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README.md

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----
-license: mit
----
+---
+language:
+  - de
+  - en
+  - fr
+  - nl
+  - sv
+license: mit
+task_categories:
+  - text-retrieval
+  - sentence-similarity
+tags:
+  - entity-linking
+  - skills
+  - multilingual
+  - ranking
+  - information-retrieval
+  - ESCO
+configs:
+- config_name: bel_q_fr_c_en
+  data_files:
+  - split: queries
+    path: "bel_q_fr_c_en/queries-00000-of-00001.parquet"
+  - split: corpus
+    path: "bel_q_fr_c_en/corpus-00000-of-00001.parquet"
+- config_name: bel_q_fr_c_fr
+  data_files:
+  - split: queries
+    path: "bel_q_fr_c_fr/queries-00000-of-00001.parquet"
+  - split: corpus
+    path: "bel_q_fr_c_fr/corpus-00000-of-00001.parquet"
+- config_name: bel_q_nl_c_en
+  data_files:
+  - split: queries
+    path: "bel_q_nl_c_en/queries-00000-of-00001.parquet"
+  - split: corpus
+    path: "bel_q_nl_c_en/corpus-00000-of-00001.parquet"
+- config_name: bel_q_nl_c_nl
+  data_files:
+  - split: queries
+    path: "bel_q_nl_c_nl/queries-00000-of-00001.parquet"
+  - split: corpus
+    path: "bel_q_nl_c_nl/corpus-00000-of-00001.parquet"
+- config_name: deu_q_de_c_de
+  data_files:
+  - split: queries
+    path: "deu_q_de_c_de/queries-00000-of-00001.parquet"
+  - split: corpus
+    path: "deu_q_de_c_de/corpus-00000-of-00001.parquet"
+- config_name: deu_q_de_c_en
+  data_files:
+  - split: queries
+    path: "deu_q_de_c_en/queries-00000-of-00001.parquet"
+  - split: corpus
+    path: "deu_q_de_c_en/corpus-00000-of-00001.parquet"
+- config_name: swe_q_sv_c_en
+  data_files:
+  - split: queries
+    path: "swe_q_sv_c_en/queries-00000-of-00001.parquet"
+  - split: corpus
+    path: "swe_q_sv_c_en/corpus-00000-of-00001.parquet"
+- config_name: swe_q_sv_c_sv
+  data_files:
+  - split: queries
+    path: "swe_q_sv_c_sv/queries-00000-of-00001.parquet"
+  - split: corpus
+    path: "swe_q_sv_c_sv/corpus-00000-of-00001.parquet"
+---
+
+# MELS: Multilingual Entity Linking of Skills
+
+MELS is a collection of 8 datasets for evaluating the linking of skill mentions to the
+ESCO Skills taxonomy. It covers 3 countries and 4 languages.
+
+## Background
+
+MELS is a sibling dataset to [MELO (Multilingual Entity Linking of Occupations)](https://huggingface.co/datasets/federetyk/MELO-Benchmark).
+Both datasets were built using the same methodology and the same type of source data:
+crosswalks between national taxonomies and ESCO, published by official labor-related
+organizations from EU member states.
+
+The difference is the entity type~~:~~
+- **MELO** links occupation mentions (job titles) to ESCO Occupations
+- **MELS** links skill mentions to ESCO Skills
+
+MELS covers fewer countries than MELO because fewer EU member states have published
+ESCO skill crosswalks. While MELO includes crosswalks from 21+ countries, only 3
+countries (Belgium, Germany, Sweden) have published skill crosswalks that could be
+used for MELS. This limited scope is why MELS was not published as a standalone
+benchmark, but the data remains useful for skill entity linking evaluation.
+
+**2026-01-01 Update**: Austria, Czechia, and Estonia have recently uploaded crosswalks for skills
+as well [[*](https://esco.ec.europa.eu/en/use-esco/eures-countries-mapping-tables)]. We plan to
+include these in a future version of MELS.
