YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia: Extended Abstract

By Johannes Hoffart, Fabian M. Suchanek, Klaus Berberich, Gerhard Weikum. 2013

1,320 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Geography, Planning and Development, Evaluation & Benchmarks, Signal Processing

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Citations in the corpus

A Neural Network for Factoid Question Answering over Paragraphs (2014, background): “A more apt comparison would be to IBM ’s proprietary Watson system (Lally et al., 2012) for Jeopardy, which is limited to single sentences, or to models trained on Yago (Hof-fart et al., 2013).”
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (2019, methodology): “…as relational schemas using expert knowledge (Lenat, 1995; Bodenreider, 2004; Miller, 1995), semistructured text extraction (Suchanek et al., 2007; Hoffart et al., 2013; Auer et al., 2007; Bollacker et al., 2008) and unstructured text extraction (Dong et al., 2014; Carlson et al., 2010; Nakashole…”
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, background): “Generic KGs (e.g., DBpedia (Bizer et al. 2009), YAGO2 (Hoffart et al. 2013), Freebase (Bollacker et al. 2008)) may contain over a million entities and close to a billion relationships.”
Explicit Knowledge-based Reasoning for Visual Question Answering (2015, methodology): “ed KBs are constructed either by manual-annotation/crowd-sourcing (e.g., DBpedia [2], Freebase [6] and Wikidata [39]), or by automatically extracting from unstructured/semistructured data (e.g., YAGO [22, 28], OpenIE [3, 14, 15], NELL [9], NEIL [10, 11]). The KB we use here is DBpedia, which contain”
FVQA: Fact-Based Visual Question Answering (2016, methodology): “le structured KBs are constructed either by manual annotation (e.g., DBpedia [23], Freebase [25] and Wikidata [29]), or by automatic extraction from unstructured/semistructured data (e.g., YAGO [28], [33], OpenIE [24], [34], [35], NELL [26], NEIL [27], [36], WebChild [37], [38], ConceptNet [39]). Th”
Don't just listen, use your imagination: Leveraging visual common sense for non-visual tasks (2015, background): “Most existing common sense knowledge bases rely on knowledge described via text – either mined [6, 26, 31] or manually entered [37, 44, 5, 45].”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “Since higher arity relations involving time and location are relatively common, the YAGO2 project extended the SPO triple format to the (subject, predicate, object, time, location) (SPOTL) format to model temporal and spatial information about relationships explicitly, without transforming them to b”
Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO (2017, neutral): “[28] mention the following reasons for that:”
Data X-Ray: A Diagnostic Tool for Data Errors (2015, background): “Web-scale knowledge bases [20,24,34] often contain a large number of errors, resulting both from mistakes, omissions, or oversights in the knowledge extraction process, and from erroneous, out-of-date information from the web sources.”
Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries (2020, background): “…retrival of triples LMs are sufﬁcient as a proof-of-concept in principle, structured KBs also allow more complex queries than the ones considered here, such as 1-to-n relations, multihop inference, queries involving numerical ranges, or facts qualiﬁed by time and location (Hoffart et al., 2013).”
Linked Disambiguated Distributional Semantic Networks (2016, background): “Recent years have witnessed an impressive amount of work on the automatic construction of wide-coverage knowledge resources from Wikipedia [3, 13] and the Web [7].”
Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems (2017, neutral): “Existing broad-coverage knowledge bases such as DBpedia, Freebase, or YAGO, as well as domain-speciﬁc ontologies such as BioPortal and NCBO can provide useful knowledge for inferring speciﬁc ﬁne-grained types.”
Collective Entity Resolution with Multi-Focal Attention (2016, methodology): “Our baseline is slightly better than Lazic et al. (2015), but degrades after adding YAGO aliases which increase ambiguity.”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “67M [216] Daily [216] YAGO [99] Knowledge graph 15,403 [77] 138,056 [77] Mostly yearly [77] Wikidata [133] Knowledge graph 11,134 [77] 150,079 [77] Yearly Reddit [127] Social network 55,863 858,49 Seconds Enron [124] Email network 151 [39] 50.”
Domain-specific Knowledge Extraction from the Web of Data (2018, methodology): “2007], YAGO [Hoffart et al. 2013], and WikiData [Erxleben et al.”
KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts (2015, methodology): “…et al., 2007) are open methods for learning multi-relation KBs. Finally, NELL (Carlson et al., 2010; Mitchell et al., 2015), FreeBase (Google, 2011) and Yago (Suchanek et al., 2007; Hoffart et al., 2013) are ontology-guided methods for extracting KBs containing both hierarchies and relations.”