LM-ontology-concept-placement

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LM-ontology-concept-placement

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Language Model based ontology concept placement

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# LM-ontology-concept-placement

This is the official repository for [A Language Model based Framework for New Concept Placement in Ontologies](https://arxiv.org/abs/2402.17897), accepted for [ESWC 2024](https://2024.eswc-conferences.org/accepted-papers/). Our [presentation slides](https://drive.google.com/file/d/16Z8eT9t2AMkJMGxdH6CnqG3YHfLW0xz1/view?usp=sharing) with updated results on GPT-4 and Llama-2-13B (at slides page 16-17).

The study provides a Language Model based framework (including pre-trained and large language models) for new concept placement in ontologies, where the input includes a mention in a text corpus with an ontology, and the outputs are the predicted edges in the ontology to place the mention.

The method combines LMs with ontology structure, and includes three steps:
* edge search (and concept search), 
* edge formation and enrichment, and 
* edge selection.

This repository provides the implementation of the methods above, running scripts, and the dataset scripts for research-based reproducibility.

Minimum running code on Google CoLab: [`LM-ontology-concept-placement.ipynb`](https://github.com/KRR-Oxford/LM-ontology-concept-placement/blob/main/LM-ontology-concept-placement.ipynb).

# Requirements
The repository is based on `Python 3.8`.

See `requirements.txt`, for running Edge-Bi-encoder, Edge-Cross-encoder, Inverted Index, Fixed Embedding.

See `requirements-LLM.txt`, for running instruction tuning LLMs.

Note: we noticed some [Dependabot alerts from GitHub](https://docs.github.com/en/code-security/dependabot/dependabot-alerts/about-dependabot-alerts) related to the previous versions of libraries (Transformers, PyTorch, NLTK, Flair, and tqdm, as in `requirements.txt` and `requirements-LLM.txt`), but we have limited bandwidth to resolve them for this research-based project. Please be aware of this when you are using the project.

Examples to install packages using conda (optional):

```
conda create -n onto38 -y python=3.8
conda activate onto38
pip install -r requirements.txt
conda deactivate
```

```
conda create -n ontollm38 -y python=3.8
conda activate ontollm38
pip install -r requirements-LLM.txt
conda deactivate
```

# Model Training and Inference
See `Edge-Bi-enc+prompt-generation.sh` for the steps of running Edge-Bi-encoder, edge enrichment, and prompt generation, with running examples in `Edge-Bi-enc+prompt-gen-run-example.sh`.

See `Edge-Bi-enc+Cross-enc.sh` for the steps of running Edge-Bi-encoder, edge enrichment, and Edge-Cross-encoder, with running examples in `Edge-Bi-enc+Cross-enc-run-example.sh`.

See `run_tune_LLAMA_2_from_data_creation.sh` a running example for data generation, instruction-tuning, and prompting of LLAMA-2.

See `blink/prompting/run_search_snomed_disease-5to10.sh` and similar files for the examples of running Inverted Index and fixed embedding based approarches.

See other files in `blink/prompting` for the prompting of GPT-3.5-turbo, FLAN-T5, and Llama-2.

For all Edge-Bi-enc and Edge-Cross-enc scripts above:
* setting `train_bi` (train Bi-encoder), `rep_ents` (pre-calculate edge embeddings), `eval_biencoder` (inference with Bi-encoder and get data for cross encoder), `train_cross` (train Cross-encoder), `inference` (whole inference) to `true` to select to perform (or not perform) each step. 
* setting `eval_set` to `train`,`valid`,`valid-NIL`,`test-NIL` with comma separated for the `eval_biencoder` step to generate data for each data split.

For Edge-Bi-enc:
* setting `use_cand_analysis` (evaluate Bi-encoder results and generate initial instructions and prompts for LLMs) to true to perform the step.

# Datasets
Our work uses the datasets at [Zenodo](https://zenodo.org/records/10432003) and its JSON keys are described in the `dataset` folder.

# Data and processing sources
Before data creation, the sources below need to be downloaded.
* SNOMED CT https://www.nlm.nih.gov/healthit/snomedct/archive.html (and use snomed-owl-toolkit to form .owl files)
* UMLS https://www.nlm.nih.gov/research/umls/licensedcontent/umlsarchives04.html (and mainly use MRCONSO for mapping UMLS to SNOMED CT)
* MedMentions https://github.com/chanzuckerberg/MedMentions (source of entity linking)

The below tools and libraries are used.
* Protege http://protegeproject.github.io/protege/
* snomed-owl-toolkit https://github.com/IHTSDO/snomed-owl-toolkit
* DeepOnto https://github.com/KRR-Oxford/DeepOnto (based on OWLAPI https://owlapi.sourceforge.net/) for ontology processing and complex concept verbalisation

# Data creation scripts
Based on [OET](https://github.com/KRR-Oxford/OET) repository: the data creation scripts are available in `data-construction` folder, where `run_preprocess_ents_and_data+new.sh` provides an overall shell script that calls the other `.py` files.

# Acknowledgement
* Our dataset is based on [OET](https://github.com/KRR-Oxford/OET) and [zenodo link](https://zenodo.org/record/8228005).
* The baseline implementations are based on [BLINKout paper](https://arxiv.org/abs/2302.07189) and [BLINK repository](https://github.com/facebookresearch/BLINK) under the MIT liscence. 
* Acknowledgement to all [data and processing sources](https://github.com/KRR-Oxford/OET#data-and-processing-sources) listed above.

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