Wikidata

Mastering Structured Data on the Semantic Web: From HTML5 Microdata to Linked Open Data

by Leslie Sikos  · 10 Jul 2015

dataset. Table 3-1 summarizes the most popular RDF dumps. Table 3-1. Popular RDF Dumps Dataset RDF Dump DBpedia http://wiki.dbpedia.org/Downloads2014 WikiData http://dumps.wikimedia.org/wikidatawiki/ GeoNames http://download.geonames.org/all-geonames-rdf.zip LinkedGeoData http://downloads.linkedgeodata.org/releases/ Open Directory http://rdf.dmoz

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Open Data Frequently Used Linked Datasets LOD datasets are published in a variety of fields. Interdisciplinary datasets such as DBpedia (http://dbpedia.org) and WikiData (http://www.wikidata.org) are general-purpose datasets and are, hence, among the most frequently used ones. Geographical applications can benefit from datasets such as GeoNames (http

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?name . ?person dbo:deathDate ?death . FILTER (?birth < "1901-01-01"^^xsd:date) . } ORDER BY ?name Wikidata Wikidata is one of the largest LOD databases that features both human-readable and machine-readable contents, at http://www.wikidata.org. Wikidata contains structured data from Wikimedia projects, such as Wikimedia Commons, Wikipedia, Wikivoyage, and Wikisource, as

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editable Freebase dataset, resulting in approximately 13 million data items. In contrast to many other LOD datasets, Wikidata is collaborative—anyone can create new items and modify existing ones. Like Wikipedia, Wikidata is multilingual. The Wikidata repository is a central storage of structured data, whereby data can be accessed not only directly but

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a label, which is a descriptive alias, connected by site links. Each item is characterized by statements that consist of a property and property value. Wikidata supports the Lua Scribunto parser extension to allow embedding scripting languages in MediaWiki and access the structured data stored in

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Wikidata through client Wikis. Data can also be retrieved using the Wikidata API. GeoNames GeoNames is a geographical database at http://www.geonames.org that provides RDF descriptions for more than 7,500

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previously known as Bigdata. It is a highly scalable, open source storage and computing platform [11]. Suitable for Big Data applications and selected for the Wikidata Query Service, Blazegraph is specifically designed to support big graphs, offering Semantic Web (RDF/SPARQL) and graph database (tinkerpop, blueprints, vertex-centric) APIs. The robust

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on graph theory and Knowledge Representation. You learned how to annotate your web site markup with machine-readable metadata from Schema.org, DBpedia, GeoNames, and Wikidata to boost site performance on Search Engine Result Pages. By now, you can write advanced machine-readable personal descriptions, using vCard-based hCard, FOAF, Schema

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Semantic Web Services with OWL-S, WSDL, WSML, and WS-BPEL. You can run complex SPARQL queries on large LOD datasets, such as DBpedia and Wikidata, and even encourage data reuse with your own easy-to-access OpenLink Virtuoso, Fuseki, or 4store SPARQL endpoint. Finally, you learned about Big Data applications

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, 74 DBpedia, 63 five-star rating system, 60 GeoNames, 65 LinkedGeoData, 66 principles, 59 RDF crawling, 62 RDF dumps, 62 SPARQL endpoints, 62 visualization, 75 Wikidata, 65 YAGO, 67 LODStats, 113 Pellet, 93 Persistent Uniform Resource Locators (PURLs), 9 Process model, 129 Protégé Active Ontology tab, 88 application, 86 class hierarchies

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, 224 relations definition, 135 service class, 134 service goal definition, 223 travel agency modeling, 223 WSMO-lite, 137 ■ Index Web Services Modeling Toolkit (WSMT), 141 Wikidata, 65 WSMO-Lite, 137 „„         X XHTML Friends Network (XFN), 30 XHTML MetaData Profiles (XMDP), 31 „„         Y Yet Another Great Ontology (YAGO), 67 „„         Z ZOOMA, 91

AI in Museums: Reflections, Perspectives and Applications

by Sonja Thiel and Johannes C. Bernhardt  · 31 Dec 2023  · 321pp  · 113,564 words

and standard data or authority files. The collection data is, moreover, linked to high-level ontologies, vocabularies, or thesauri systems such as AAT, GND, Geonames, Wikidata, or ICONCLASS, which ensure the correct use of terms and provide additional context. These sources of knowledge representation provide a high-quality source for machine

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provides a representative snapshot of the current state of research, thus painting a sample picture of AI roll-out in museums world1 https://www.wikidata.org/wiki/Wikidata:WikiProject_Museum_AI_projects. 132 Part 2: Perspectives wide. This review of research from the field was the starting point for the seminar to

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for NER (Labusch/Neudecker/Zellhöfer 2019). Furthermore, to disambiguate the entities recognized and link them to authority data (in this case, Wikidata QIDs), knowledge bases were constructed using Wikipedia and Wikidata for German, French, and English, and a purpose-trained BERT context disambiguation model was developed (Labusch/Neudecker 2020) that decides for

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suggests interesting topics based on individual interests compiled by means of an optional onboarding survey, and links automatically recognized entities to external sources, primarily from Wikidata. A personalized approach ensures that users engage with content that matches their preferences. To support access, an AI-based translation service enables text and information

This Is for Everyone: The Captivating Memoir From the Inventor of the World Wide Web

by Tim Berners-Lee  · 8 Sep 2025  · 347pp  · 100,038 words

Description Format, or RDF. Starting with this language for simple facts, we began to build massive public data encyclopaedias. If you look at locations like WikiData and various public databases, most of it is mere facts – just gazillions of them. RDF gave us a clean and elegant way to represent these

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Cod ref1 WHATWG (Web Hypertext Application Technology Working Group) ref1 White, E. B. ref1 Wide Area Information System (WAIS) ref1 widgets ref1 Wiki pages ref1 WikiData ref1 Wikipedia ref1, ref2 Willets, David ref1 Williams, David ref1, ref2, ref3 Windows ref1, ref2, ref3, ref4 windsurfing ref1, ref2 Wizard’s Workshop ref1, ref2