Analyzing Wikidata Transclusion on English Wikipedia

• URL: http://arxiv.org/abs/2011.00997v1
• Date: Mon, 2 Nov 2020 14:16:42 GMT
• Title: Analyzing Wikidata Transclusion on English Wikipedia
• Authors: Isaac Johnson
• Abstract summary: This work presents a taxonomy of Wikidata transclusion and an analysis of Wikidata transclusion within English Wikipedia. It finds that Wikidata transclusion that impacts the content of Wikipedia articles happens at a much lower rate (5%) than previous statistics had suggested (61%).
• Score: 1.5736899098702972
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wikidata is steadily becoming more central to Wikipedia, not just in maintaining interlanguage links, but in automated population of content within the articles themselves. It is not well understood, however, how widespread this transclusion of Wikidata content is within Wikipedia. This work presents a taxonomy of Wikidata transclusion from the perspective of its potential impact on readers and an associated in-depth analysis of Wikidata transclusion within English Wikipedia. It finds that Wikidata transclusion that impacts the content of Wikipedia articles happens at a much lower rate (5%) than previous statistics had suggested (61%). Recommendations are made for how to adjust current tracking mechanisms of Wikidata transclusion to better support metrics and patrollers in their evaluation of Wikidata transclusion.

Related papers

• Class Order Disorder in Wikidata and First Fixes [0.0]
  SPARQL queries were evaluated against Wikidata to determine the prevalence of several kinds of violations and suspect information. Suggestions are provided on how the problems might be addressed, either though better tooling or involvement of the Wikidata community.
  arXiv  Detail & Related papers  (2024-11-23T13:15:13Z)
• Towards a Brazilian History Knowledge Graph [50.26735825937335]
  We construct a knowledge graph for Brazilian history based on the Brazilian Dictionary of Historical Biographies (DHBB) and Wikipedia/Wikidata. We show that many terms/entities described in the DHBB do not have corresponding concepts (or Q items) in Wikidata.
  arXiv  Detail & Related papers  (2024-03-28T22:05:32Z)
• Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences [68.8204255655161]
  We propose our mapping process for the task of converting Wikidata statements to natural language text (WS2T) for Wikipedia projects at the sentence level. The main step is to organize statements, represented as a group of quadruples and triples, and then to map them to corresponding sentences in English Wikipedia. We evaluate the output corpus in various aspects: sentence structure analysis, noise filtering, and relationships between sentence components based on word embedding models.
  arXiv  Detail & Related papers  (2022-10-23T08:34:33Z)
• WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs [66.88232442007062]
  We introduce WikiDes, a dataset to generate short descriptions of Wikipedia articles. The dataset consists of over 80k English samples on 6987 topics. Our paper shows a practical impact on Wikipedia and Wikidata since there are thousands of missing descriptions.
  arXiv  Detail & Related papers  (2022-09-27T01:28:02Z)
• Improving Wikipedia Verifiability with AI [116.69749668874493]
  We develop a neural network based system, called Side, to identify Wikipedia citations that are unlikely to support their claims. Our first citation recommendation collects over 60% more preferences than existing Wikipedia citations for the same top 10% most likely unverifiable claims. Our results indicate that an AI-based system could be used, in tandem with humans, to improve the verifiability of Wikipedia.
  arXiv  Detail & Related papers  (2022-07-08T15:23:29Z)
• Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking [76.00737707718795]
  We propose a novel candidate retrieval paradigm based on entity profiling. We use the profile to query the indexed search engine to retrieve candidate entities. Our approach complements the traditional approach of using a Wikipedia anchor-text dictionary.
  arXiv  Detail & Related papers  (2022-02-27T17:38:53Z)
• Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History [5.727994421498849]
  We present Wikidated 1.0, a dataset of Wikidata's full revision history. To the best of our knowledge, it constitutes the first large dataset of an evolving knowledge graph.
  arXiv  Detail & Related papers  (2021-12-09T15:54:03Z)
• Survey on English Entity Linking on Wikidata [3.8289963781051415]
  Wikidata is a frequently updated, community-driven, and multilingual knowledge graph. Current Wikidata-specific Entity Linking datasets do not differ in their annotation scheme from schemes for other knowledge graphs like DBpedia. Almost all approaches employ specific properties like labels and sometimes descriptions but ignore characteristics such as the hyper-relational structure.
  arXiv  Detail & Related papers  (2021-12-03T16:02:42Z)
• Assessing the quality of sources in Wikidata across languages: a hybrid approach [64.05097584373979]
  We run a series of microtasks experiments to evaluate a large corpus of references, sampled from Wikidata triples with labels in several languages. We use a consolidated, curated version of the crowdsourced assessments to train several machine learning models to scale up the analysis to the whole of Wikidata. The findings help us ascertain the quality of references in Wikidata, and identify common challenges in defining and capturing the quality of user-generated multilingual structured data on the web.
  arXiv  Detail & Related papers  (2021-09-20T10:06:46Z)
• Commonsense Knowledge in Wikidata [3.8359194344969807]
  This paper investigates whether Wikidata con-tains commonsense knowledge which is complementary to existing commonsense sources. We map the relations of Wikidata to ConceptNet, which we also leverage to integrate Wikidata-CS into an existing consolidated commonsense graph.
  arXiv  Detail & Related papers  (2020-08-18T18:23:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.