Knowledge Tree
DBpedia
Facts (30)
Sources
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 30 facts
procedureDBpedia connects equal entities across different wikis using sameAs links derived from interwiki links, and utilizes a testing library based on SHACL and other integrity tests to debug data problems.
claimDBpedia is a popular knowledge graph that establishes a central access point for the Semantic Web.
claimKnowledge graph solutions often use rule-based mappings to extract entities and relations from semi-structured sources, as seen in DBpedia, Yago, DRKG, VisualSem, and WorldKG.
referenceThe paper 'The new dbpedia release cycle: Increasing agility and efficiency in knowledge extraction workflows' by M. Hofer, S. Hellmann, M. Dojchinovski, and J. Frey, published in the International Conference on Semantic Systems in 2020, discusses improvements to the DBpedia release cycle.
referenceA.P. Aprosio, C. Giuliano, and A. Lavelli demonstrated a method for extending the coverage of DBpedia properties using distant supervision over Wikipedia in 2013.
claimIn the example provided in the source text, the mention 'Richard David James' is linked to the DBpedia entity 'dbr:Richard_David_James'.
referenceThe authors of 'Construction of Knowledge Graphs: State and Challenges' recommend starting Knowledge Graph construction with large, curated 'premium' data sources such as Wikipedia and DBpedia.
procedureThe live extraction process for DBpedia monitors ontology mapping wiki changes and schedules affected pages for re-extraction, but skips post-processing and quality assurance steps to improve performance.
referenceD. Ritze, O. Lehmberg, and C. Bizer published 'Matching html tables to dbpedia' in the Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics in 2015.
referenceDBpedia Spotlight is a tool that performs named entity extraction and links those mentions to the DBpedia knowledge graph.
claimDBpedia supports deep provenance by linking each extracted value to the revision ID of the source article and the specific extractor used.
claimSince 2020, the DBpedia extraction cycle has utilized the Databus platform to manage data releases, versioning, data quality reports, and automatic metadata generation.
claimDBpedia and YAGO are the only knowledge graph construction solutions that perform an automatic consistency check.
claimDBpedia checks for dataset completeness and measures quality against the previous version of the dataset.
claimDBpedia and Yago are limited to batch updates that require a full recomputation of the knowledge graph.
referenceH. Paulheim and S.P. Ponzetto published 'Extending DBpedia with Wikipedia List Pages' at the NLP-DBPEDIA workshop at the International Semantic Web Conference in 2013.
measurementThe DBpedia knowledge graph, established in 2007, contains 50 million entities and 21 billion facts, utilizing RDF format.
referenceS. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z.G. Ives authored 'DBpedia: A Nucleus for a Web of Open Data', published in the ISWC/ASWC proceedings in 2007.
claimDBpedia, YAGO, and NELL integrate information from Wikipedia as a primary source for knowledge.
claimDBpedia requires manual changes to its ontology and data mappings, which must be loaded before running a new batch update.
procedureThe DBpedia community manually curates the DBpedia ontology and infobox mappings through a publicly accessible MediaWiki interface, from which the DBpedia Extraction Framework (DIEF) fetches the latest versions for each extraction run.
procedureThe DBpedia post-processing phase includes a type consistency check to ensure entities and relations adhere to DBpedia ontology definitions, followed by a completion phase that materializes transitive type (is-a) relations.
claimDBpedia enriches integrated knowledge graph data by attaching additional entity type information based on current ontology and relation data.
claimDBpedia extracts data from 140 sources (Wikipedias), interlinking equivalent entities using 'sameAs' connections found in page articles.
referenceEmpirical evaluation on DBpedia by Acosta et al. [234] shows that combining expert crowdsourcing and paid microtasks on Amazon Mechanical Turk is a complementary and affordable way to enhance knowledge graph data quality.
referenceThe article 'Dbpedia and the live extraction of structured data from wikipedia' by M. Morsey, J. Lehmann, S. Auer, C. Stadler, and S. Hellmann, published in Program in 2012, discusses live extraction of structured data for DBpedia.
claimYAGO, DBpedia, NELL, and Wikidata are examples of open knowledge graphs.
measurementDBpedia performs monthly batch extractions consuming data from up to 140 wikis, including various language-specific Wikipedia versions, Wikidata, and Wikimedia Commons, with each release undergoing completeness and quality validation.
procedureThe WorldKG ontology construction process involves fetching geographic class information tags from OpenStreetMap, using key-value pairs to infer a class hierarchy, and aligning these classes with Wikidata and DBpedia using an unsupervised machine learning approach followed by manual verification.
claimUtilizing disambiguated aliases from high-quality sources, such as Wikipedia redirects or DBpedia’s dbo:alias property, increases the coverage of entity dictionaries.