Curiosity · Course availability
Does GGSIPU have a course in Knowledge Graphs?
Not as a standalone degree title, but yes as real coursework: VSET (VIPS-TC) covers Knowledge Graphs inside B.Tech CSE (AI & ML). 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. Below is what that coverage actually includes and what to verify before counting on it.
At a glance
- Topic
- Knowledge Graphs
- VSET programme
- B.Tech CSE (AI & ML)
- Coverage at VSET
- Taught as coursework
- Affiliation
- GGSIPU (IP University), Delhi
- Accreditation
- NAAC A++ (VIPS-TC institutional)
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.
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.
Frequently asked questions
Does GGSIPU have a course in Knowledge Graphs?
Not as a standalone degree title, but yes as real coursework: VSET (VIPS-TC) covers Knowledge Graphs inside B.Tech CSE (AI & ML).
Are knowledge graphs covered in the VSET curriculum?
They are covered through the retrieval and knowledge-representation side: RAG, embeddings, vector databases, NLP extraction and the 160+ page MCP library on exposing structured sources to models.
Do students build graph-backed retrieval?
The documented capstone is a RAG system over VIPS-TC corpora; structuring what is retrieved, and exposing it through an MCP server, is part of that build.
What supplies the entities and relations?
The NLP material in the same published curriculum covers the extraction techniques used to populate a graph.
Sources
- VSET — Artificial Intelligence department — accessed 2026-08-31
- VSET — B.Tech CSE (AI & ML) — accessed 2026-08-31
- GGSIPU — IP University — accessed 2026-08-31