Enterprises are finding new ways to incorporate knowledge graphs into their operations. Kathleen Walch, principal analyst, managing partner and founder of Cognilytica, analyzes the various use cases for knowledge graphs in enterprises.
When used correctly, knowledge graphs can make the natural language understanding part of natural language processing smarter. With the capability to encode relationships between words and their meaning, knowledge graphs can enhance conversation and interaction. These graphs enable voice assistants to answer questions by mapping the questions to an organized set of information. Knowledge graphs that represent ontologies and discovered relationships between entities help enterprises make sense of various data sources. They improve efficiency in content management and search by filtering and recommending relevant information for each user and providing more personalized search features.
When used by machine learning systems to document their decision flows, knowledge graphs can add more transparency to the AI decision-making process. For these applications, knowledge graphs have particular value because they explicitly identify all entities and their relationships to each other, thereby making the AI-decision making process explainable.
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