- A knowledge graph, also known as a semantic network, represents a network of real-world entities such as objects, events, situations or concepts.
- A knowledge graph is a database that allows AI systems to deal with complex, interrelated data.
Businesses are increasingly using AI applications to make decisions. However, AI systems have not yet been able to reach their full potential as reliable solutions to complex problems. Neither AI nor the knowledge graph are new technologies until recently when they have matured and joined forces. While data and computing power have fueled their rise over the past decade, it is the powerful combination of the two that has sparked interest in contextual AI.
The concept of knowledge graph
A knowledge graph, also known as a semantic network, represents a network of real-world entities—such as objects, events, situations or concepts—and illustrates the relationship between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term knowledge “graph.”
A knowledge graph is made up of three main components: nodes, edges, and labels. Any object, place, or person can be a node. It is also worth noting that definitions of knowledge graphs vary, and there are studies that show that knowledge graphs are no different from knowledge bases or ontologies.
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Knowledge Graph construction gets big boost from AI
Data is everywhere. AI has become invaluable in storing and organizing vast amounts of information – using the “knowledge graph.” The Knowledge graph is a database that allows an AI system to process complex, interconnected data. It stores information as a network of data points connected by different types of relationships. The knowledge graph powers Internet search, recommendation systems, and chatbots.
Over the past decade, deep learning and encoder-decoder transformer architectures have fundamentally changed the field of artificial intelligence, dramatically improving knowledge sensing techniques. Neural networks can now use network scale data to learn language models in a completely unsupervised manner, storing vast amounts of background knowledge. Most data in an enterprise usually exists in the form of text documents. Therefore, building a knowledge graph based on this data requires customized information extraction (IE) analysis for entity identification and relationship extraction. This process is also known as knowledge base filling (KBP), and one of its tasks is to fill slots.
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The application and potential of AI combined with knowledge graph
Education: Education is of great significance to the development of human society. Much research has focused on deploying intelligent applications to improve the quality of education. Specifically, in the age of big data, data processing becomes a challenging task due to the complexity and unstructured nature of educational data. Therefore, intelligent education systems tend to apply structured data, such as knowledge graphs. Some knowledge graph-based applications support the educational process, with a particular focus on data processing and knowledge dissemination.
Scientific Research: In addition to building academic knowledge maps, many researchers use knowledge maps to develop a variety of applications that benefit scientific research. A scientific publication management model is proposed to help non-researchers learn approaches to sustainability from research thinking. They built an academic network based on the Knowledge graph to manage scientific entities. Scientific entities, including researchers, papers, journals, and organizations, are interrelated in terms of their properties.
Social networking: With the rapid development of social media such as Facebook and Twitter, online social networking has penetrated into human life and brought many benefits such as building social relationships and convenient access to information. Various social knowledge graphs are modeled and applied to analyze key information from social networks. These knowledge graphs are typically constructed based on people’s activities and their posts on social media, which are applied to numerous apps with different functions.