+
+## Dataset Structure
+
+Each subset (configuration) contains two splits:
+
+- **`queries`**: Skill mentions from national taxonomies, with indices of matching ESCO skills
+- **`corpus`**: ESCO skill labels
+
+### Schema
+
+**queries split:**
+| Column | Type | Description |
+|--------|------|-------------|
+| `text` | `string` | The skill mention (surface form) |
+| `labels` | `list[int]` | Indices of relevant corpus elements |
+
+**corpus split:**
+| Column | Type | Description |
+|--------|------|-------------|
+| `text` | `string` | The ESCO skill label (surface form) |
+
+## Available Subsets
+
+The subset names follow the pattern: `{country}_q_{query_lang}_c_{corpus_lang}`
+
+| Subset | Country | Query Lang | Corpus Lang | # Queries | # Corpus |
+|--------|---------|------------|-------------|-----------|----------|
+| `bel_q_fr_c_fr` | Belgium | fr | fr | 2,247 | 17,312 |
+| `bel_q_fr_c_en` | Belgium | fr | en | 2,247 | 97,520 |
+| `bel_q_nl_c_nl` | Belgium | nl | nl | 2,247 | 25,748 |
+| `bel_q_nl_c_en` | Belgium | nl | en | 2,247 | 97,520 |
+| `deu_q_de_c_de` | Germany | de | de | 1,722 | 19,466 |
+| `deu_q_de_c_en` | Germany | de | en | 1,722 | 97,520 |
+| `swe_q_sv_c_sv` | Sweden | sv | sv | 4,381 | 19,251 |
+| `swe_q_sv_c_en` | Sweden | sv | en | 4,381 | 100,273 |
+
+### Subset Naming Convention
+
+- `{country}`: ISO 3166-1 alpha-3 country code (e.g., `deu` for Germany)
+- `q_{lang}`: Query language (ISO 639-1 code)
+- `c_{lang}`: Corpus language (ISO 639-1 code)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load a specific subset
+ds = load_dataset("federetyk/MELS-Benchmark", "deu_q_de_c_de")
+
+# Access the data
+query_surface_forms = ds["queries"]["text"]
+corpus_surface_forms = ds["corpus"]["text"]
+label_lists = ds["queries"]["labels"]
+
+# Example: Get relevant corpus texts for the first query
+query_idx = 0
+print(f"Query: {query_surface_forms[query_idx]}")
+print(f"Relevant ESCO skills:")
+for corpus_idx in label_lists[query_idx]:
+    print(f"  - {corpus_surface_forms[corpus_idx]}")
+```
+
+## Relation to MELO
+
+MELS uses the same methodology as MELO. For details on how the datasets were
+constructed from ESCO crosswalks, see the MELO paper and repository:
+
+- **Paper:** [MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations](https://recsyshr.aau.dk/wp-content/uploads/2024/10/RecSysHR2024-paper_2.pdf)
+- **Repository:** [github.com/Avature/melo-benchmark](https://github.com/Avature/melo-benchmark)
+- **HuggingFace:** [federetyk/MELO-Benchmark](https://huggingface.co/datasets/federetyk/MELO-Benchmark)
+
+## Citation
+
+If you use this dataset, please cite the MELO paper (which describes the methodology
+used to construct both MELO and MELS):
+
+```bibtex
+@inproceedings{retyk2024melo,
+  title        = {{MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations}},
+  author       = {Federico Retyk and Luis Gasco and Casimiro Pio Carrino and Daniel Deniz and Rabih Zbib},
+  booktitle    = {Proceedings of the 4th Workshop on Recommender Systems for Human Resources
+                  (RecSys in {HR} 2024), in conjunction with the 18th {ACM} Conference on
+                  Recommender Systems},
+  year         = {2024},
+  url          = {https://recsyshr.aau.dk/wp-content/uploads/2024/10/RecSysHR2024-paper_2.pdf},
+}
+```
+
+## License
+
+This dataset is licensed under the MIT License. See the [LICENSE](https://github.com/Avature/melo-benchmark/blob/main/LICENSE) file for more information.

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