This Google patent outlines a sophisticated system for identifying and verifying relationships between entities and their specific attributes within massive datasets. By employing a multi-layered neural network, the technology analyzes text to determine if a characteristic, such as a person's salary or a city's population, truly belongs to a given subject. The process utilizes five distinct vector embeddings that evaluate sentence structure, linguistic context, and patterns found in similar known entities to infer hidden connections. This methodology allows search engines to construct rich knowledge bases and present structured information even when a direct relationship isn't explicitly stated in a single sentence. For content creators, the patent highlights the importance of consistent attribute modeling and clear syntactic structures to help automated systems recognize and categorize factual data. Ultimately, this innovation enhances the accuracy and depth of search results by building a more comprehensive understanding of how real-world objects and their properties relate.https://www.kopp-online-marketing.com/patents-papers/identifying-entity-attribute-relations