The Future of Knowledge Graph: Will Structured and Semantic Search Become One?

Explore the future of knowledge graphs and the integration of structured and semantic search using large language models and knowledge graphs. Learn about the history of knowledge graphs, the benefits and use cases of structured and semantic search, and the approach of combining text embeddings and vector indexes to generate answers from knowledge graphs.

#embeddings

#vector-search

Apr 25, 2024•8m read time•From medium.com

Table of contents

What LLM & Graph May Bring to the Future of Knowledge Graphs 1. 1960s and 1970s — Semantic Networks 2. 1980s — Frames and Expert Systems 3. 1990s — Ontologies and RDF:4. 2000s — SPARQL and DBpedia:5. 2010s — Rise of Commercial Knowledge Graphs RDF Triple Stores vs. Labeled Property Graphs: What's the Difference? - Graph Database & Analytics 6. 2020s and Beyond: The Era of Neural Knowledge Graph?Text Embedding — What, Why and How?The Movie Graph Enhance Semantic Search of Text Embeddings through Collaborative Filtering over A Knowledge Graph The Design Adding Q&A Features to Your Knowledge Graph in 3 Simple Steps English to Cypher with GPT-3 in Doctor.ai LangChain Cypher Search: Tips & Tricks Embeddings Are All You Need Save & Index Text Embeddings in Neo4j Neo4j's Vector Search: Unlocking Deeper Insights for AI-Powered Applications - Graph Database &…Generate Answers Test Results of Sample Questions

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