Knowledge Graph Final Year Projects 2026
Knowledge graphs (KGs) represent entities and relations as structured triples, enabling reasoning, search and recommendation. Student projects cover construction from text, embedding models (TransE, RotatE, ComplEx), link prediction benchmarks, entity alignment and question answering over KGs.
Below: 80+ topics with tools and representative datasets.
Tools & Platforms
Neo4j
PyKEEN
RDFLib
DGL / PyG
spaCy · OpenIE
FB15k-237 · WN18RR
Best Knowledge Graph Project Topics (80+)
Topics with tools and datasets.
| # | Project Topic | Tools | Datasets |
|---|---|---|---|
| KG Construction & Schema | |||
| 01 | BuildNamed Entity Recognition + Relation Extraction Pipeline for KG | spaCy · OpenIE concepts | News / Wikipedia text |
| 02 | BuildBuilding a Domain Knowledge Graph from Structured Tables | Python · Neo4j | CSV / relational exports |
| 03 | BuildRDF Triple Store Population and SPARQL Query Demo | RDFLib · Fuseki concepts | Sample RDF graphs |
| 04 | BuildOntology Design Lite: Classes, Properties and Constraints | Protégé concepts · OWL | Domain ontology sketch |
| 05 | BuildCoreference Resolution Impact on KG Completeness | spaCy · coref | Document collections |
| 06 | BuildNoise Filtering and Confidence Scoring for Extracted Triples | heuristics · scoring | OpenIE outputs |
| 07 | BuildIncremental KG Update from Streaming Text | pipeline · Neo4j | Time-stamped documents |
| 08 | BuildVisualization of Constructed KG (Neo4j Bloom / NetworkX) | Neo4j · NetworkX · pyvis | Built domain graph |
| 09 | BuildSchema Matching between Heterogeneous Data Sources | similarity · mapping | Multi-source tables |
| 10 | BuildEnd-to-End: Text → Triples → Graph Database | spaCy · Neo4j | Domain corpus |
| Knowledge Graph Embeddings | |||
| 11 | EmbTransE Embedding Training and Evaluation | PyKEEN · AmpliGraph | FB15k-237 · WN18RR |
| 12 | EmbTransH / TransR Comparison for Complex Relations | PyKEEN | FB15k-237 |
| 13 | EmbRotatE Rotational Embeddings for KG | PyKEEN | FB15k-237 · WN18RR |
| 14 | EmbComplEx Complex Embeddings and Hermitian Products | PyKEEN | FB15k-237 |
| 15 | EmbDistMult and Simple Bilinear Models Baseline | PyKEEN | WN18RR |
| 16 | EmbEmbedding Dimension and Negative Sampling Ablation | PyKEEN · sweeps | FB15k-237 |
| 17 | EmbVisualizing Entity Embeddings with t-SNE / UMAP | sklearn · matplotlib | Trained TransE/RotatE |
| 18 | EmbRelation-Specific Embedding Analysis | PyKEEN · analysis | FB15k-237 relations |
| 19 | EmbFrom-Scratch TransE Implementation in PyTorch | PyTorch pure | Toy / FB15k subset |
| 20 | EmbBenchmark Suite: Multiple Embedding Models on Fixed Protocol | PyKEEN · standardized eval | FB15k-237 · WN18RR |
| Link Prediction | |||
| 21 | LinkLink Prediction with TransE / RotatE Rank Metrics | PyKEEN · MRR · Hits@K | FB15k-237 |
| 22 | LinkFiltered vs Raw Ranking Protocol Comparison | PyKEEN · evaluation | WN18RR |
| 23 | LinkHead vs Tail Prediction Asymmetry Study | embedding models | FB15k-237 |
| 24 | LinkPath-Based Features for Link Prediction | path ranking · ML | FB15k subset |
| 25 | LinkEnsemble of Embedding Models for Link Prediction | score fusion | FB15k-237 |
| 26 | LinkInductive Link Prediction on Emerging Entities | inductive setups · PyKEEN | Inductive splits |
| 27 | LinkTemporal Link Prediction Concepts | time-aware embeddings | ICEWS / GDELT subsets |
| 28 | LinkNegative Sampling Strategies Impact on Ranking | PyKEEN · sampling modes | FB15k-237 |
| Entity Alignment & Matching | |||
| 29 | AlignCross-KG Entity Alignment with Embedding Methods | OpenEA concepts · PyKEEN | DBP15K concepts |
| 30 | AlignAttribute-Enhanced Entity Matching | feature similarity · ML | Entity pairs with attrs |
