Many techniques in the social sciences and graph theory deal with the problem of examining and analyzing patterns found in the underlying structure and associations of a group of ...
Jeremy Kubica, Andrew W. Moore, David Cohn, Jeff G...
Shortage of manually labeled data is an obstacle to supervised relation extraction methods. In this paper we investigate a graph based semi-supervised learning algorithm, a label ...
Jinxiu Chen, Dong-Hong Ji, Chew Lim Tan, Zheng-Yu ...
The paper presents a kernel for learning from ordered hypergraphs, a formalization that captures relational data as used in Inductive Logic Programming (ILP). The kernel generaliz...
Previous studies of data-driven dependency parsing have shown that the distribution of parsing errors are correlated with theoretical properties of the models used for learning an...
Learning temporal graph structures from time series data reveals important dependency relationships between current observations and histories. Most previous work focuses on learn...