Simplifying RDF Data for Graph-Based Machine Learning.

P. Bloem, A. Wibisono, G.K.D de Vries

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

From the perspective of machine learning and data mining applications, expressing data in RDF rather than a domain-specific for- mat can add complexity and obfuscate the internal structure. We in- vestigate and illustrate this issue with an example where bio-molecular graph datasets are expressed in RDF. We use this example to inspire pre- processing techniques which reverse some of the complications of adding semantic annotations, exposing those patterns in the data that are most relevant to machine learning. We test these methods in a number of clas- sification experiments and show that they can improve performance both for our example datasets and real-world RDF datasets.
LanguageEnglish
Title of host publicationKNOW@ LOD
StatePublished - 2014

Cite this

Bloem, P., Wibisono, A., & de Vries, G. K. D. (2014). Simplifying RDF Data for Graph-Based Machine Learning. In KNOW@ LOD
Bloem, P. ; Wibisono, A. ; de Vries, G.K.D. / Simplifying RDF Data for Graph-Based Machine Learning.KNOW@ LOD. 2014.
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title = "Simplifying RDF Data for Graph-Based Machine Learning.",
abstract = "From the perspective of machine learning and data mining applications, expressing data in RDF rather than a domain-specific for- mat can add complexity and obfuscate the internal structure. We in- vestigate and illustrate this issue with an example where bio-molecular graph datasets are expressed in RDF. We use this example to inspire pre- processing techniques which reverse some of the complications of adding semantic annotations, exposing those patterns in the data that are most relevant to machine learning. We test these methods in a number of clas- sification experiments and show that they can improve performance both for our example datasets and real-world RDF datasets.",
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Bloem, P, Wibisono, A & de Vries, GKD 2014, Simplifying RDF Data for Graph-Based Machine Learning. in KNOW@ LOD.

Simplifying RDF Data for Graph-Based Machine Learning. / Bloem, P.; Wibisono, A.; de Vries, G.K.D.

KNOW@ LOD. 2014.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Simplifying RDF Data for Graph-Based Machine Learning.

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AU - Wibisono,A.

AU - de Vries,G.K.D

PY - 2014

Y1 - 2014

N2 - From the perspective of machine learning and data mining applications, expressing data in RDF rather than a domain-specific for- mat can add complexity and obfuscate the internal structure. We in- vestigate and illustrate this issue with an example where bio-molecular graph datasets are expressed in RDF. We use this example to inspire pre- processing techniques which reverse some of the complications of adding semantic annotations, exposing those patterns in the data that are most relevant to machine learning. We test these methods in a number of clas- sification experiments and show that they can improve performance both for our example datasets and real-world RDF datasets.

AB - From the perspective of machine learning and data mining applications, expressing data in RDF rather than a domain-specific for- mat can add complexity and obfuscate the internal structure. We in- vestigate and illustrate this issue with an example where bio-molecular graph datasets are expressed in RDF. We use this example to inspire pre- processing techniques which reverse some of the complications of adding semantic annotations, exposing those patterns in the data that are most relevant to machine learning. We test these methods in a number of clas- sification experiments and show that they can improve performance both for our example datasets and real-world RDF datasets.

M3 - Conference contribution

BT - KNOW@ LOD

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Bloem P, Wibisono A, de Vries GKD. Simplifying RDF Data for Graph-Based Machine Learning. In KNOW@ LOD. 2014.