BEIR DBPedia-Entity (orgrctera/beir_dbpedia_entity)

Overview

DBpedia-Entity v2 is a standard test collection for entity-oriented search over the DBpedia knowledge base: given a short information need expressed in natural language, systems must retrieve DBpedia entities (articles) that satisfy that need. The collection unifies queries from several benchmarks (e.g. SemSearch, INEX, QALD entity search tasks) with graded relevance judgments collected under consistent guidelines.

BEIR (Benchmarking IR) repackages DBpedia-Entity as one of its heterogeneous zero-shot retrieval tasks. In the BEIR setting, each query is evaluated against a fixed corpus of DBpedia passages (title + abstract text per entity); models are scored with standard IR metrics after ranking corpus documents using the official qrels.

This Hub release (orgrctera/beir_dbpedia_entity) exposes the BEIR-style split as a tabular retrieval dataset: one row per query, with gold relevant entity IDs (and relevance grades) in expected_output. The underlying 467 queries from DBpedia-Entity v2 appear here as dev (67) and test (400) partitions, matching the BEIR train/dev/test convention for this benchmark.

Task: retrieval (DBPedia-Entity)

• Task type: Retrieval — ad hoc entity retrieval over the DBpedia-Entity corpus distributed with BEIR (dense / sparse / hybrid retrievers, rerankers, or full RAG stacks).
• Input: A natural-language query (input) describing the sought entities or information need.
• Supervision: expected_output is a JSON string of { "id": "<corpus-doc-id>", "score": <grade> } entries. Grades follow the DBpedia-Entity v2 qrels (typically 1 and 2 for different relevance levels; treat as graded labels when computing nDCG, or binarize for binary Recall depending on your protocol).

Full benchmark evaluation requires indexing the BEIR DBpedia-Entity corpus (document text keyed by _id) and ranking with the same query / qrels splits. See UKPLab/beir and ir-datasets: beir/dbpedia-entity for the canonical corpus + evaluation layout.

Background

DBpedia-Entity v2

Hasibi et al. (SIGIR 2017) introduced DBpedia-Entity v2, an updated test collection built on a 2015-10 DBpedia snapshot. Compared to earlier versions, v2 uses a unified candidate pool across retrieval models and uniform crowdsourced assessments, yielding graded judgments for a large set of query–entity pairs and making the collection a widely used benchmark for entity search and related semantic retrieval research.

• Resources: iai-group/DBpedia-Entity on GitHub · Dataset overview

BEIR

Thakur et al. (2021) curated BEIR: 18 public datasets spanning nine retrieval task families (including entity retrieval). DBpedia-Entity is the BEIR benchmark for retrieving structured encyclopedic entities from a large KB-derived corpus—complementary to news, biomedical, or QA-style collections in the same suite.

• Code / data hub: UKPLab/beir · BeIR/dbpedia-entity on Hugging Face

Relation to ir-datasets / beir/dbpedia-entity

The same corpus, queries, and qrels are documented in ir-datasets (beir/dbpedia-entity, plus dev and test topic subsets). This dataset is a row-oriented export aligned with other CTERA benchmark releases: one row per query with string expected_output for tooling compatibility.

Data fields

Column	Type	Description
id	string	Unique row identifier (UUID).
input	string	The query text (information need).
expected_output	string	JSON array of { "id": "<doc_id>", "score": <int> } for judged relevant corpus entities (qrels-style).
metadata.query_id	string	Source query id (e.g. SemSearch / INEX style ids such as SemSearch_ES-81, INEX_XER-147).
metadata.split	string	dev or test.

Splits

Split	Queries
dev	67
test	400
Total	467

Examples

Illustrative rows (document lists truncated for readability; full qrels may contain many entities per query).

Example 1 — dev

• input: south dakota state university
• expected_output (excerpt):

[
  {"id": "<dbpedia:2011–12_South_Dakota_State_Jackrabbits_men's_basketball_team>", "score": 1},
  {"id": "<dbpedia:Dakota_State_University>", "score": 2},
  {"id": "<dbpedia:South_Dakota_Board_of_Regents>", "score": 1}
]

• metadata.query_id: SemSearch_ES-81 · metadata.split: dev

Example 2 — test

• input: Chemical elements that are named after people
• expected_output (excerpt):

[
  {"id": "<dbpedia:Bohrium>", "score": 2},
  {"id": "<dbpedia:Curium>", "score": 2},
  {"id": "<dbpedia:Einsteinium>", "score": 2},
  {"id": "<dbpedia:Naming_of_elements>", "score": 1}
]

• metadata.query_id: INEX_XER-147 · metadata.split: test

References

DBpedia-Entity v2 (original test collection)

Abstract (SIGIR 2017): The paper presents DBpedia-Entity v2, a test collection for entity search built on DBpedia, with a unified pooling setup and crowdsourced relevance judgments suitable for comparing entity retrieval methods on a large query set.

• Faegheh Hasibi, Fedor Nikolaev, Chenyan Xiong, Krisztian Balog, Svein Erik Bratsberg, Alexander Kotov, James Callan. “DBpedia-Entity v2: A Test Collection for Entity Search.” Proceedings of the 40th ACM SIGIR (2017). ACM DL · Author PDF

BEIR (benchmark including DBpedia-Entity)

Abstract (arXiv:2104.08663): We introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval. We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains…

• Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych. “BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models.” arXiv:2104.08663

Citation

If you use DBpedia-Entity, cite:

@inproceedings{Hasibi2017DBpediaEntityV2,
  author    = {Hasibi, Faegheh and Nikolaev, Fedor and Xiong, Chenyan and
               Balog, Krisztian and Bratsberg, Svein Erik and Kotov, Alexander and
               Callan, James},
  title     = {{DBpedia-Entity v2}: A Test Collection for Entity Search},
  booktitle = {Proceedings of the 40th International ACM SIGIR Conference on
               Research and Development in Information Retrieval},
  year      = {2017},
  url       = {https://doi.org/10.1145/3077136.3080751},
  doi       = {10.1145/3077136.3080751}
}

If you use the BEIR benchmark formulation, cite:

@article{Thakur2021Beir,
  title   = {{BEIR}: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
  author  = {Thakur, Nandan and Reimers, Nils and R{\"u}ckl{\'e}, Andreas and
             Srivastava, Abhishek and Gurevych, Iryna},
  journal = {arXiv preprint arXiv:2104.08663},
  year    = {2021},
  url     = {https://arxiv.org/abs/2104.08663}
}

Provenance

Exported for retrieval evaluation with DBpedia-Entity as the BEIR sub-benchmark dbpedia-entity. Corpus passages are not duplicated per row; join expected_output ids to the BEIR DBpedia-Entity corpus for full title and abstract text when building an index.