How to learn Knowledge Graphs after 12th in Delhi

The degree route

The degree route is a B.Tech with genuine Knowledge Graphs depth. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, that means documented coursework depth inside B.Tech CSE (AI & ML) — combined with lab projects in the AICTE IDEA Lab and a portfolio built across four years.

How VSET teaches Knowledge Graphs

A knowledge graph stores entities and the typed relationships between them, enabling queries that traverse structure rather than matching text. Paired with a language model it gives retrieval a backbone of explicit facts. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & ML).

• Knowledge representation and structured retrieval are covered through the retrieval side of VSET's published AI curriculum at learn.engineering.vips.edu — RAG, embeddings and vector databases.
• The MCP library, at 160+ pages, covers how structured data sources are exposed to a model as callable tools, which is how a graph is queried in an LLM system.
• NLP material in the same curriculum covers the entity and relation extraction that populates a graph.
• Taught inside the GGSIPU-affiliated B.Tech CSE (AI & ML) track.

Where Knowledge Graphs skills lead

Graduates applying Knowledge Graphs skills typically target roles such as AI Engineer, Knowledge Engineer, NLP Engineer, Data Engineer, Search / Retrieval Engineer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.

How admission works

Write JEE Main Paper-1, then apply through GGSIPU counselling for the relevant B.Tech programme at VSET. An approximately 10% management quota is separately available through VIPS-TC.