Intro
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Modern Knowledge Graphs: Structured Data for the Digital Age
The concept of "knowledge graphs" has roots in scientific literature dating back to the 1970s. However, it was Google's launch of its Knowledge Graph in 2012 that propelled the technology into mainstream daily life. This initiative transformed traditional search results, moving beyond simple keyword matching to provide richer, semantically connected information about people, places, and things directly within search.

Google

Introducing the Knowledge Graph: things, not strings

We hope this will give you a more complete picture of your interest, provide smarter search results, and pique your curiosity.

Modern Knowledge Graphs: Capabilities and Structure
Unlike their ancient predecessors, modern knowledge graphs are digital, scalable systems designed to capture complex relationships across vast datasets. They feature formal schemas that define entity types and the specific relationships between them, enabling sophisticated data integration and inference.
These powerful structures are foundational for AI applications, semantic search, and fields like bioinformatics, where they model intricate biological networks and facilitate drug discovery. Their precise, machine-readable format unlocks deeper insights from interconnected information.

GitHub

GitHub - wcm-wanglab/iBKH: iBKH: The integrative Biomedical Knowledge Hub

iBKH: The integrative Biomedical Knowledge Hub. Contribute to wcm-wanglab/iBKH development by creating an account on GitHub.

Applications of Modern Knowledge Graphs
Modern knowledge graphs are transforming how we interact with information, driving innovation across various industries. Their ability to connect disparate data points uncovers hidden relationships and enables advanced capabilities.
Semantic Search
Enhance search engines by understanding context and meaning, delivering more relevant results than keyword-based searches.
Recommendation Systems
Power personalized recommendations in e-commerce, streaming, and content platforms by mapping user preferences and product attributes.
Fraud Detection
Identify suspicious patterns and connections in financial transactions and networks, significantly improving the accuracy of fraud prevention.
Drug Discovery
Accelerate scientific research by modeling complex biological pathways, drug interactions, and patient data in bioinformatics.
These applications leverage the graph's structure to perform sophisticated queries and infer new insights, going beyond simple data retrieval.

https://github.com/totogo/awesome-knowledge-graph
Knowledge Graphs and AI
Experience a paradigm shift with the latest LLM advancements reshaping knowledge-graph operations. Integrating the versatility, tailorability, and data security of open-source models with the state-of-the-art solutions from renowned players such as OpenAI, Google, or Anthropic, you pave the way for an efficient, top-tier system. This collaborative strategy empowers small teams to quickly develop accurate knowledge structures that effortlessly convert unprocessed data into actionable intelligence.
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