activity
20192022
most citedKnowledge Graph Embeddings and Explainable AI

13 citations · 21 across the 3 of their papers we have counts for

collaborators

5 papers

cs.DB20224 cited

SemTUI: a Framework for the Interactive Semantic Enrichment of Tabular Data

Marco Ripamonti, Flavio De Paoli, Matteo Palmonari

The large availability of datasets fosters the use of \acrshort{ml} and \acrshort{ai} technologies to gather insights, study trends, and predict unseen behaviours out of the world…

cs.CL2021

SWEAT: Scoring Polarization of Topics across Different Corpora

Federico Bianchi, Marco Marelli, Paolo Nicoli +1

Understanding differences of viewpoints across corpora is a fundamental task for computational social sciences. In this paper, we propose the Sliced Word Embedding Association Test…

cs.AI202013 cited

Knowledge Graph Embeddings and Explainable AI

Federico Bianchi, Gaetano Rossiello, Luca Costabello +2

Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we intr…

cs.AI20204 cited

Compass-aligned Distributional Embeddings for Studying Semantic Differences across Corpora

Federico Bianchi, Valerio Di Carlo, Paolo Nicoli +1

Word2vec is one of the most used algorithms to generate word embeddings because of a good mix of efficiency, quality of the generated representations and cognitive grounding. Howev…

cs.CL2019

Training Temporal Word Embeddings with a Compass

Valerio Di Carlo, Federico Bianchi, Matteo Palmonari

Temporal word embeddings have been proposed to support the analysis of word meaning shifts during time and to study the evolution of languages. Different approaches have been propo…