The problem of graph classification has attracted great interest in the last decade. Current research on graph classification assumes the existence of large amounts of labeled tra...
We present a novel approach to relation extraction that integrates information across documents, performs global inference and requires no labelled text. In particular, we tackle ...
We view match as an operator that takes two graph-like structures (e.g., XML schemas) and produces a mapping between the nodes of these graphs that correspond semantically to each ...
Fausto Giunchiglia, Pavel Shvaiko, Mikalai Yatskev...
Graph-based semi-supervised learning has gained considerable
interests in the past several years thanks to its effectiveness
in combining labeled and unlabeled data through
labe...
Abstract: Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In mul...