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Tacit Knowledge Retrieval System

Hybrid retrieval over laboratory discussions combining RGT embeddings with a knowledge graph, built end-to-end from document ingestion to evaluation.

Research Intern · National Taiwan University of Science and Technology2026
  • Retrieval
  • Knowledge Graph
  • LLM Systems
  • Evaluation

A research internship project for preserving and retrieving the tacit knowledge embedded in unstructured laboratory discussions.

Problem

Laboratory knowledge is often buried inside unstructured discussions — meeting notes, chat logs, and informal write-ups. When that knowledge is needed again, people search manually or ask around, and much of it simply gets lost.

Research Question

Can a hybrid retrieval system that combines embedding-based search with a knowledge graph improve the discovery of tacit knowledge compared with dense retrieval alone?

System

The pipeline runs end-to-end: raw discussion data in, retrievable answers out.

Discussion documents
        │
        ▼
Document ingestion ── normalize, chunk, dedupe
        │
        ├──────────────► RGT embeddings ───────────┐
        │                                          ├─► Hybrid Retrieval
        └──────────────► Knowledge Graph ──────────┘        │
                                               (rules + NLP) │
                                                             ▼
                                                    Reranking
                                                             │
                                                             ▼
                                                        Evaluation
  • RGT embeddings capture semantic similarity between passages.
  • Knowledge graph captures entity and relationship structure that embeddings alone miss.
  • Hybrid retrieval fuses both signals with reranking before evaluation.

My Contribution

  • Designed the document ingestion and chunking strategy for noisy discussion logs.
  • Built the embedding generation and graph construction stages.
  • Implemented the hybrid retrieval and reranking logic.
  • Ran the evaluation suite and error analysis.

Experiments

  • Baselines: dense retrieval only, BM25, and graph-only traversal.
  • Proposed: hybrid (dense + graph) with reranking.
  • Evaluated on a labeled query set derived from real lab discussions.

Results

Hybrid retrieval improved recall on relationship-heavy queries, where pure dense retrieval struggled. Error analysis showed the graph mainly helps when the answer is spread across multiple documents.

Results are placeholder text — replace with actual numbers and analysis.

What I Learned

  • Retrieval quality is decided as much by ingestion and chunking as by the model.
  • Knowledge graphs and embeddings are complementary, not competing, signals.
  • Evaluation needs a task-aligned query set, not just off-the-shelf benchmarks.