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Diefficiency Metrics: Measuring the Continuous Efficiency of Query Processing Approaches Read chapter Read first chapter

2017 | OriginalPaper | Chapter

WebIsALOD: Providing Hypernymy Relations Extracted from the Web as Linked Open Data

Authors : Sven Hertling, Heiko Paulheim

Published in: The Semantic Web – ISWC 2017

Publisher: Springer International Publishing

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Abstract

Hypernymy relations are an important asset in many applications, and a central ingredient to Semantic Web ontologies. The IsA database is a large collection of such hypernymy relations extracted from the Common Crawl. In this paper, we introduce WebIsALOD, a Linked Open Data release of the IsA database, containing 400M hypernymy relations, each provided with rich provenance information. As the original dataset contained more than 80% wrong, noisy extractions, we run a machine learning algorithm to assign confidence scores to the individual statements. Furthermore, 2.5M links to DBpedia and 23.7k links to the YAGO class hierarchy were created at a precision of 97%. In total, the dataset contains 5.4B triples.

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previous chapter The CEDAR Workbench: An Ontology-Assisted Environment for Authoring Metadata that Describe Scientific Experiments
next chapter Ontology-Based Data Access to Slegge
Footnotes
2
NP stands for noun phrase.
 
4
We restricted the workers to have a 95% approval rate and a minimum of 100 approved HITs (human intelligence tasks), following the recommendations by [3, 5], and restricted their location to the US to attract a large fraction of native speakers.
 
7
The reason why we did not use the gold standard of the full dataset for training is its imbalance (cf. Sect. 2), i.e., the number of positive examples (only 37 out of 500) is too low for learning a meaningful model.
 
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Metadata
Title
WebIsALOD: Providing Hypernymy Relations Extracted from the Web as Linked Open Data
Authors
Sven Hertling
Heiko Paulheim
Copyright Year
2017
Book
The Semantic Web – ISWC 2017

Print ISBN: 978-3-319-68203-7

Electronic ISBN: 978-3-319-68204-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68204-4_11

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