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Structuring Linked Data Search Results Using Probabilistic Soft Logic Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Seed, an End-User Text Composition Tool for the Semantic Web

Authors : Bahaa Eldesouky, Menna Bakry, Heiko Maus, Andreas Dengel

Published in: The Semantic Web – ISWC 2016

Publisher: Springer International Publishing

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Abstract

Despite developments of Semantic Web-enabling technologies, the gap between non-expert end-users and the Semantic Web still exists. In the field of semantic content authoring, tools for interacting with semantic content remain directed at highly trained individuals. This adds to the challenges of bringing user-generated content into the Semantic Web. In this paper, we present Seed, short for Semantic Editor, an extensible knowledge-supported natural language text composition tool for non-experienced end-users. It enables automatic as well as semi-automatic creation of standards based semantically annotated textual content with focus on the task of text composition. We point out the structure of Seed, compare it with related work and explain how it excels at utilizing Linked Open Data and state of the art Natural Language Processing to realize user-friendly generation of textual content for the Semantic Web. We also present experimental evaluation results involving a diverse group of 120 participants, which showed that Seed helped end-users easily create and interact with semantic content with nearly no prerequisite knowledge.

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previous chapter User Validation in Ontology Alignment
next chapter Exception-Enriched Rule Learning from Knowledge Graphs
Footnotes
1
Evaluation data available at http://​tiny.​cc/​seed-iswc2016-data.
 
2
Seed, the Semantic Editor - http://​tiny.​cc/​seed-video.
 
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Metadata
Title
Seed, an End-User Text Composition Tool for the Semantic Web
Authors
Bahaa Eldesouky
Menna Bakry
Heiko Maus
Andreas Dengel
Copyright Year
2016
Book
The Semantic Web – ISWC 2016

Print ISBN: 978-3-319-46522-7

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

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46523-4_14

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