  Wikipedia is a huge opportunity for machine learning, being the largest
semi-structured base of knowledge available. Because of this, many works
examine its contents, and focus on structuring it in order to make it usable in
learning tasks, for example by classifying it into an ontology. Beyond its
textual contents, Wikipedia also displays a typical graph structure, where
pages are linked together through citations. In this paper, we address the task
of integrating graph (i.e. structure) information to classify Wikipedia into a
fine-grained named entity ontology (NE), the Extended Named Entity hierarchy.
To address this task, we first start by assessing the relevance of the graph
structure for NE classification. We then explore two directions, one related to
feature vectors using graph descriptors commonly used in large-scale network
analysis, and one extending flat classification to a weighted model taking into
account semantic similarity. We conduct at-scale practical experiments, on a
manually labeled subset of 22,000 pages extracted from the Japanese Wikipedia.
Our results show that integrating graph information succeeds at reducing
sparsity of the input feature space, and yields classification results that are
comparable or better than previous works.
