Why Knowledge Graphs are Important for AI

Why Knowledge Graphs are Important for AI

April 5, 2018

Knowledge graphs allow people and machines to better tap into the connections in their data sets. In fact, every time data is added, the entire data ecosystem is enriched as it is connected to everything else. The contextual value grows exponentially because networks are graphs.

Context requires connections, and knowledge graphs use connected data to understand concepts and infer meaning so the system can better react to an inquiry. Additionally, users do not have to stitch together multiple strings to reach their goals. Meanwhile, knowledge graphs accumulate contextual knowledge with each conversation. This is context for user-centered AI.

Additionally, new connections and new data create the opportunity for data scientists to evolve and refine the algorithms that operate on knowledge graphs, thereby making knowledge graphs vital for modern AI systems.

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Brought to you by Scope e-Knowledge Center, an SPi Global Company, a trusted global partner for Digital Content Transformation Solutions, Knowledge Modeling (Taxonomies, Thesauri and Ontologies), Abstracting & Indexing (A&I), Metadata Enrichment and Entity Extraction.

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