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Erschienen in:
Modeling, Generating, and Publishing Knowledge as Linked Data Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Extracting Knowledge Claims for Automatic Evidence Synthesis Using Semantic Technology

verfasst von : Jinlong Guo

Erschienen in: Knowledge Engineering and Knowledge Management

Verlag: Springer International Publishing

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Abstract

Systematic review, a form of evidence synthesis that critically appraises existing studies on the same topic and synthesizes study results, helps reduce the evidence gap. However, keeping the systematic review up-to-date is a great challenge partly due to the difficulty in interpreting the conclusion of a systematic review. A promising approach to this challenge is to make semantic representation of the claims made in both the systematic review and the included studies it synthesizes so that it’s possible to automatically predict whether the conclusion of a systematic review changes given a new study. In this dissertation work, we developed a taxonomy to represent knowledge claims both in systematic review and its included studies with the goal of automatically updating a systematic review. We then developed machine learning models to automatically predict a synthesized claim from claims in individual studies.

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Vorheriges Kapitel Semantic Data Integration for Industry 4.0 Standards
Nächstes Kapitel A Proposal for Self-Service OLAP Endpoints for Linked RDF Datasets
Fußnoten
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Metadaten
Titel
Extracting Knowledge Claims for Automatic Evidence Synthesis Using Semantic Technology
verfasst von
Jinlong Guo
Copyright-Jahr
2017
Buch
Knowledge Engineering and Knowledge Management

Print ISBN: 978-3-319-58693-9

Electronic ISBN: 978-3-319-58694-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-58694-6_37