RELD: A Knowledge Graph of Relation Extraction Datasets

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 ∼ 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.