs bring for recommender systems, question-answering systems, and information retrieval tools: 

• Recommender systems: Recommender systems are fruitful solutions to the information explosion problem and are employed in various fields to enhance user experience. There are traditional recommender systems and knowledge graph-based recommender systems. Compared to the former type, the lateral one has benefits such as better data representation. Also, information about new items and users can be obtained through the relations between entities within knowledge graphs in knowledge graph-based recommender systems. And the reasoning process can be easily illustrated by the propagation of knowledge graphs. 
• Question-answering Systems: Question answering is one of the most central AI services, which aims to search for the answers to natural language questions by analyzing the semantic meanings. Knowledge graph-based question-answering systems typically analyze the user question and retrieve the portion of knowledge graphs for answering. The answering task is facilitated using similarity measures or by producing structured queries in standard formats. Compared to traditional question answering, knowledge graph-based question-answering systems have increased efficiency and multi-hop question answering. 
• Information Retrieval: Compared to traditional information retrieval, knowledge graph-based information retrieval has a semantic representation of items, high search efficiency and accurate retrieval results. 

Application and potentials 

The knowledge graph shows potential in four major domains- education, scientific research, social networks and healthcare. Although some researchers try to use knowledge graphs to develop beneficial applications in other domains, such as finance, the knowledge graph-based intelligent service in these areas is relatively obscure and still needs to be explored. 

In education, the quality of offline school teaching is of vital importance. Therefore, several knowledge graph-based applications focus on supporting teaching and learning. Knowledge graph-based intelligent applications can deal with complicated educational data, making offline and online education more convenient and efficient. In addition to constructing academic knowledge graphs, many researchers also take advantage of knowledge graphs to develop various applications beneficial to scientific research.  

Furthermore, with the rapid growth of social media such as Facebook and Twitter, online social networks have penetrated human life and brought plenty of benefits, such as social relationship establishment and convenient information acquisition. Various social knowledge graphs are modelled and applied to analyze the critical information from the social network.  

Similarly, with medical information explosively growing, medical knowledge analysis plays an instrumental role in different healthcare systems. Therefore, research focuses on integrating medical information into knowledge graphs to empower intelligent systems to understand and process medical knowledge quickly and correctly. 

Conclusion 

Graphs have always been important in the computational sciences, mathematics, and artificial intelligence (AI). Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten years. Building on a storied tradition of graphs in the AI community, a KG may be defined as a directed, labelled, multi-relational graph with some form of semantics. This has been fuelled by the increased publication of structured datasets on the Web and well-publicized successes of large-scale projects such as the Google Knowledge Graph and the Amazon Product Graph. 

Published in Springer Link.