Knowledge graphs have emerged as a compelling abstraction for organizing the world’s structured knowledge, and as a way to integrate information extracted from multiple data sources. KM World brought this interesting news to our attention in their article, “Turning data into gold: Knowledge graphs, AI, and machine learning.”
Knowledge graphs sit at the epicenter of artificial intelligence (AI) and machine learning, due in part to their highly contextualized understanding of the relationships between data, enterprise knowledge and the terms that populate both. Plus they have credibility.
Knowledge graphs are also known as semantic networks in the context of AI. They have been used as a store of world knowledge for AI agents since the early days of the field, and have been applied in all areas of computer science.
Knowledge graphs are designed for interoperability and linking together all data via uniform standards, compared to property graphs, which simply function as silos.
At the end of the day, content needs to be findable, and that happens with a strong, standards-based taxonomy. Access Innovations is one of a very small number of companies able to help its clients generate ANSI/ISO/W3C-compliant taxonomies and associated rule bases for machine-assisted indexing.
Melody K. Smith
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.