| 31 | AlignString Similarity Baselines for Entity Resolution | fuzzy matching · sklearn | Name variation sets |
| 32 | AlignGraph Structure Signals for Alignment | GNN / embedding align | Cross-lingual KG pairs |
| 33 | AlignActive Learning for Entity Alignment Annotation | uncertainty sampling | Candidate pairs |
| 34 | AlignEvaluation Metrics for Alignment: Precision, Recall, F1 | standard metrics | Aligned entity sets |
| 35 | AlignMulti-Source Entity Resolution Pipeline | blocking · matching | Multi-DB entity records |
| 36 | AlignVisual Inspection Tool for Alignment Candidates | Streamlit · pairs UI | Alignment outputs |
| Knowledge Graph Question Answering (KGQA) | |||
| 37 | KGQASimple KGQA: Template-Based SPARQL Generation | templates · RDFLib | SimpleQuestions concepts |
| 38 | KGQANeural Semantic Parsing for KG Questions | seq2seq · HF | WebQuestionsSP concepts |
| 39 | KGQAEmbedding-Based KGQA with Candidate Ranking | TransE + ranker | SimpleQuestions |
| 40 | KGQAMulti-Hop Reasoning over KG for Complex Questions | path finding · GNN | MetaQA / ComplexWebQ concepts |
| 41 | KGQAEntity Linking for Question Understanding | spaCy · EL models | Question corpora |
| 42 | KGQAEnd-to-End KGQA Demo with Neo4j Backend | Neo4j · Cypher · UI | Domain KG + questions |
| 43 | KGQAError Analysis: Ambiguity and Incomplete KG Paths | manual + metrics | Failed QA cases |
| 44 | KGQAConversational KGQA with Context Tracking | dialogue state · KG | Multi-turn QA samples |
| Graph Neural Networks on KGs | |||
| 45 | GNNR-GCN Relational Graph Convolutional Networks | DGL · PyG | FB15k-237 node class / LP |
| 46 | GNNCompGCN Composition-Based Multi-Relational GCN | DGL · PyTorch | WN18RR · FB15k-237 |
| 47 | GNNGraph Attention Networks on Knowledge Graphs | GAT · relation-aware | Entity classification |
| 48 | GNNNode Classification on Heterogeneous KGs | HGT / HAN concepts | Domain typed graphs |
| 49 | GNNLink Prediction with GNN Encoders + Decoders | DGL-KE · PyG | FB15k-237 |
| 50 | GNNSubgraph Extraction for Inductive Reasoning | GraIL concepts · PyG | Inductive LP splits |
| 51 | GNNScalable Neighbor Sampling for Large KGs | GraphSAINT / NeighborLoader | Large graph samples |
| 52 | GNNGNN vs Pure Embedding Model Comparison | unified eval protocol | FB15k-237 · WN18RR |
| Applications & Domain KGs | |||
| 53 | AppMovie / Entertainment Knowledge Graph + Recommendations | Neo4j · embeddings | MovieLens + metadata KG |
| 54 | AppAcademic Paper Citation KG Construction and Analysis | Neo4j · NetworkX | OpenAlex / DBLP samples |
| 55 | AppE-Commerce Product Knowledge Graph | triples · Neo4j | Amazon product relations |
| 56 | AppHealthcare / Biomedical KG Exploration Concepts | UMLS / DrugBank concepts | Public biomedical subsets |
| 57 | AppCybersecurity Threat Intelligence KG Lite | IOC relations · graph | Public threat reports |
| 58 | AppLegal Case Citation Graph Analysis | NetworkX · centrality | Case law citation samples |
| 59 | AppTourism / POI Knowledge Graph for Recommendations | geo + relations | POI + review graphs |
| 60 | AppNews Event KG: Entities, Relations and Timelines | extraction · temporal | News corpora |
| Reasoning, Quality & Capstone | |||
| 61 | AdvRule Mining on Knowledge Graphs (AMIE-Style Concepts) | rule miners · Python | FB15k subset rules |
| 62 | AdvConsistency Checking and Constraint Violation Detection | SHACL concepts · RDF | Ontology + instance data |
| 63 | AdvKG Completion: Predicting Missing Links at Scale | embedding + ranking | FB15k-237 completion |
| 64 | AdvExplainable Link Prediction: Path and Attention Evidence | path ranking · attention | Explained predictions |
