Gianluca Cima

@uniroma1.it

Assistant Professor, Department of Computer, Control and Management Engineering
Sapienza University of Rome

Gianluca Cima


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https://researchid.co/gianlucacima

EDUCATION

PhD in Engineering in Computer Science

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence
59

Scopus Publications

409

Scholar Citations

12

Scholar h-index

17

Scholar i10-index

Scopus Publications

  • Foundations of Formal Reasoning over Knowledge Bases Combining Symbolic and Sub-Symbolic Knowledge
    Gianluca Cima, Marco Console, Laura Papi
    Proceedings of the Aaai Conference on Artificial Intelligence, 2026
    More and more organizations are relying on Machine Learning (ML) models to support internal decision-making processes. To better support such processes, it would be highly beneficial to contextualize the inductively acquired knowledge encoded in these models and enable formal reasoning over it. Despite significant progress in Neural-Symbolic AI, this specific challenge remains largely under-explored. We propose a framework that allows to integrate the knowledge induced by ML classifiers with the knowledge specified by logic-based formalisms. The framework is based on the novel notion of Hybrid Knowledge Base (HKB), consisting of two components: an ontology and a set of ML binary classifiers. As usual, the ontology provides an intensional representation of the modeled domain through logic-based axioms, while the binary classifiers implicitly encode the extensional knowledge. Specifically, a HKB associates to each concept and role mentioned in the ontology a classifier based on a set of features deemed to be relevant for the application domain, thereby virtually populating the concepts and roles with the instances and pairs of instances from the feature space. Besides the definition of the new framework, as a more technical contribution we show how to reason in this framework by studying query answering over HKBs. In particular, we investigate the computational complexity of query answering in a rich language over HKBs in which the ontology is specified in (the Description Logic counterpart of) RDFS, while the binary classifiers are represented by Multi-Layer Perceptrons.
  • Expressive Recursive Answers for Ontological Knowledge Bases
    Luca Andolfi, Gianluca Cima, Marco Console, Maurizio Lenzerini
    Proceedings of the Aaai Conference on Artificial Intelligence, 2026
    A fundamental use of knowledge bases (KBs) is query answering, i.e., retrieving the information entailed by the KB in response to a user query. When both the KB and the query are specified as logical formulae, the standard form of answer provided to users is the set of all certain answers (CAs): tuples of constants that satisfy the formula defining the query in every model of the logical theory defining the KB. Despite their wide adoption, CAs are known to be just a lossy representation of the information that a KB and a query provide. While several alternative answer languages have been proposed in the literature, no general consensus has emerged on the most suitable approach to query answering over ontological KBs, as each language comes with its own limitations. To address some of these issues, we introduce Regularly Recurrent Answers (RRAs), a novel answer language for queries over ontological KBs based on regular expressions. RRAs support the representation of infinite sets of tuples of constants via a simple (and arguably well understood) generation mechanism. We show that RRAs can capture a fundamental fragment of the certain information entailed by union of conjunctive queries and DL-Lite KBs, making them a strong candidate for informative query answering settings. Our contribution includes the formal definition of RRAs, a proof of their informativeness, and a study of the computational complexity of query answering problem using RRAs.
  • Answering MetaQueries over RDFS Ontologies Under the SPARQL Metamodeling Semantics Entailment Regime
    Diego Calvanese, Gianluca Cima, Julien Corman, Roberto Maria Delfino, Maurizio Lenzerini, Lorenzo Marconi, Antonella Poggi, Ognjen Savkovic
    Lecture Notes in Computer Science, 2026
  • Ontology-Based Schema-Level Data Quality: The Case of Consistency
    Gianluca Cima, Marco Console, Maurizio Lenzerini
    Journal of Data and Information Quality, 2025
    The quality of metadata plays a crucial role in many data FAIRification processes. So much so, in fact, that all the four main principles of data FAIRification prescribe the use of high-quality metadata. One of the main data management paradigms where metadata is a first-class citizen is Ontology-Based Data Management (OBDM). The goal of OBDM is to provide users with a reconciled view of a set of heterogeneous data sources by means of a semantic metadata layer comprising an ontology and a mapping. The former is a high-level, declarative representation of the domain of interest written in terms of a logical theory, and the latter is a formal description of the relation between the symbols in the ontology and the data at the sources. In this article, we introduce a novel data quality framework based on OBDM and specifically tailored for metadata analysis. The target of this framework is one of the most common forms of metadata currently in circulation, i.e., the integrity constraints defined by a database schema. Specifically, we will focus on the data quality dimension known as Consistency, i.e., the property of data that is free of contradictions and incoherence. In this context, our techniques provide a set of tools to compare the integrity constraints defined by a database schema against the knowledge encoded in an ontology and check whether these constraints are strict enough (i.e., protect) and are not too strict (i.e., are faithful to) for such knowledge. The contribution of the article is the presentation of the framework and the study of the related computational problems. We will present a detailed computational complexity analysis of such problems and show that they are decidable for classes of OBDM specifications and integrity constraints that are very popular in practice.
