MELS-Benchmark

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). 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.