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2016 | OriginalPaper | Buchkapitel

WebBrain: Joint Neural Learning of Large-Scale Commonsense Knowledge

verfasst von : Jiaqiang Chen, Niket Tandon, Charles Darwis Hariman, Gerard de Melo

Erschienen in: The Semantic Web – ISWC 2016

Verlag: Springer International Publishing

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Abstract

Despite the emergence and growth of numerous large knowledge graphs, many basic and important facts about our everyday world are not readily available on the Web. To address this, we present WebBrain, a new approach for harvesting commonsense knowledge that relies on joint learning from Web-scale data to fill gaps in the knowledge acquisition. We train a neural network model to learn relations based on large numbers of textual patterns found on the Web. At the same time, the model learns vector representations of general word semantics. This joint approach allows us to generalize beyond the explicitly extracted information. Experiments show that we can obtain representations of words that reflect their semantics, yet also allow us to capture conceptual relationships and commonsense knowledge.

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Vorheriges Kapitel Containment of Expressive SPARQL Navigational Queries

Nächstes Kapitel Efficient Algorithms for Association Finding and Frequent Association Pattern Mining

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https://catalog.ldc.upenn.edu/LDC2006T13.

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Titel: WebBrain: Joint Neural Learning of Large-Scale Commonsense Knowledge
verfasst von: Jiaqiang Chen
Niket Tandon
Charles Darwis Hariman
Gerard de Melo
Verlag: Springer International Publishing
Buch: The Semantic Web – ISWC 2016
Print ISBN: 978-3-319-46522-7

Electronic ISBN: 978-3-319-46523-4

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46523-4_7

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