arXiv subjects

Lorenzo Giammei

Publications and source records attributed to Lorenzo Giammei.

4 recordsLinked to original sources

arXiv2026

The Belief-Desire-Intention Ontology for modelling mental reality and agency

The Belief-Desire-Intention (BDI) model is a cornerstone for representing rational agency in artificial intelligence and cognitive sciences. Yet, its integration into structured, semantically interoperable knowledge representations remains limited. This paper presents a formal BDI Ontology, conceived as a modular Ontology Design Pattern (ODP) that captures the cognitive architecture of agents through beliefs, desires, intentions, and their dynamic interrelations. The ontology ensures semantic precision and reusability by aligning with foundational ontologies and best practices in modular design. Two complementary lines of experimentation demonstrate its applicability: (i) coupling the ontology with Large Language Models (LLMs) via Logic Augmented Generation (LAG) to assess the contribution of ontological grounding to inferential coherence and consistency; and (ii) integrating the ontology within the Semas reasoning platform, which implements the Triples-to-Beliefs-to-Triples (T2B2T) paradigm, enabling a bidirectional flow between RDF triples and agent mental states. Together, these experiments illustrate how the BDI Ontology acts as both a conceptual and operational bridge between declarative and procedural intelligence, paving the way for cognitively grounded, explainable, and semantically interoperable multi-agent and neuro-symbolic systems operating within the Web of Data.

cs.AI↗

arXiv2026

Enhancing Gender Equality Assessment through Object-Oriented Bayesian networks: the European Gender Equality Index Case

A novel data-driven framework is introduced to assess gender equality by complementing and empowering a widely used European gender composite indicator, the Gender Equality Index (GEI). The GEI synthetizes the latent construct of gender equality into a single score and is extensively employed for cross-country comparison and monitoring. While effective for communication and benchmarking, this practice is affected by conceptual and methodological limitations, including marginal analysis that leaves interactions and conditional (in)dependencies unmeasured, and a lack of predictive capability. To address these limitations, this paper proposes the use of Object-Oriented Bayesian Networks (OOBNs) to model the GEI. By preserving the hierarchical structure of the index, OOBNs extend Bayesian Networks and enable a multivariate and probabilistic representation of interdependencies among the components of gender equality. This approach advances intersectional gender statistics by shifting the focus from computing a single composite score to modelling the underlying mechanisms that shape gender inequalities. The proposed methodology enhances the assessment and monitoring of gender equality and adds a predictive dimension through scenario-based evaluation, thereby supporting Gender Impact Assessment and policy decision-making. An application to Italian official statistics illustrates the practical relevance of the framework and its applicability to other national contexts and policy needs.

stat.AP↗

arXiv2025

LEAD: LLM-enhanced Engine for Author Disambiguation

Author Name Disambiguation (AND) is a long-standing challenge in bibliometrics and scientometrics, as name ambiguity undermines the accuracy of bibliographic databases and the reliability of research evaluation. This study addresses the problem of cross-source disambiguation by linking academic career records from CercaUniversità, the official registry of Italian academics, with author profiles in Scopus. We introduce LEAD (LLM-enhanced Engine for Author Disambiguation), a novel hybrid framework that combines semantic features extracted through Large Language Models (LLMs) with structural evidence derived from co-authorship and citation networks. Using a gold standard of 606 ambiguous cases, we compare five methods: (i) Label Spreading on co-authorship networks; (ii) Bibliographic Coupling on citation networks; (iii) a standalone LLM-based approach; (iv) an LLM-enriched configuration; and (v) the proposed hybrid pipeline. LEAD achieves the best performance (F1 = 96.7%, accuracy = 95.7%) with lower computational cost than full LLM models. Bibliographic Coupling emerges as the fastest and strongest single-source method. These findings demonstrate that integrating semantic and structural signals within a selective hybrid strategy offers a robust and scalable solution to cross-database author identification. Beyond the Italian case, this work highlights the potential of hybrid LLM-based methods to improve data quality and reliability in scientometric analyses.

cs.DL↗

arXiv2025

Statistical Challenges in Analyzing Migrant Backgrounds Among University Students: a Case Study from Italy

The methodological issues and statistical complexities of analyzing university students with migrant backgrounds is explored, focusing on Italian data from the University of Milano-Bicocca. With the increasing size of migrant populations and the growth of the second and middle generations, the need has risen for deeper knowledge of the various strata of this population, including university students with migrant backgrounds. This presents challenges due to inconsistent recording in university datasets. By leveraging both administrative records and an original targeted survey we propose a methodology to fully identify the study population of students with migrant histories, and to distinguish relevant subpopulations within it such as second-generation born in Italy. Traditional logistic regression and machine learning random forest models are used and compared to predict migrant status. The primary contribution lies in creating an expanded administrative dataset enriched with indicators of students' migrant backgrounds and status. The expanded dataset provides a critical foundation for analyzing the characteristics of students with migration histories across all variables routinely registered in the administrative data set. Additionally, findings highlight the presence of selection bias in the targeted survey data, underscoring the need of further research.

stat.AP↗