Knowledge Graphs & Vector Systems
Build retrieval systems that combine knowledge graphs, vector search, and hybrid ranking for entity-grounded AI.
Duration
6 weeks · 8-10 hours/week
Level
Intermediate
Delivery
Online
Status
Open for enrollment
Why this technology matters.
Knowledge graphs and vector systems are the two complementary memories of modern AI: vectors find things that mean similar things, and graphs record the exact entities and relationships between them. Together they matter now because pure vector search hallucinates connections while pure rules cannot handle fuzzy language — hybrid retrieval grounds answers in both meaning and fact.
They are used by support teams, retailers, and research groups to power entity-grounded search, recommendations, and retrieval pipelines that need both semantic similarity and precise relationships. They solve fuzzy matching plus structured reasoning, but they do not solve missing or dirty source data — a graph built on stale records still returns stale answers, and embeddings cannot invent knowledge that was never ingested.
By the end you will be able to build a vector search index with hybrid ranking, an entity-grounded retrieval pipeline that joins graph traversals with semantic recall, and a knowledge-backed Q&A system that cites its sources.
Why this course exists
The gap is between a toy semantic-search demo over clean sample data and a production retrieval system that stays accurate over messy, changing entity data at scale. The course teaches the arc from Model embeddings to Context chunking to Retrieval with hybrid ranking to Tools over graph stores to Evaluation of groundedness, so students can ship retrieval that is both flexible and trustworthy.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python and REST APIs
- Basic familiarity with embeddings and databases
- Understanding of data modeling concepts
Technologies & tools
Skills you'll gain
A 6 weeks arc, module by module.
- Module 01
Week 1 — Retrieval beyond vectors: when graphs help
- Module 02
Week 2 — Knowledge graph modeling and ingestion
- Module 03
Week 3 — Vector indexes and hybrid search
- Module 04
Week 4 — Entity resolution and grounding
- Module 05
Week 5 — Evaluating graph-augmented retrieval
- Module 06
Week 6 — Capstone: a grounded retrieval system
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Model entities, relations, and constraints for graph-grounded retrieval.
- Operate vector stores with chunking, embeddings, and freshness controls.
- Fuse graph and vector signals with hybrid search and re-ranking.
- Ship an entity-grounded retrieval system with measurable accuracy gains.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Entity-grounded movie knowledge graph
- Project 02
Vector search index with hybrid ranking
- Project 03
Graph-augmented retrieval API
- Capstone
Entity-grounded AI retrieval system with graph plus vector search
Speak the language first.
- Vector embeddings
- Numeric representations of text that place similar meanings close together so search can find related content.
- Vector similarity search
- Finding the stored items whose embeddings are closest to a query, typically with cosine similarity or a similar measure.
- Knowledge graphs
- Databases of entities and their relationships, such as people linked to organizations, that support precise factual lookups.
- Hybrid retrieval
- Combining keyword search, vector search, and graph lookups so each method covers the others' blind spots.
- Hybrid ranking and fusion
- Merging multiple ranked result lists into one ordering, often with weighted scores or reciprocal rank fusion.
- Entity linking
- Matching a name in text to the correct unique entity in the graph, resolving ambiguity between same-named things.
- Chunking for retrieval
- Splitting documents into passages sized for embedding so each chunk carries enough context to rank and cite well.
- Graph traversal queries
- Queries that hop across relationships, such as finding a product's supplier's suppliers, to answer multi-step questions.
- Entity-grounded generation
- Generating answers constrained to retrieved entities and facts so outputs stay tied to verified sources.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Bad chunking returns unusable passages
Inspect top retrieved chunks for truncation and overlap, then adjust chunk size and overlap and re-index a test corpus before full rebuild.
Entity ambiguity pollutes graph results
Add disambiguation using context and type filters, review conflated entities, and split merged nodes with corrected aliases.
Vector search misses exact names and codes
Add keyword search alongside vectors with hybrid fusion, and boost exact-match fields for identifiers and proper nouns.
Stale embeddings after document updates
Version the embedding pipeline, re-embed changed documents on update events, and monitor index freshness with a lag dashboard.
Slow queries at scale
Profile whether the bottleneck is embedding, search, or graph hops, then tune index parameters, add caching, and limit traversal depth.
Answers cite wrong entities
Require the generator to cite retrieved node IDs, validate citations before display, and fall back to retrieved snippets when validation fails.
Before you move on, you should be able to
- Explain how embeddings, vector search, and graphs complement each other
- Design a hybrid retrieval pipeline for entity-grounded questions
- Build a knowledge graph with entities, relations, and linked text
- Evaluate retrieval quality with recall and ranking measures
- Diagnose chunking, linking, and ranking failures systematically
- Deploy a retrieval service with caching and freshness monitoring
- Document indexing choices and their effect on answer quality
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
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