├── src
│ ├── models
│ │ ├── init.py
│ │ └── calc_stats.py
│ │ ├── ontology_models.py
│ │ ├── ontology_mapper_rag.py
│ │ ├── ontology_mapping_engine.py
│ │ ├── ontology_mapper_lm.py
│ │ ├── ontology_mapper_st.py
│ │ ├── schema_mapper.py
│ │ ├── method_model.yaml
│ ├── CustomLogger
│ ├── KnowledgeDb
│ ├── Plotter
│ ├── QA
│ ├── Tests
└── README.md
└── <jupyter_nb1.ipynb>
└── <jupyter_nb2.ipynb>| Topic | Links | Resource Type |
|---|---|---|
| Review paper on all pretrained biomedical BERT models | Link | paper |
| Review of deep learning approaches for biomedical entity recognition | Link | paper |
| Comprehensive Review of pre-trained foundation models | Link | paper |
| KERMIT Knowledge graphs | Link | paper |
| LLMs4OM (Uses RAG Framework for matching concepts) | Link | paper |
| DeepOnto | Link | computational_tool |
| Text2Onto | Link | computational_tool |
| SapBert | Link | computational_tool |
| Ontology mapping with LLM’s | Link | computational_tool |
| Exploring LLM’s for ontology alignment | Link | computational_tool |
| Ontology alignment evaluation initiative | Link | dataset |
| Commonly used dataset for benchmarking of new methods | Link | dataset |
| NCIT Ontologies | Link | dataset |
| ML Friendly datasets for equivalence and subsumption mapping | Link | dataset |
| Positive and Negative Sampling Strategies for Representation Learning in Semantic Search | Link | blog |
| How to train sentence transformers | Link | blog |