We present YAGO, a light-weight and extensible ontology with high coverage and quality.
YAGO builds on entities and relations and currently contains more than 1 million entities and …

This article presents YAGO, a large ontology with high coverage and precision. YAGO has
been automatically derived from Wikipedia and WordNet. It comprises entities and relations …

In this paper, we present an overview of generalized expectation criteria (GE), a simple,
robust, scalable method for semi-supervised training using weakly-labeled data. GE fits …

A common representation used in text categorization is the bag of words model (aka.
unigram model). Learning with this particular representation involves typically some …

The tremendous development in information technology led to an explosion of data and
motivated the need for powerful yet efficient strategies for data mining and knowledge …

Ontologies are becoming more and more popular as background knowledge for intelligent
applications. Up to now, there has been a schism between manually assembled, highly …

Much work has focused on automatically constructing conceptual taxonomies or semantic
networks from large text corpora. In this paper, we use a state-of-the-art data-driven …

Statistical topic models provide a general data‐driven framework for automated discovery of
high‐level knowledge from large collections of text documents. Although topic models can …

Traditional supervised approaches realize an inductive learning process: A model is learnt
from labeled examples, in order to predict the labels of unseen examples. On the other …

An ontology is a computer-processable collection of knowledge about the world. This thesis
explains how an ontology can be constructed and expanded automatically. The proposed …