Blame for README.md

---
annotations_creators:
- expert-generated
language:
- en
multilinguality:
- monolingual
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
- zero-shot-classification
- feature-extraction
- other
task_ids:
- natural-language-inference
- semantic-similarity-scoring
pretty_name: EMED (e-MedSolution to OMOP CDM Schema Matching dataset)
tags:
- schema-matching
- data-integration
- healthcare
- omop-cdm
- e-medsolution
- medical-informatics
- tabular-schema
license: other
configs:
- config_name: default
  default: true
  data_files:
  - split: train
    path: data/train.csv
  - split: validation
    path: data/validation.csv
  - split: test
    path: data/test.csv
dataset_info:
  features:
  - name: omop
    dtype: string
  - name: table
    dtype: string
  - name: des1
    dtype: string
  - name: des2
    dtype: string
  - name: label
    dtype: int64
  - name: d1
    dtype: string
  - name: d2
    dtype: string
  - name: d3
    dtype: string
  - name: d4
    dtype: string
  splits:
  - name: train
    num_examples: 64957
  - name: validation
    num_examples: 8126
  - name: test
    num_examples: 8121
---

# EMED: Healthcare Schema Matching Dataset

## Dataset Summary

**EMED** is a schema matching dataset created by mapping a clinical dataset from the **e-MedSolution** healthcare information system domain to the **OMOP Common Data Model (CDM)**.

Schema matching in healthcare requires aligning complex, domain-specific clinical tables and attributes across heterogeneous standards. EMED provides **81,204** candidate attribute pairs across **55** e-MedSolution source tables (**804** attributes) and **7** OMOP CDM target tables (**101** attributes), with **239** verified positive matches.

This dataset is described in the paper [Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching](https://huggingface.co/papers/2501.08686). The accompanying code is available at [https://github.com/machuangtao/kg-rag4sm](https://github.com/machuangtao/kg-rag4sm).

## Dataset Creation Pipeline

The **EMED** dataset was created and annotated with the following three-stage pipeline:

1. **Data Preprocessing**: The textual description of each table and attribute name originates from the database design document of the *e-MedSolution* system, which was translated from Hungarian to English.
2. **Matching Pair Generation**: The candidate matching pairs were generated based on the Cartesian product of the table and attribute names in the *e-MedSolution* data model and a core subset of tables and attributes from the *OMOP CDM* data model (`person`, `visit_occurrence`, `visit_detail`, `condition_occurrence`, `procedure_occurrence`, `measurement`, and `specimen`).
3. **Data Annotation**: Each matching pair was initially annotated manually based on the translated textual descriptions of the table and attribute names in the source and target data models, and then partially reviewed and verified by a Hungarian native speaker for consistency.

## Dataset Statistics

### Overall Dataset Summary

| Dataset | Schema Pair | Domain | Source Tables (e-MedSolution) | Source Attributes | Target Tables (OMOP CDM) | Target Attributes | Total Pairs | Positive Matches (`label=1`) |
| :--- | :--- | :--- | ---: | ---: | ---: | ---: | ---: | ---: |
| **EMED** | EMED-OMOP | Healthcare | 55 | 804 | 7 | 101 | 81,204 | 239 |

### Split Breakdown

| Split | File | Total Pairs | Positive (`label=1`) | Negative (`label=0`) |
| :--- | :--- | ---: | ---: | ---: |
| `train` | `data/train.csv` | 64,957 | 109 | 64,848 |
| `validation` | `data/validation.csv` | 8,126 | 126 | 8,000 |
| `test` | `data/test.csv` | 8,121 | 4 | 8,117 |
| **Total** | — | **81,204** | **239** | **80,965** |

## Data Fields

Each record in the dataset represents a candidate matching pair between an OMOP CDM `<table>-<attribute>` and an e-MedSolution `<table>-<attribute>`:

| Column | Type | Description |
| :--- | :--- | :--- |
| `omop` | `string` | Target OMOP CDM table and attribute name formatted as `<omop_table>-<omop_attribute>` (e.g., `person-person_id`). |
| `table` | `string` | Source e-MedSolution table and attribute name formatted as `<emed_table>-<emed_attribute>` (e.g., `hun_bed-nsid`). |
| `des1` | `string` | Combined textual description of the target OMOP CDM table and attribute (`<d1>;<d2>`). |
| `des2` | `string` | Combined textual description of the source e-MedSolution table and attribute (`<d3>;<d4>`). |
| `label` | `int64` | Binary ground-truth match indicator (`1` = match, `0` = non-match). |
| `d1` | `string` | Textual description of the target OMOP CDM table. |
| `d2` | `string` | Textual description of the target OMOP CDM attribute. |
| `d3` | `string` | Textual description of the source e-MedSolution table. |
| `d4` | `string` | Textual description of the source e-MedSolution attribute (including foreign-key/context notes where available). |

### Example Instance

```json
{
  "omop": "person-person_id",
  "table": "hun_bed-nsid",
  "des1": "the person domain contains records that uniquely identify each patient in the source data who is time at-risk to have clinical observations recorded within the source systems.;a unique identifier for each person.",
  "des2": "The hun_bed table records the basic data of patient beds, i.e., bed number, type, and its availability.;The nurses station identifier code that belongs to specific ward (patient room).;A foreign key to the nurses station identifier in hun_nurses station table recording the belonging nurses station of the bed.",
  "label": 0,
  "d1": "the person domain contains records that uniquely identify each patient in the source data who is time at-risk to have clinical observations recorded within the source systems.",
  "d2": "a unique identifier for each person.",
  "d3": "The hun_bed table records the basic data of patient beds, i.e., bed number, type, and its availability.",
  "d4": "The nurses station identifier code that belongs to specific ward (patient room).;A foreign key to the nurses station identifier in hun_nurses station table recording the belonging nurses station of the bed."
}
```

## Usage

You can load the dataset directly using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Load the train / validation / test splits
dataset = load_dataset("dylan-ma/EMED")
print(dataset)

```

## Repository Structure

```text
EMED/
├── .gitattributes          # Git LFS tracking configuration
├── README.md               # Hugging Face Dataset Card & metadata
└── data/
    ├── train.csv           # Training split (64,957 rows)
    ├── validation.csv      # Validation split (8,126 rows)
    └── test.csv            # Test split (8,121 rows)
```

## Citation

If you use the **EMED** dataset in your research, please cite the following work:

```bibtex
@article{kgrag4sm2026,
  author   = {Ma, Chuangtao and Chakrabarti, Sriom and Khan, Arijit and Moln{\'{a}}r, B{\'{a}}lint },
  title    = {Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching},
  journal  = {CoRR},
  volume   = {abs/2501.08686},
  year     = {2025}
}

@article{ma2020knowledge,
  title={{Knowledge-enriched Schema Mapping: A Preliminary Case Study of e-MedSolution System}},
  author={Ma, Chuangtao},
  journal={E{\"o}tv{\"o}s Lor{\'a}nd University, Tech. Rep.},
  year={2020}
}
```