Knowledge Graph refers to a knowledge base that uses a graph structure to represent information. It is a database that stores knowledge in the form of interlinked entities and their attributes. The concept was introduced by Google in 2012 to enhance search results and provide more accurate and relevant information to users. In a knowledge graph, data is organized into nodes (entities) and edges (relationships). Each node represents a distinct entity, such as a person, place, or concept, and is associated with various attributes or properties. Edges represent the connections or relationships between entities. The key characteristic of a knowledge graph is its ability to understand context and semantic relationships. This allows it to go beyond simple keyword matching and provide more comprehensive search results. For example, when searching for "Barack Obama," the knowledge graph can display not only basic information about him but also related entities like his family members, political career, speeches, and other relevant topics. Knowledge graphs are built using various techniques such as natural language processing, machine learning, and semantic analysis. They can be created from structured data sources like databases or from unstructured data like web pages. Knowledge graphs have applications in various domains including search engines, virtual assistants, recommendation systems, and question-answering systems. ## Databases that supports Knowledge Graph Some of the database technology vendors that support Knowledge Graph include: 1. [[Neo4j]]: Neo4j is a graph database management system that provides support for building and querying Knowledge Graphs. It offers a flexible schema, expressive query language (Cypher), and powerful graph algorithms. 2. Amazon Neptune: Amazon Neptune is a fully managed graph database service provided by AWS. It is optimized for storing and querying highly connected data such as Knowledge Graphs. Neptune supports various graph models, including property graphs and RDF graphs. 3. Stardog: Stardog is a knowledge graph platform that combines a highly scalable RDF graph database with powerful reasoning capabilities. It supports semantic data integration, virtual graphs, and advanced queries using SPARQL. 4. Virtuoso: Virtuoso is an enterprise-grade triplestore database that supports RDF-based Knowledge Graphs. It provides support for SPARQL, RDBMS-style SQL queries, and various web services standards for data integration. 5. AllegroGraph: AllegroGraph is a high-performance RDF graph database designed for building and querying large-scale Knowledge Graphs. It offers advanced features like geospatial indexing, full-text search, and reasoning capabilities. 6. Oracle Spatial and Graph: Oracle Spatial and Graph is an option for Oracle Database that provides native support for managing spatial data as well as property graphs and RDF graphs. It allows users to store, query, and analyze Knowledge Graphs alongside other types of data. 7. Microsoft Azure Cosmos DB: Azure Cosmos DB is a globally distributed multi-model database service offered by Microsoft. It includes support for creating and querying graph databases using the Gremlin query language, making it suitable for building Knowledge Graphs. These are just some of the vendors providing technologies specifically designed to support Knowledge Graphs. There may be other vendors or technologies available in the market as well. # Conclusion Overall, knowledge graphs play a crucial role in organizing vast amounts of information in a structured manner, enabling better understanding of complex relationships between entities and delivering more accurate and contextualized results to users. # References ```dataview Table title as Title, authors as Authors where contains(subject, "knowledge graph") or contains(subject, "Knowledge Graph") ```