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European Semantic Web Conference

ESWC 2023: The Semantic Web pp 337–353Cite as

RELD: A Knowledge Graph of Relation Extraction Datasets

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13870))

Abstract

Relation extraction plays an important role in natural language processing. There is a wide range of available datasets that benchmark existing relation extraction approaches. However, most benchmarking datasets are provided in different formats containing specific annotation rules, thus making it difficult to conduct experiments on different types of relation extraction approaches. We present RELD, an RDF knowledge graph of eight open-licensed and publicly available relation extraction datasets. We modeled the benchmarking datasets into a single ontology that provides a unified format for data access, along with annotations required for training different types of relation extraction systems. Moreover, RELD abides by the Linked Data principles. To the best of our knowledge, RELD is the largest RDF knowledge graph of entities and relations from text, containing \(\sim \)1230 million triples describing 1034 relations, 2 million sentences, 3 million abstracts and 4013 documents. RELD contributes to a variety of uses in the natural language processing community, and distinctly provides unified and easy modeling of data for benchmarking relation extraction and named entity recognition models.

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Notes

  1. 1.

    We excluded datasets (e.g., TACRED) that are not freely available in this current version of the RELD. However, they can easily be included in the future.

  2. 2.

    The detail information of schema, i.e., object properties, data properties, classes are available on RELD homepage.

  3. 3.

    Due to page size limitation, some details and extra instances are truncated from Listing 1.2.

  4. 4.

    We use VoID vocabulary to describe different metadata of the dataset.

  5. 5.

    We use NLTK [9] for tokenization, parts of speech tagging, and punctuation.

  6. 6.

    The complete details of the mapping process and the tools used are available in the tutorial https://reld-tutorial.readthedocs.io/en/latest/tutorial.html.

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Acknowledgment

This work has been supported by the BMBF-funded EuroStars project PORQUE (01QE2056C), the European Union’s Horizon Europe research and innovation programme ENEXA (101070305), the Ministry of Culture and Science of North Rhine-Westphalia (MKW NRW) within the project SAIL (NW21-059D) and the University of Malakand Pakistan.

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Authors and Affiliations

  1. DICE Group, Department of Computer Science, Paderborn University, Paderborn, Germany

    Manzoor Ali, Muhammad Saleem, Diego Moussallem, Mohamed Ahmed Sherif & Axel-Cyrille Ngonga Ngomo

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  1. Manzoor Ali
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  2. Muhammad Saleem
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  3. Diego Moussallem
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  4. Mohamed Ahmed Sherif
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  5. Axel-Cyrille Ngonga Ngomo
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Correspondence to Manzoor Ali .

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Editors and Affiliations

  1. Universidade de Lisboa, Lisbon, Portugal

    Catia Pesquita

  2. University of London, London, UK

    Ernesto Jimenez-Ruiz

  3. Rensselaer Polytechnic Institute, Troy, MI, USA

    Jamie McCusker

  4. Universidade de Lisboa, Lisbon, Portugal

    Daniel Faria

  5. Fondazione Bruno Kessler, Povo, Trento, Italy

    Mauro Dragoni

  6. KU Leuven, Sint-Katelijne-Waver, Belgium

    Anastasia Dimou

  7. EURECOM, Biot, France

    Raphael Troncy

  8. University of Mannheim, Mannheim, Germany

    Sven Hertling

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Ali, M., Saleem, M., Moussallem, D., Sherif, M.A., Ngonga Ngomo, AC. (2023). RELD: A Knowledge Graph of Relation Extraction Datasets. In: Pesquita, C., et al. The Semantic Web. ESWC 2023. Lecture Notes in Computer Science, vol 13870. Springer, Cham. https://doi.org/10.1007/978-3-031-33455-9_20

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  • DOI: https://doi.org/10.1007/978-3-031-33455-9_20

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