We present an automatic approach to the construction of BabelNet, a very large, wide-
coverage multilingual semantic network. Key to our approach is the integration of …

We present YAGO2, an extension of the YAGO knowledge base, in which entities, facts, and
events are anchored in both time and space. YAGO2 is built automatically from Wikipedia …

In this paper we present BabelNet–a very large, wide-coverage multilingual semantic
network. The resource is automatically constructed by means of a methodology that …

We automatically create enormous, free and multilingual silver-standard training annotations
for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner …

Most word representation methods assume that each word owns a single semantic vector.
This is usually problematic because lexical ambiguity is ubiquitous, which is also the …

The Web offers a corpus of over 100 million tables [6], but the meaning of each table is rarely
explicit from the table itself. Header rows exist in few cases and even when they do, the …

In 2004 we published in this journal an article describing OntoLearn, one of the first systems
to automatically induce a taxonomy from documents and Web sites. Since then, OntoLearn …

Abstract The Web of Data currently coming into existence through the Linked Open Data
(LOD) effort is a major milestone in realizing the Semantic Web vision. However, the …

This paper presents an overview of automatic methods for building domain knowledge
structures (domain models) from text collections. Applications of domain models have a long …

Recent years have seen a great deal of work that exploits collaborative, semi-structured
content for Artificial Intelligence (AI) and Natural Language Processing (NLP). This special …