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Keyword-Based Search Over Databases: A Roadmap for a Reference Architecture Paired with an Evaluation Framework Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Improving Open Information Extraction for Semantic Web Tasks

Authors : Cheikh Kacfah Emani, Catarina Ferreira Da Silva, Bruno Fiès, Parisa Ghodous

Published in: Transactions on Computational Collective Intelligence XXI

Publisher: Springer Berlin Heidelberg

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Abstract

Open Information Extraction (OIE) aims to automatically identify all the possible assertions within a sentence. Results of this task are usually a set of triples (subject, predicate, object). In this paper, we first present what OIE is and how it can be improved when we work in a given domain of knowledge. Using a corpus made up of sentences in building engineering construction, we obtain an improvement of more than 18 %. Next, we show how OIE can be used at a base of a high-level semantic web task. Here we have applied OIE on formalisation of natural language definitions. We test this formalisation task on a corpus of sentences defining concepts found in the pizza ontology. At this stage, 70.27 % of our 37 sentences-corpus are fully rewritten in OWL DL.

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previous chapter Subject-Related Message Filtering in Social Media Through Context-Enriched Language Models
next chapter Searching Web 2.0 Data Through Entity-Based Aggregation
Footnotes
2
An exhaustive list of labels for phrases is available in the Penn Treebank [6].
 
4
With a large ontology, such comparison must take advantage of an index for the sake of scalability.
 
6
\(r_i\) is the subsumption or the set of elements of a more complex restriction (URI of the restriction property, OWL keywords for the type of the restriction, etc.) as explained in the introduction of Sect. 4.3.
 
7
Only for better understanding. The choice of or would not have changed anything.
 
10
Concepts’ tokens are usually surrounded by adjectives, adverbs, prepositions, etc.
 
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Metadata
Title
Improving Open Information Extraction for Semantic Web Tasks
Authors
Cheikh Kacfah Emani
Catarina Ferreira Da Silva
Bruno Fiès
Parisa Ghodous
Copyright Year
2016
Publisher
Springer Berlin Heidelberg
Book
Transactions on Computational Collective Intelligence XXI

Print ISBN: 978-3-662-49520-9

Electronic ISBN: 978-3-662-49521-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-662-49521-6_6

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