Freebase: A Shared Database of Structured General Human Knowledge

By Kurt Dewitt Bollacker, Robert Cook, Patrick Tufts. 2007

279 citations recorded by the source corpus.

Subfields: Knowledge Graphs & Representation

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Citations in the corpus

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…”
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them (2021, methodology): “KBQA A number of early approaches in ODQA focused on using structured KBs (Berant et al., 2013) such as Freebase (Bollacker et al., 2007), with recent examples from F´evry et al. (2020) and Verga et al. (2020).”
A Survey on Application of Knowledge Graph (2020, background): “Nowadays, “Knowledge Graph” is also referring to semantic web knowledge bases such as DBpedia [4], YAGO [5], Wikidata or Freebase [6].”
Knowledge Graphs (2020, unknown): “Many of the most prominent open knowledge graphs – including DBpedia [310], YAGO [505], Freebase [54], and Wikidata [542] – cover multiple domains, representing a broad diversity of entities and relationships; we first discuss these in turn.”
Adversarial Sets for Regularising Neural Link Predictors (2017, methodology): “FREEBASE [Bollacker et al., 2007] is a large knowledge graph that stores general facts about the world. The FB122 dataset is a subset of FREEBASE regarding the topics of people, location and sports, and contains 9,738 entities, 122 relation types, and 112,476 triples. For both data sets, we used the”
Differentiable Reasoning on Large Knowledge Bases and Natural Language (2019, background): “Freebase (Bollacker, Cook, and Tufts 2007) is a large knowledge graph that stores general facts about the world.”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, background): “For example, DBpedia [40] (developed from Wikipedia) is a fundamental encyclope-dia knowledge graph, while Freebase [4] incorporates automatic extraction tools to obtain more content.”
Natural language to SQL: Where are we today? (2020, methodology): “Some techniques also require additional inputs: an open-domain knowledge base such as Freebase [7] for detecting named entities; domain-speciﬁc dictionary/ontology; a pre-built mapping table between phrases in q nl and SQL keywords; WordNet [29]; and a word embedding matrix for calculating word…”
An open-source toolkit for mining Wikipedia (2013, background): “They can either base their work on secondary structures that others have extracted from it, such as Freebase [2] and Yago [26], or they can start from scratch and build their own algorithms to mine Wikipedia directly.”
Relational Memory-Augmented Language Models (2022, background): “Although Freebase relations have been observed to improve the performance on smaller datasets (e.g. WikiText-2; Logan et al., 2019) and particular domains (e.g. movies and actors; Ahn et al., 2016), we find that RELATIONLM with Freebase relations does not improve over transformer-XL on a much larger”
Neural Natural Language Inference Models Enhanced with External Knowledge (2017, background): “…that in addition to relation embedding trained on WordNet, other relational embedding resources exist; e.g., that trained on Freebase (WikiData) (Bollacker et al., 2007), but such knowledge resources are mainly about facts (e.g., relationship between Bill Gates and Microsoft) and are less for…”