Avature/MELS-Benchmark

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). 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 [*]. 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

from datasets import load_dataset

# Load a specific subset
ds = load_dataset("Avature/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
• Repository: github.com/Avature/melo-benchmark
• HuggingFace: Avature/MELO-Benchmark

Citation

If you use this dataset, please cite the MELO paper (which describes the methodology used to construct both MELO and MELS):

@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 file for more information.