Wikidata

By Denny Vrandečić, Markus Krötzsch. 2014

2,785 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Information Systems and Management, Knowledge Graphs & Representation

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Citations in the corpus

StereoSet: Measuring stereotypical bias in pretrained language models (2020, methodology): “We curate diverse set of target terms for the target domains using Wikidata relation triples (Vrandečić and Krötzsch, 2014).”
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering (2019, methodology): “Specifically, we use Wikipedia as the text corpus C and Wikidata (Vrandečić and Krötzsch, 2014) as the knowledge base K, as there exists an alignment between the two resources and Wikipedia has been widely used before in opendomain question answering research (Chen et al.”
oLMpics-On What Language Model Pre-training Captures (2019, methodology): “We instantiate ENT using information from W IKIDATA (Vrandeˇci´c and Kr˝otzsch, 2014), choosing challenging distractors.”
Knowledge Enhanced Contextual Word Representations (2019, methodology): “Early experiments with embeddings derived from Wikidata relations 4 did not improve results.”
ProoFVer: Natural Logic Theorem Proving for Fact Verification (2021, methodology): “In such cases, we retain the word level alignments from the aligner and perform lexical level entailment assignment with the help of Wordnet and Wikidata.”
Time-Aware Language Models as Temporal Knowledge Bases (2021, methodology): “Starting with the November 2020 Wikidata snapshot (Vrandeˇci´c and Krötzsch, 2014) we ﬁrst identify all facts which have either a start or an end date after 2010 and whose subjects and objects are both entities with Wikipedia pages.”
Mass-Editing Memory in a Transformer (2022, background): “…Miller, 1995) have been followed by web-powered knowledge graphs (Auer et al., 2007; Bollacker et al., 2007; Suchanek et al., 2007; Havasi et al., 2007; Carlson et al., 2010; Dong et al., 2014; Vrandeˇci´c & Krötzsch, 2014; Bosselut et al., 2019) that extract knowledge from large-scale sources.”
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System (2021, positive): “FewRel contains relations from different domains, including Wikipedia (Vrandečić and Krötzsch, 2014), SemEval-2010 (Hendrickx et al.”
Unsupervised Commonsense Question Answering with Self-Talk (2020, methodology): “Petroni et al. (2019) showed that such LMs can be used in a KB completion task over ConceptNet and Wikidata (Vrandečić and Krötzsch, 2014) by converting KB relations into natural language templates and querying the LM for the missing part in the triplet (concept1, relation, concept2).”
Explicit Knowledge-based Reasoning for Visual Question Answering (2015, methodology): “In structured KBs, knowledge is typically represented by a large number of triples of the form (arg1,rel,arg2) , where arg1 and arg2 denote two entities in the KB and rel denotes a predicate representing the relationship between these two entities.”
Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling (2019, methodology): “Accordingly, we use the neural-el (Gupta et al., 2017) entity linker to identify additional links to Wikidata, and identify coreferences using Stanford CoreNLP 2 to cover pronouns, nominals, and other tokens missed by the linker.”
Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model (2019, methodology): “3Each Wikipedia entity (title) corresponds to a unique entity node in Wikidata.”
Differentiable Reasoning over a Virtual Knowledge Base (2020, methodology): “Probing Experiments Finally, to compare the representations learned by the BERT model finetuned on the WikiData slot-filling task, we design two probing experiments.”
Assessing The Factual Accuracy of Generated Text (2019, methodology): “l number of facts in the article. 3 DATASET We create a dataset for fact extraction using distant supervision that is based entirely on the English Wikipedia corpus and the Wikidata knowledge baseWKB [37]. Our distant supervisor is very similar to the one proposed by Mintz et al. [17]. Although the ”