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An Empirical Evaluation of RDF Graph Partitioning Techniques Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Fuzzy Semantic Labeling of Semi-structured Numerical Datasets

Authors : Ahmad Alobaid, Oscar Corcho

Published in: Knowledge Engineering and Knowledge Management

Publisher: Springer International Publishing

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Abstract

SPARQL endpoints provide access to rich sources of data (e.g. knowledge graphs), which can be used to classify other less structured datasets (e.g. CSV files or HTML tables on the Web). We propose an approach to suggest types for the numerical columns of a collection of input files available as CSVs. Our approach is based on the application of the fuzzy c-means clustering technique to numerical data in the input files, using existing SPARQL endpoints to generate training datasets. Our approach has three major advantages: it works directly with live knowledge graphs, it does not require knowledge-graph profiling beforehand, and it avoids tedious and costly manual training to match values with types. We evaluate our approach against manually annotated datasets. The results show that the proposed approach classifies most of the types correctly for our test sets.

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previous chapter An Empirical Evaluation of RDF Graph Partitioning Techniques
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Footnotes
2
We are not referring here to the gold standards that are built manually or the semantic models that are constructed by domain experts.
 
3
We use the same notation and variable names as in [3] (Bezdek et al.) 1984.
 
6
Two files related to the class person is missing from the classification.
 
7
Which means weight in Spanish.
 
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Metadata
Title
Fuzzy Semantic Labeling of Semi-structured Numerical Datasets
Authors
Ahmad Alobaid
Oscar Corcho
Copyright Year
2018
Book
Knowledge Engineering and Knowledge Management

Print ISBN: 978-3-030-03666-9

Electronic ISBN: 978-3-030-03667-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-03667-6_2

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