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 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 …

Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for
many web applications. For example, online retailers (eg, Amazon and eBay) use …

We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that
transforms any input word vector space to emphasise the asymmetric relation of lexical …

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 …

We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy
detection and directionality. While previous embeddings have shown limitations on …

The fundamental role of hypernymy in NLP has motivated the development of many
methods for the automatic identification of this relation, most of which rely on word …

Taxonomies, especially the ones in specific domains, are becoming indispensable to a
growing number of applications. State-of-the-art approaches assume there exists a text …

Taxonomic relation identification aims to recognize the 'is-a'relation between two terms.
Previous works on identifying taxonomic relations are mostly based on statistical and …