Most existing methods determine relation types only after all the entities have been
recognized, thus the interaction between relation types and entity mentions is not fully …

We examine the embedding approach to reason new relational facts from a largescale
knowledge graph and a text corpus. We propose a novel method of jointly embedding …

With the advent of the Internet, large amount of digital text is generated everyday in the form
of news articles, research publications, blogs, question answering forums and social media …

Populating a database with unstructured information is a long-standing problem in industry
and research that encompasses problems of extraction, cleaning, and integration. Recent …

We present a framework for constructing a specific type of knowledge graph, a concept map
from textbooks. Using Wikipedia, we derive prerequisite relations among these concepts. A …

The essence of distantly supervised relation extraction is that it is an incomplete multi-label
classification problem with sparse and noisy features. To tackle the sparsity and noise …

Populating a database with information from unstructured sources—also known as
knowledge base construction (KBC)—is a long-standing problem in industry and research …

Abstract Machine learning approaches to relation extraction are typically supervised and
require expensive labeled data. To break the bottleneck of labeled data, a promising …

Distant supervision is a widely applied approach to automatic training of relation extraction
systems and has the advantage that it can generate large amounts of labelled data with …

Abstract Knowledge bases store structured information about entities or concepts of the
world and can be used in various applications, such as information retrieval or question …