| .. | ||
| corpus_quality_assessment.md.pdf | ||
| Merged_DB_erasmus_overview.pdf | ||
| opus_pipeline.py | ||
| README.md | ||
| SemanticMerge.pdf | ||
| similarity_distribution_sample.txt | ||
| title | published | taxonomy | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dries Huybens | true |
|
ERASMUS Intern Summer 2026, 20 July - 30 August
Topic:
Multilingual Knowledge Graphs from medical data
Goal:
- Construct a knowledge graph from medical package inserts in multiple languages
- Utilize the graph in an intelligent agent that recommends medication.
Tasks:
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Continue project of Bogdan Paul Chis
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Study repositories:
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Learn intelligent agents and generative models - OpenAI API, Agent frameworks, RAG systems.
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Learn about knowledge graphs and GraphRAG. Read several research papers.
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Prepare a Python based workflow, use git code repository
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Visualize the graph
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Prepare an agent that utilizes the unstructured data and graph-data.
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Evaluate the agent using DeepEval or RAGAS.
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Write a report
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Put all code to GIT
Project tasks update:
- Prepare a multilingual parallel corpus from OPUS data
- Prepare dataset for visual language model evaluation from foto
- Prepare dataset for visual language model evaluation from wikipedia
- Prepare corpus of text and image data from pravda.sk
Outupus:
- multilingual corpus was analyzed with embedding models. Semantic overlap between languages is low, so multilngual parallel corpus will be small.
- A corpus from FotkyZadarmo.sk