© 2019 World Scientific Publishing Company. This paper presents a classifier architecture that is able to deal with classification of interlinked entities when the only information available is the existing relationships between these entities, i.e. no semantic content is known for either the entities or their relationships. After proposing a classifier to deal with this problem, we provide extensive experimental evaluation showing that our proposed method is sound and that it is able to achieve high accuracy, in most cases much higher than other already existing algorithms configured to tackle this very same problem. The contributions of this paper are twofold: first, it presents a classifier for interlinked entities that outperforms most of the existing algorithms when the only information available is the relationships between these entities; second, it reveals the power of using label independent (LI) features extracted from network structural properties in the bootstrapping phases of relational classification.
|Journal||International Journal of Software Engineering and Knowledge Engineering|
|Publication status||Published - 1 Jan 2019|
- network learning
- Networked classification
- networked data
- relational learning
- support vector machines