Search Results for author: Jacopo de Berardinis

Found 5 papers, 2 papers with code

OntoChat: a Framework for Conversational Ontology Engineering using Language Models

1 code implementation9 Mar 2024 Bohui Zhang, Valentina Anita Carriero, Katrin Schreiberhuber, Stefani Tsaneva, Lucía Sánchez González, Jongmo Kim, Jacopo de Berardinis

Ontology engineering (OE) in large projects poses a number of challenges arising from the heterogeneous backgrounds of the various stakeholders, domain experts, and their complex interactions with ontology designers.

The Music Meta Ontology: a flexible semantic model for the interoperability of music metadata

no code implementations7 Nov 2023 Jacopo de Berardinis, Valentina Anita Carriero, Albert Meroño-Peñuela, Andrea Poltronieri, Valentina Presutti

The semantic description of music metadata is a key requirement for the creation of music datasets that can be aligned, integrated, and accessed for information retrieval and knowledge discovery.

Information Retrieval Retrieval

The Music Annotation Pattern

1 code implementation30 Mar 2023 Jacopo de Berardinis, Albert Meroño-Peñuela, Andrea Poltronieri, Valentina Presutti

The annotation of music content is a complex process to represent due to its inherent multifaceted, subjectivity, and interdisciplinary nature.

SOLIS: Autonomous Solubility Screening using Deep Neural Networks

no code implementations18 Mar 2022 Gabriella Pizzuto, Jacopo de Berardinis, Louis Longley, Hatem Fakhruldeen, Andrew I. Cooper

This can result in researchers spending a significant amount of their time on repetitive tasks, which introduces errors and can prohibit production of statistically relevant data.

Image Segmentation Semantic Segmentation

At Your Service: Coffee Beans Recommendation From a Robot Assistant

no code implementations26 Aug 2020 Jacopo de Berardinis, Gabriella Pizzuto, Francesco Lanza, Antonio Chella, Jorge Meira, Angelo Cangelosi

From this, we propose how this computational model can be deployed on a service robot to reliably predict customers' coffee bean preferences, starting from the user inputting their coffee preferences to the robot recommending the coffee beans that best meet the user's likings.

Recommendation Systems

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