Kernel-Level Semantic Search with Knowledge Graphs

Various types of knowledge exist across individuals, processes, and tools. The chief objective of Knowledge Graph (KG) is to aggregate the data into graph format, ensuring that it remains manageable, non-corrupted, scalable, and easily discoverable.At its core, a KG is a structure where each node represents real-world entities and edges logically depict the relationships between the nodes. The graph can be directed or un-directed, depending on the organization’s needs. Our approach involves a directed graph that also includes backward edges!The end objective of KG is to operationalize Knowledge (a piece of information) at the kernel level and make it available to users when they feed specific queries to the graph. The output should be the most relevant and concise response available, neither too lengthy nor too brief.