J
Johannes Hoffart
Researcher at Goldman Sachs
Publications - 44
Citations - 3933
Johannes Hoffart is an academic researcher from Goldman Sachs. The author has contributed to research in topics: Entity linking & Context (language use). The author has an hindex of 16, co-authored 42 publications receiving 3413 citations. Previous affiliations of Johannes Hoffart include Max Planck Society & University of Dayton.
Papers
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Journal ArticleDOI
YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia
TL;DR: YAGO2 as mentioned in this paper is an extension of the YAGO knowledge base, in which entities, facts, and events are anchored in both time and space, and it contains 447 million facts about 9.8 million entities.
Proceedings Article
Robust Disambiguation of Named Entities in Text
Johannes Hoffart,Mohamed Amir Yosef,Ilaria Bordino,Hagen Fürstenau,Manfred Pinkal,Marc Spaniol,Bilyana Taneva,Stefan Thater,Gerhard Weikum +8 more
TL;DR: A robust method for collective disambiguation is presented, by harnessing context from knowledge bases and using a new form of coherence graph that significantly outperforms prior methods in terms of accuracy, with robust behavior across a variety of inputs.
Proceedings ArticleDOI
YAGO2: exploring and querying world knowledge in time, space, context, and many languages
Johannes Hoffart,Fabian M. Suchanek,Klaus Berberich,Edwin Lewis-Kelham,Gerard de Melo,Gerhard Weikum +5 more
TL;DR: YAGO2, an extension of the YAGO knowledge base with focus on temporal and spatial knowledge, is presented, automatically built from Wikipedia, GeoNames, and WordNet, and contains nearly 10 million entities and events, as well as 80 million facts representing general world knowledge.
Book ChapterDOI
YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames
TL;DR: This paper explains how YAGO is built from its sources, how its quality is evaluated, how a user can access it, and how other projects utilize it.
Proceedings ArticleDOI
KORE: keyphrase overlap relatedness for entity disambiguation
TL;DR: A novel notion of semantic relatedness between two entities represented as sets of weighted (multi-word) keyphrases, with consideration of partially overlapping phrases is developed, which improves the quality of prior link-based models, and also eliminates the need for explicit interlinkage between entities.