Towards Efficient Exploitation of Large Knowledge Bases by Context Graphs
Abstract
One problem related to the exploitation of knowledge graphs, in particular when
processing with machine learning methods, is the scaling up problem. We propose
here a method to significantly reduce the size of the used graphs to focus on a useful
part in a given usage context. We define the notion of context graph as an extract
from one or more general knowledge bases (such as DBpedia,Wikidata, Yago) that
contains the set of information relevant to a specific domain while preserving the
properties of the original graph.We validate the approach on a DBpedia excerpt for
entities related to the Data&Musée project and the KORE reference set according
to two aspects: the coverage of the context graph and the preservation of the similarity
between its entities. The results show that the use of context graphs makes
the exploitation of large knowledge bases more manageable and efficient while preserving
the properties of the initial graph.
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