T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples

By Hady ElSahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon S. Hare, Frédérique Laforest, Elena Paslaru Bontas Simperl. 2018

381 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Few-shot Learning with Retrieval Augmented Language Models (2022, background): “We evaluate our model on the following tasks and datasets included in KILT: question answering: NaturalQuestions (Kwiatkowski et al., 2019), TriviaQA (Joshi et al., 2017) and HotpotQA (Yang et al., 2018); slot ﬁlling: Zero Shot RE (Levy et al., 2017) and T-REx (Elsahar et al., 2018); entity linking:”
How Context Affects Language Models' Factual Predictions (2020, neutral): “9 on T-REx and × 3 .”
Language Models as Knowledge Bases? (2019, methodology): “It is derived from the T-REx dataset (Elsahar et al., 2018) and is much larger than Google-RE with a broader set of relations.”
Autoregressive Search Engines: Generating Substrings as Document Identifiers (2022, background): “Surfacing knowledge from large corpora is a crucial step when dealing with knowledge intensive language tasks (Levy et al., 2017; Dinan et al., 2019; Elsahar et al., 2018; Petroni et al., 2021), such as open-domain question answering (Voorhees et al., 1999; Joshi et al., 2017; Yang et al., 2018;…”
The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus (2021, background): “…be considered to tackle KI tasks, such as large collections of question-answer pairs (Lewis et al., 2021; Huber et al., 2021), structured knowledge sources (Berant et al., 2013; Levy et al., 2017; Elsahar et al., 2018) or domain speciﬁc collections (Tsatsaronis et al., 2015; Saikh et al., 2021).”
Learning Dense Representations of Phrases at Scale (2020, methodology): “, 2020): T-REx (Elsahar et al., 2018) and zero-shot relation extraction (Levy et al.”
Autoregressive Entity Retrieval (2020, background): “…Questions (Kwiatkowski et al., 2019), HotpotQA (Yang et al., 2018c), TriviaQA (Joshi et al., 2017), ELI5 (Fan et al., 2019); slot ﬁlling with T-REx (Elsahar et al., 2018), Zero Shot RE (Levy et al., 2017); entity disambiguation on AIDA CoNLL-YAGO, WNED-WIKI and WNED-CWEB; dialogue with Wizard of…”
KILT: a Benchmark for Knowledge Intensive Language Tasks (2020, background): “T-REx (Elsahar et al., 2018) provides a largescale collection of facts aligned to sentences in Wikipedia abstracts through distant supervision.”
True Few-Shot Learning with Language Models (2021, methodology): “We use the “TREx” split [57], which consists of 41 relations (up to 1k examples each).”
DART: Open-Domain Structured Data Record to Text Generation (2020, methodology): “We also explored automatic alignments be-tween the knowledge base and text including Neural Wikipedian (Vougiouklis et al., 2018) and T-Rex (Elsahar et al., 2018).”
GLM-130B: An Open Bilingual Pre-trained Model (2022, background): “We do not include other distantly supervised dataset (e.g., T-Rex (Elsahar et al., 2018)) since they can be extremely noisy.”
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts (2021, methodology): “The relations we learn to predict are T-REx original (Elsahar et al., 2018), T-REx extended (Shin et al., 2020), Google-RE (Orr, 2013), and ConceptNet (Speer et al., 2017)—or rather, the subsets that were used by the LAMA and AutoPrompt papers.”
Knowledge Neurons in Pretrained Transformers (2021, methodology): “The experts design various prompt templates for 38 relations from the T-REx dataset (ElSahar et al., 2018).”
Factual Probing Is [MASK]: Learning vs. Learning to Recall (2021, methodology): “ther from the original T-REx dataset (Elsahar et al., 2018) or from Wikidata, with no triples that appear in the LAMA benchmark.”
Measuring and Improving Consistency in Pretrained Language Models (2021, methodology): “We use knowledge graph data from T-REx (Elsahar et al., 2018).”
Modifying Memories in Transformer Models (2020, methodology): “For this task, we introduced two benchmarks based on T-REx and zsRE datasets.”