Abstract
| Language | English |
|---|---|
| Title of host publication | KNOW@ LOD |
| State | Published - 2014 |
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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 proceeding › Conference contribution
TY - GEN
T1 - Simplifying RDF Data for Graph-Based Machine Learning.
AU - Bloem,P.
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
ER -