DBpedia: A Multilingual Cross-domain Knowledge Base

By Pablo N. Mendes, Max Jakob, Christian Bizer. 2012

217 citations recorded by the source corpus.

The DBpedia project extracts structured information from Wikipedia editions in 97 different languages and combines this information into a large multi-lingual knowledge base covering many specific domains and general world knowledge. The knowledge base contains textual descriptions (titles and abstracts) of concepts in up to 97 languages. It also contains structured knowledge that has been extracted from the infobox systems of Wikipedias in 15 different languages and is mapped onto a single consistent ontology by a community effort. The knowledge base can be queried using the SPARQL query language and all its data sets are freely available for download. In this paper, we describe the general DBpedia knowledge base and as well as the DBpedia data sets that specifically aim at supporting computational linguistics tasks. These task include Entity Linking, Word Sense Disambiguation, Question Answering, Slot Filling and Relationship Extraction. These use cases are outlined, pointing at added value that the structured data of DBpedia provides.

Subfields: Artificial Intelligence, Information Retrieval & Search

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ihab F. Ilyas
2. Claire Gardent
3. Anastasia Shimorina
4. Laura Perez-Beltrachini
5. Nan Tang
6. Sanjay Krishnan
7. Mourad Ouzzani
8. Xu Chu

In Information Retrieval & Search

1. Andrew Chisholm
2. Samuel Madden
3. Ihab F. Ilyas
4. Mourad Ouzzani
5. Ben Hachey
6. Nan Tang

Citations in the corpus

• Creating Training Corpora for NLG Micro-Planners (2017, background): “DBPe-dia is a multilingual knowledge base that was built from various kinds of structured information contained in Wikipedia (Mendes et al., 2012).”
• Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery (2018, methodology): “• MASSACHUSETTS OPEN DATA: We use 300+ CSV files from the Massachusetts Open Data portal and used DBPedia [19] as reference ontology.”
• Entity Disambiguation with Web Links (2015, methodology): “We use existing conditional probability estimates from the DBpedia Lexicalizations data set (Mendes et al., 2012).”
• Knowledge Graphs (2020, unknown): “The content in the DBpedia knowledge graph is not only multidomain, but also multilingual: as of 2012, DBpedia contained labels and abstracts in up to 97 different languages [347].”