Giuseppe Rizzo

TL;DR: This work describes a new Twitter entity disambiguation dataset, and conducts an empirical analysis of named entity recognition and disambigsuation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.

TL;DR: GERBIL aims to become a focal point for the state of the art, driving the research agenda of the community by presenting comparable objective evaluation results.

TL;DR: This work proposes entity2rec, a novel approach to learning user-item relatedness from knowledge graphs for top-N item recommendation that outperforms a number of state-of-the-art recommender systems and assesses the importance of property-specific relatedness scores on the overall ranking quality.

TL;DR: NERD is proposed, a framework which unifies 10 popular named entity extractors available on the web, and the NERD ontology which provides a rich set of axioms aligning the taxonomies of these tools.

TL;DR: A thorough evaluation of five popular Linked Data entity extractors which expose APIs: AlchemyAPI, DBPedia Spotlight, Extractiv, OpenCalais and Zemanta is proposed and an evaluation framework developed and the results of a controlled evaluation performed by human beings are presented.