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Knowledge Graphs for AI Engineers: Designing KG embeddings, SPARQL and Cypher patterns, and integrating graphs
Paperback
Series: Applied LLM Systems: Production Patterns for Agents, Context, and Knowledge Graphs, Book 4
Publisher: Independently Published
Published: Sep 17 2025
Pages: 210
Weight: 0.82
Height: 0.44 Width: 7.00 Depth: 10.00
Language: English
What's inside:
- Ontology and schema blueprints for common domains.
- ETL patterns and pipeline templates for reliable KG ingestion.
- Entity linking, canonicalization, and de-duplication best practices.
- KG embeddings, graph neural net overviews, and conversion to vector indexes.
- Query examples in SPARQL and Cypher tuned for retrieval and analytics.
- Integration patterns for combining graph queries with vector search in RAG stacks.
- Reasoning and rule-engine examples for inference and business rules.
- Visualization and analytics workflows for KG insights and reporting.
- Governance: access control, data provenance, compliance, and lifecycle management.
- Scaling, replication, and operational playbooks for production KGs.
- Knowledge engineers, data engineers, ML engineers, and technical product teams building graph-backed AI.
- Enterprises needing explainability, versioning, and governance in their AI pipelines.
- Research teams and tool-builders interested in KG embeddings and hybrid retrieval.
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