Knowledge Graph is a structured representation of knowledge that captures relationships between entities, concepts, and facts in a graph-like structure. It serves as a powerful tool for organizing and understanding information in a more interconnected and contextual manner. The essence of a Knowledge Graph lies in its ability to go beyond traditional databases and flat data representations by emphasizing the connections between pieces of information, enabling more comprehensive insights and facilitating more sophisticated querying and reasoning.
The structure of a Knowledge Graph typically involves nodes representing entities and edges representing the relationships between them. These relationships can range from simple associations to more complex attributes and hierarchical connections. Knowledge Graphs can be manually curated or automatically generated from various sources, such as text documents, databases, and semantic web data. They find applications in a wide array of fields, including natural language processing, search engines, recommendation systems, and question-answering, among others. By capturing the inherent relationships in data, Knowledge Graphs enhance AI systems’ ability to interpret and respond to user queries, make contextually informed decisions, and provide a deeper level of understanding of the interconnected nature of the world’s information.
« Back to Glossary Index
