1. According to the context provided, knowledge graphs are essential tools that provide a unified view of data distributed across various locations, helping to break down data silos and centralize information for easier access. They are used to stitch together disparate data sources into a data fabric, enabling connections and relationships to be understood. Knowledge graphs are crucial for unifying data regardless of location, enabling real-time access across multiple applications within an organization. 2. Knowledge graphs leverage semantic modeling and automated management infrastructure to represent data with a flexible and reusable semantic model. They are focused on efficient data strategy by creating a structured way to represent data natively stored in different structures. Semantic technology allows for incremental investments in building out models and reusing them over time. This approach helps provide predictive abilities through enriching data with machine learning and automating tasks to reduce manual effort in managing data effectively. 3. Knowledge graphs are seeing increased adoption in AI applications, especially in the context of generative AI and large language models, where their ability to represent complex relationships is crucial for providing meaningful insights. They are used to capture knowledge declaratively and independently of technology, enabling businesses to create richer semantics and compatibility with modern data stacks. Graph databases, especially in the form of knowledge graphs, are known for their ability to efficiently map relationships, support semantic layers, and power intelligent applications with deeper contextual relevance.