GutBrainIE @ CLEF 2027 is the third iteration of a shared task within the BioASQ Lab at CLEF, organized within the EU-supported HEREDITARY project. The challenge promotes the development of Information Extraction (IE) systems to extract and link knowledge from biomedical literature on the gut-brain axis, supporting research on the connections between gut microbiota, mental health, and neurological disorders.

The 2027 edition comprises two main subtasks: Named Entity Recognition and Disambiguation (NERD), identifying entities in PubMed abstracts and linking them to reference biomedical concepts, and Relation Extraction (RE), identifying relations between entity pairs. Alongside end-to-end extraction, this edition introduces standalone RE using ground-truth entity annotations to evaluate relation extraction independently.

Role: Organizer

DE-ESCAlate is a research project developed in collaboration with the University Hospital of Padua, focusing on improving the management of patients with heart failure, a condition associated with high healthcare costs and frequent hospitalizations. It aims to develop tools that facilitate access to clinical information, enable the automatic computation of risk and comorbidity scores, and provide real-time decision support to clinicians, contributing to more informed and efficient patient management.

The project leverages Artificial Intelligence and Natural Language Processing to transform unstructured clinical documents, including emergency department and triage records, into structured, actionable information. We are developing an Information Extraction pipeline capable of automatically identifying relevant clinical entities, linking them to standardized medical concepts, and extracting variables from patient records.

COSTA (Construction cOst Semantic Text Annotations) is a research project focused on extracting structured information from unstructured construction cost descriptions to support cost management and Building Information Modeling (BIM). The project develops a semantically annotated dataset of construction price list entries and investigates automated Information Extraction techniques to identify and classify relevant entities and relationships. COSTA aims to improve the accessibility, interoperability, and usability of construction cost information, facilitating its integration into digital construction workflows.

HEREDITARY aims to significantly transform the way we approach disease detection, prepare treatment response, and explore medical knowledge by building a robust, interoperable, trustworthy and secure framework that integrates multimodal health data (including genetic data) while ensuring compliance with cross-national privacy-preserving policies. The HEREDITARY framework comprises five interconnected layers, from federated data processing and semantic data integration to visual interaction.

By utilizing advanced federated analytics and learning workflows, we aim to identify new risk factors and treatment responses focusing, as exploratory use cases, on neurodegenerative and gut microbiome related disorders. HEREDITARY is harmonizing and linking various sources of clinical, genomic, and environmental data on a large scale. This enables clinicians, researchers, and policymakers to understand these diseases better and develop more effective treatment strategies. HEREDITARY adheres to the citizen science paradigm to ensure that patients and the public have a primary role in guiding scientific and medical research while maintaining full control of their data. Our goal is to change the way we approach healthcare by unlocking insights that were previously impossible to obtain.

Role: Participant

Project No: 101137074
Call: HORIZON-HLTH-2023-TOOL-05
Topic: Tools and technologies for a healthy society
Funding (UNIPD): 1.138.046€
Website: https://hereditary-project.eu/

GutBrainIE @ CLEF 2026 is the second iteration of a shared task within the BioASQ Lab at CLEF 2026. It is a Natural Language Processing (NLP) challenge organized within the EU-supported HEREDITARY project.
The challenge focuses on extracting structured information from biomedical abstracts about the gut microbiota and its connections with neurological disorders and mental health, supporting research on the gut-brain interplay.

The 2026 edition includes four subtasks: Named Entity Recognition (NER), Named Entity Recognition and Disambiguation (NERD), Mention-Level Relation Extraction (M-RE), and Concept-Level Relation Extraction (C-RE). Participants identify and classify biomedical entities, link them to reference concepts, and extract relations between entity mentions and concepts.

Role: Organizer

Website: https://hereditary.dei.unipd.it/challenges/gutbrainie/2026/

GutBrainIE @ CLEF 2025 is the TASK #6 of the BioASQ CLEF Lab 2025, proposing a Natural Language Processing (NLP) challenge on biomedical texts within the context of the EU-supported project HEREDITARY.
Specifically, it is focused on extracting structured information from biomedical abstracts related to the gut microbiota and its connections with Parkinson's disease and mental health, aiming to foster the development of Information Extraction (IE) systems that can support experts in understanding the gut-brain interplay.

The GutBrainIE task is divided into two main subtasks. In the first task, participants are asked to identify and classify specific text spans into predefined categories, while in the second one they have to determine if a particular relationship defined between two categories holds or not.

Role: Organizer

Website: https://hereditary.dei.unipd.it/challenges/gutbrainie/2025/

Source Code: GitHub Repository.
Topic: Knowledge Base Construction

This project has been carried out for the course of "Graph Databases" of the Master Degree in Computer Engineering of the Department of Information Engineering, University of Padua.

A web app designed to help companies and professionals to manage their business activities efficiently while being compliant with Italian regulations.

Source Code: GitHub Repository.
Topic: Web App Development.

This project has been carried out for the course of "Web Applications" of the Master Degree in Computer Engineering of the Department of Information Engineering, University of Padua.

An information retrieval system that can effectively handle changes over time, specifically focusing on the temporal evolution of Web documents.
This system has been developed to participate in the LongEval Lab at CLEF 2023, Task 1: LongEval-Retrieval.

Source Code: GitHub Repository.
Paper: Click Here.
Topic: Information Retrieval.

This project has been carried out for the course of "Search Engines" of the Master Degree in Computer Engineering of the Department of Information Engineering, University of Padua.