Revolutionizing Semantic Search with RAG and Knowledge Graphs
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. As the scale of data increases in both size and complexity, the need for retrieving relevant information from enormous data repositories is more pressing than ever. Semantic search is a sophisticated approach that goes beyond keyword matching and dives into the deeper context behind a query. Semantic search is critical nowadays to return the essence of the search rather than only exact matches. However, despite the advanced capabilities of semantic search, it faces significant challenges. Traditional algorithms struggle with understanding the nuances of the human language, leading to a superficial level understanding of the user's intent. In addition, the exponential increase in volume and variety of data makes the extraction of relevant information even more challenging. This is where Retrieval-Augmented Generation (RAG) comes into play. RAG is a cutting-edge approach
As the scale of data increases in both size and complexity, the need for retrieving relevant information from enormous data repositories is more pressing than ever. Semantic search is a sophisticated approach that goes beyond keyword matching and dives into the deeper context behind a query. Semantic search is critical nowadays to return the essence of the search rather than only exact matches. However, despite the advanced capabilities of semantic search, it faces significant challenges. Traditional algorithms struggle with understanding the nuances of the human language, leading to a superfi
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