Alignments between natural language and Knowledge Base (KB) triples are an essential prerequisite for training machine learning approaches employed in a variety of Natural Language Processing problems. These include Relation Extraction, KB Population, Question Answering and Natural Language Generation from KB triples. Available datasets that provide those alignments are plagued by significant shortcomings – they are of limited size, they exhibit a restricted predicate coverage, and/or they are of unreported quality. To alleviate these shortcomings, we present T-REx, a dataset of large-scale alignments between Wikipedia abstracts and Wikidata triples.T-REx consists of 11 million triples aligned with 3.09 million Wikipedia abstracts (6.2 million sentences). T-REx is two orders of magnitude larger than the largest available alignments dataset and covers 2.5 times more predicates. Additionally, we stress the quality of this language resource thanks to an extensive crowdsourcing evaluation. T-REx is publicly available at https://w3id.org/t-rex.
@InProceedings{ELSAHAR18.632, author = {Hady Elsahar and Pavlos Vougiouklis and Arslen Remaci and Christophe Gravier and Jonathon Hare and Frederique Laforest and Elena Simperl}, title = "{T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
Summary of the paper