Multimodal KB Harvesting for Emerging Spatial Entities

Yeo, Jinyoung; Cho, Hyunsouk; Park, Jin-Woo; Hwang, Seung-won

doi:10.1109/tkde.2017.2651805

Cited by 4 publications

(1 citation statement)

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“…Further methods for early discovery of emerging entities have been developed, for example, by [261,647,6,435,661], with emphasis on social media or web tables as sources. For entities of type event, news digests such as daily pages in Wikipedia (e.g., https://en.wikipedia.org/wiki/Portal:Current_events/2020_July_14) are valuable assets, too.…”

Section: Emerging Entitiesmentioning

confidence: 99%

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

Weikum¹,

Dong²,

Razniewski³

et al. 2020

Preprint

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Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics.This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

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