Creativity · Projects
Knowledge Graphs projects for B.Tech students — real examples
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. The strongest B.Tech portfolios are built on real projects, not tutorials. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, Knowledge Graphs project work runs through the AICTE IDEA Lab under faculty mentorship — here are the real patterns students build on.
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)
What students actually build
- RAG capstones over VIPS-TC corpora require students to decide how retrieved knowledge is structured and selected.
- Student-built MCP servers define exactly which structured sources a model may query.
Labs and infrastructure
- Extraction model runs and embedding generation for graph construction use the AICTE IDEA Lab GPU workstations.
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.
Frequently asked questions
What makes a good Knowledge Graphs project for B.Tech?
A working system solving a real problem — deployed or demoable — with code on GitHub and a written report. Depth on one well-executed Knowledge Graphs project beats five tutorial clones.
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