| 65 | AdvMulti-Modal Knowledge Graphs: Text + Image Entities | CLIP + triples | Multi-modal entity sets |
| 66 | AdvTemporal Knowledge Graph Embedding and Forecasting | TTransE concepts | ICEWS sample |
| 67 | AdvFederated Knowledge Graph Embedding Concepts | FL + embeddings | Partitioned triples |
| 68 | AdvKG Quality Metrics: Completeness, Accuracy, Consistency | metric suite | Built / public KGs |
| 69 | AdvSPARQL Query Optimization and Benchmarking | query plans · timing | Complex SPARQL suite |
| 70 | AdvInteractive KG Explorer Dashboard | Streamlit · Neo4j / NetworkX | Domain graph UI |
| 71 | AdvReproducibility Package: Seeds, Splits, Embedding Logs | PyKEEN · configs | Full experiment template |
| 72 | AdvTeaching Package: Construction → Embeddings → LP Curriculum | notebooks · scripts | FB15k teaching set |
| 73 | AdvCapstone: Domain KG from Scratch + Embedding + Application | end-to-end pipeline | User-chosen domain |
| 74 | AdvOpen Challenges: Scalability, Noise and Inductive Settings | literature + experiments | Hard benchmark subsets |
| 75 | AdvKnowledge Graph Embeddings for Downstream NLP Tasks | entity linking · features | NLP + KG joint |
| 76 | AdvHybrid Symbolic–Neural Reasoning over KG | rules + embeddings | Reasoning benchmarks |
| 77 | AdvGraph Database vs Triple Store Performance Comparison | Neo4j · RDF store | Same query workload |
| 78 | AdvPrivacy-Preserving KG Sharing Concepts | anonymization · DP lite | Sensitive entity graphs |
| 79 | AdvAPI Deployment of KG Query and Embedding Lookup | FastAPI · Neo4j / vectors | Served KG endpoints |
| 80 | AdvBenchmark Report: Embedding Models across Two Datasets | PyKEEN · tables | FB15k-237 + WN18RR |
| 81 | AdvReal-Time KG Update and Query Consistency | streaming · Neo4j | Update workload demo |
| 82 | AdvFull Delivery Package: Code, Metrics, Thesis Structure | template · viva Q&A | Complete KG project |
Datasets are public (FB15k-237, WN18RR, DBpedia/Wikidata samples, etc.). Always cite sources and respect licences. Contact us for training scripts, metrics, university-format report, PPT and viva Q&A.
Why Choose Us for Knowledge Graph Projects?
Bangalore-based guidance for BE, BTech and MTech students in knowledge graphs and semantic technologies.
Construction & Schema
Text-to-triples pipelines, Neo4j population, RDF/SPARQL and ontology design lite.
Embeddings & Link Prediction
TransE, RotatE, ComplEx with PyKEEN and standard MRR/Hits@K evaluation.
Alignment & KGQA
Entity alignment, multi-hop QA and end-to-end question answering demos.
GNN & Applications
R-GCN, CompGCN and domain KGs for movies, papers, e-commerce and more.
FAQ — Knowledge Graph Projects
Strong topics include KG construction from text, TransE/RotatE embeddings, link prediction on FB15k-237/WN18RR, entity alignment, multi-hop KGQA and R-GCN/CompGCN models.
Neo4j, RDFLib, PyKEEN, AmpliGraph, DGL/PyG, spaCy; datasets include FB15k-237, WN18RR, DBpedia/Wikidata samples and domain triple stores.
Embedding training and GNNs benefit from a GPU. Construction, SPARQL and Neo4j work well on CPU. Colab/Kaggle GPUs are enough for FB15k-scale experiments.
Yes — training scripts, evaluation metrics, university-format report, PPT and viva Q&A.
Knowledge Graph Lab — Bangalore
Construction, embedding and application support for KG final-year projects.
KG
Construction
Text → TriplesConstruction
Embeddings
TransE · RotatE
PyKEENTransE · RotatE
Link
Prediction
FB15k-237Prediction
Entity
Alignment
Cross-KGAlignment
KGQA
Multi-Hop
SPARQL · NeuralMulti-Hop
GNN on
Knowledge Graphs
R-GCN · CompGCNKnowledge Graphs
Neo4j
Applications
Domain KGsApplications
Viva & Report
Support
DocumentationSupport