Freebase: a collaboratively created graph database for structuring human knowledge

By Kurt Dewitt Bollacker, Colin Evans, Praveen K. Paritosh, Tim Sturge, Jamie Taylor. 2008

5,902 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Knowledge Graphs & Representation

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Citations in the corpus

How Much Knowledge Can You Pack into the Parameters of a Language Model? (2020, methodology): “, 2013), comprising questions from web queries matched to corresponding entries in FreeBase (Bollacker et al., 2008); and TriviaQA (Joshi et al.”
Word-Entity Duet Representations for Document Ranking (2017, methodology): “Recently, large general domain knowledge bases, such as DBpe-dia [11] and Freebase [1], have emerged.”
Reading Wikipedia to Answer Open-Domain Questions (2017, methodology): “Unlike knowledge bases (KBs) such as Freebase (Bollacker et al., 2008) or DBPedia (Auer et al.”
Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index (2019, background): “Earlier work in large-scale question answering (Be-rant et al., 2013) has focused on answering questions from a structured KB such as Freebase (Bol-lacker et al., 2008).”
Key-Value Memory Networks for Directly Reading Documents (2016, methodology): “The development of large-scale Knowledge Bases (KBs) such as Freebase (Bollacker et al. , 2008) helped organize information into structured forms, prompting recent progress to focus on answering questions by converting them into logical forms that can be used to query such databases (Berant et al.…”
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2021, background): “In fact, the broader literature on automatic knowledge graph construction has shown, time and again, that machine-authored knowledge graphs do not match the quality of human-authored knowledge graphs (Etzioni et al., 2011; Mitchell et al., 2015; Bollacker et al., 2008).”
VQA: Visual Question Answering (2015, background): “However, it includes as building blocks several components that the CV, NLP, and KR [4, 6, 25, 29, 3] communities have made significant progress on during the past few decades.”
Constructing Datasets for Multi-hop Reading Comprehension Across Documents (2017, background): “, i.e., in a structured form with pre-deﬁned schema. That is, they work as part of a pipeline, and either rely on the output of a previous IE step (Banko et al., 2007), or on direct human annotation (Bollacker et al., 2008) which tends to be costly and biased in coverage. However, recent neural RC m”
Neural Approaches to Conversational AI (2018, neutral): “iewing TREC QA open benchmarks. 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 important resources for support”
The Web as a Knowledge-Base for Answering Complex Questions (2018, methodology): “Note that C OMP Q does not suffer from any string matching issue, as it operates directly against the Freebase KB and thus is guaranteed to output the answer in the correct form.”
Neural Text Generation from Structured Data with Application to the Biography Domain (2016, background): “An infobox is a fact table describing a person, similar to a person subgraph in a knowledge base (Bollacker et al., 2008; Ferrucci, 2012).”
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia (2016, methodology): “Mintz et al. (2009) apply distant supervision to extracting Freebase triples (Bollacker et al., 2008) from Wikipedia text, analogous to the relational part of W IKI R EADING .”
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (2019, methodology): “…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., 2014; Carlson et al., 2010; Nakashole et al., 2011, 2012; Niu, 2012) into relational…”
Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets (2020, methodology): “Questions were obtained by mining a search engine, and answers are Freebase entities (Bollacker et al., 2008) annotated by crowdworkers.”
Quasar: Datasets for Question Answering by Search and Reading (2017, methodology): “ng questions posed in natural language. Depending on the knowledge source available there are two main approaches for factoid QA. Structured sources, including Knowledge Bases (KBs) such as Freebase (Bollacker et al., 2008), are easier to process automatically since the information is organized acco”
Question Answering with Subgraph Embeddings (2014, background): “Freebase FREEBASE (Bollacker et al., 2008) is a huge and freely available database of general facts; data is organized as triplets (subject, type1.”
Who did What: A Large-Scale Person-Centered Cloze Dataset (2016, background): “First, reading comprehension systems must infer answers from a given unstructured passage rather than structured knowledge sources such as Freebase (Bollacker et al., 2008)”
Conversational AI: The Science Behind the Alexa Prize (2018, methodology): “Teams generally used publicly available knowledge bases such as Evi (Evi, 2017), FreeBase (Bollacker et al., 2008), Microsoft Concept Graph (Concept, 2017), etc.”
Distant supervision for relation extraction without labeled data (2009, methodology): “Our algorithm uses Freebase (Bollacker et al., 2008), a large semantic database, to provide distant supervision for relation extraction.”
Scaling Semantic Parsers with On-the-Fly Ontology Matching (2013, background): “Freebase (Bollacker et al., 2008) is a large, collaboratively authored database containing almost 40 million entities and two billion facts, covering more than 100 domains.”