Knowledge graphs are a powerful way to represent and store information in a graph format, where nodes represent entities (such as people, places, things, concepts) and edges represent the relationships between these entities. This structure allows for efficient querying, linking, and analysis of data across different domains, enabling more sophisticated and context-aware applications in AI. Knowledge graphs underpin many modern AI systems, providing a structured framework to enhance search engines, recommendation systems, natural language processing tasks, and semantic analysis by offering a comprehensive view of relationships and entities. They facilitate the integration of heterogeneous data sources and support inferencing, allowing AI systems to derive new insights and knowledge.
The concept of a knowledge graph was popularized by Google in 2012 when they introduced their "Knowledge Graph" to enhance their search engine's results with semantically structured knowledge. However, the underlying principles and technologies, like semantic networks and ontologies, have been explored in artificial intelligence and knowledge management since the 1960s and 1970s.
While no single individual is credited with inventing knowledge graphs, they are the result of decades of research in semantic networks, ontologies, and the semantic web. Tim Berners-Lee, the inventor of the World Wide Web, has been influential in pushing forward the concepts of the semantic web, which are foundational to modern knowledge graphs. The development and popularization of knowledge graphs have been a collective effort by numerous researchers and organizations, including major technology companies like Google, which played a significant role in bringing knowledge graphs into the public consciousness.