DBpedia: A Nucleus for a Web of Open Data

By Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, Z. Ives. 2007

5,749 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Information Systems, Evaluation & Benchmarks, Knowledge Graphs & Representation

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Citations in the corpus

Reading Wikipedia to Answer Open-Domain Questions (2017, background): “Unlike knowledge bases (KBs) such as Freebase (Bollacker et al., 2008) or DB-Pedia (Auer et al., 2007), which are easier for computers to process but too sparsely populated for open-domain question answering (Miller et al., 2016), Wikipedia contains up-to-date knowledge that humans are interested in”
Key-Value Memory Networks for Directly Reading Documents (2016, methodology): “The creation of large-scale KBs (Auer et al., 2007; Bollacker et al., 2008) have led to the development of a new class of QA methods based on semantic parsing (Berant et al.”
Mass-Editing Memory in a Transformer (2022, background): “Pioneering hand-curated efforts (Lenat, 1995; 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…”
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge (2016, background): “ConceptNet 5.5 is built from the following sources: • OpenCyc, a hierarchy of hypernyms provided by Cyc (Lenat and Guha 1989), a system that represents common sense knowledge in predicate logic • A subset of DBPedia (Auer et al. 2007), a network of facts extracted from Wikipedia infoboxes With the c”
Neural Approaches to Conversational AI (2018, neutral): “sks and models are extended to the conversational QA setting. 3.1 Knowledge Base Organizing the world’s facts and storing them in a structured database, large scale Knowledge Bases (KB) like DBPedia (Auer et al., 2007), Freebase (Bollacker et al., 2008) and Yago (Suchanek et al., 2007) have become i”
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (2019, methodology): “Previous work has developed models capable of reading and extracting semi-structured text (Suchanek et al., 2007; Hoffart et al., 2013; Auer et al., 2007; Bollacker et al., 2008) and unstructured text (Dong et al.”
Ask Me Anything: Free-Form Visual Question Answering Based on Knowledge from External Sources (2015, methodology): “The external data source that we use here is DBpedia [2].”
A survey of data fusion in smart city applications (2019, background): “Thus, DBpedia [235] is designed to address the aforementioned issue using public and private stocks of semantic web.”
Complex Embeddings for Simple Link Prediction (2016, background): “Web-scale knowledge bases (KBs) provide a structured representation of world knowledge, with projects such as DBPedia (Auer et al., 2007), Freebase (Bollacker et al., 2008) or the Google Knowledge Vault (Dong et al., 2014).”
Explicit Knowledge-based Reasoning for Visual Question Answering (2015, methodology): “, 2008] and DBpedia [Auer et al., 2007]) or by automatic extraction from unstructured/semi-structured data (e.”
Named Entity Recognition with Bidirectional LSTM-CNNs (2015, methodology): “Unfortunately the difference is minuscule for OntoNotes, most likely because our lexicon does not match DBpedia categories well.”
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base (2015, background): “Organizing the world’s facts and storing them in a structured database, large-scale knowledge bases (KB) like DBPedia (Auer et al., 2007) and Freebase (Bollacker et al.”
Combining Fact Extraction and Verification with Neural Semantic Matching Networks (2018, methodology): “(3) Rather than depending on structured machine-friendly knowledge bases such as Freebase (Bollacker et al. 2008) and DBpedia (Auer et al. 2007), we formalize the three subtasks as a similar textual semantic matching problem and propose one of the first neural systems that are able to conduct eviden”
Representing General Relational Knowledge in ConceptNet 5 (2012, methodology): “• The semantic connections between Wikipedia articles represented in DBPedia (Auer et al., 2007), with cross-references to the corresponding DBPedia resources.”
Information Extraction over Structured Data: Question Answering with Freebase (2014, methodology): “With the recent growth in KBs such as DBPedia (Auer et al., 2007), Freebase (Bollacker et al., 2008) and Yago2 (Hoffart et al., 2011), it has become more practical to consider answering questions across wider domains, with commercial systems including Google Now, based on Google’s Knowledge Graph, a”
Embedding Entities and Relations for Learning and Inference in Knowledge Bases (2014, background): “Recent years have witnessed a rapid growth of knowledge bases (KBs) such as Freebase 1 , DBPe-dia (Auer et al., 2007), and YAGO (Suchanek et al., 2007).”
FVQA: Fact-Based Visual Question Answering (2016, methodology): “Large-scale structured Knowledge Bases (KBs) [22], [23], [24], [25], [26], [27], [28] in contrast, offer an explicit, and”
What Value Do Explicit High Level Concepts Have in Vision to Language Problems? (2015, background): “onsense-related visual questions. Our semantic attributes representation enables us to fetch higher-level knowledge-based informations beyond images since all existing knowledge bases (such as DBpedia[3], Freebase[6]) are using semantic words, but not images, as KB entities. In the following experim”