  • Enhancing cooperativity in controlled query evaluation over ontologies
    Piero Bonatti, Gianluca Cima, Domenico Lembo, Francesco Magliocca, Lorenzo Marconi, Riccardo Rosati, Luigi Sauro, Domenico Fabio Savo
    Artificial Intelligence, 2025
    Controlled Query Evaluation (CQE) is a methodology designed to maintain confidentiality by either rejecting specific queries or adjusting responses to safeguard sensitive information. In this investigation, our focus centers on CQE within Description Logic ontologies, aiming to ensure that queries are answered truthfully as long as possible before resorting to deceptive responses, a cooperativity property which is called the “longest honeymoon”. Our work introduces new semantics for CQE, denoted as MC-CQE, which enjoys the longest honeymoon property and outperforms previous methodologies in terms of cooperativity. We study the complexity of query answering in this new framework for ontologies expressed in the Description Logic DL-Lite_R. Specifically, we establish data complexity results under different maximally cooperative semantics and for different classes of queries. Our results identify both tractable and intractable cases. In particular, we show that the evaluation of Boolean unions of conjunctive queries is the same under all the above semantics and its data complexity is in AC^0. This result makes query answering amenable to SQL query rewriting. However, this favorable property does not extend to open queries, even with a restricted query language limited to conjunctions of atoms. While, in general, answering open queries in the MC-CQE framework is intractable, we identify a sub-family of semantics under which answering full conjunctive queries is tractable.
  • Recent Advances in Logic-Based Entity Resolution
    Meghyn Bienvenu, Gianluca Cima, Víctor Gutiérrez-Basulto, Yazmín Ibáñez-García, Zhiliang Xiang
    SIGMOD Record, 2025
    Entity resolution (ER) is a central task in data quality, which is concerned with identifying pairs of distinct constants or tuples that refer to the same real-world entity. Declarative approaches, based upon logical rules and constraints, are a natural choice for tackling complex, collective ER tasks involving the joint resolution of multiple entity types across multiple tables. This paper provides an overview of recent advances in logicbased entity resolution, with a particular focus on the Lace framework, first introduced at PODS'22 and subsequently extended with additional features (IJCAI'23, KR'23) and equipped with an answer set programmingbased implementation (KR'24, KR'25).
  • Answering Conjunctive Queries with Safe Negation and Inequalities over RDFS Knowledge Bases
    Gianluca Cima, Marco Console, Roberto Maria Delfino, Maurizio Lenzerini, Antonella Poggi
    Proceedings of the Aaai Conference on Artificial Intelligence, 2025
    Expressing negative conditions is a crucial feature of query languages for knowledge bases (KBs). Answering such queries over ontological KBs, however, is a very challenging task that becomes undecidable even for lightweight Description Logic (DL) ontologies. Such negative results hold even for Conjunctive Queries (CQs) equipped with basic forms of negative conditions such as the so-called safe negation or inequality atoms. One ontology language that is seemingly unaffected by these results is (the DL counterpart of) RDFS even if equipped with disjointness axioms. Answering CQs with inequalities over such ontologies is known to be Pi^p_2-complete, if the number of inequality atoms is unbounded, and NP-complete if we limit this number to one. Notably, these results leave open the cases of CQs with a fixed number greater than two of inequality atoms. Additionally, such a thorough analysis is missing for CQs with safe negation. In this paper, we embark in a refined analysis of the combined complexity of answering CQs with inequality atoms and safe negation over RDFS ontologies augmented with disjointness axioms. Firstly, we provide a unified Pi^p_2 query answering algorithm for the general problem. Secondly, we confirm the generally held conjecture according to which answering CQs with two inequality atoms over such ontologies is already Pi^p_2-hard. This result closes an important gap in the current literature and has an impact on the widely influential problem of query containment. Lastly, for CQs with safe negation, we prove a behavior similar to that of CQs with inequality atoms. Specifically, we show that answering CQs with at most one negated atom can be done in NP, while allowing at most two negated atoms is sufficient to obtain Pi^p_2-hardness.
  • Answering Expressive Conjunctive Queries over RDFS Knowledge Bases (Extended Abstract)
    Ceur Workshop Proceedings, 2025
  • Advances in Logic-Based Entity Resolution: Enhancing ASPEN with Local Merges and Optimality Criteria
    Zhiliang Xiang, Meghyn Bienvenu, Gianluca Cima, Víctor Gutiérrez-Basulto, Yazmín Ibáñez-García
    Proceedings of the International Conference on Knowledge Representation and Reasoning, 2025
    In this paper, we present ASPEN+, which extends an existing ASP-based system, ASPEN,for collective entity resolution with two important functionalities: support for local merges and new optimality criteria for preferred solutions. Indeed, ASPEN only supports so-called global merges of entity-referring constants (e.g. author ids), in which all occurrences of matched constants are treated as equivalent and merged accordingly. However, it has been argued that when resolving data values, local merges are often more appropriate, as e.g. some instances of ‘J. Lee’ may refer to ‘Joy Lee’, while others should be matched with ‘Jake Lee’. In addition to allowing such local merges, ASPEN+ offers new optimality criteria for selecting solutions, such as minimizing rule violations or maximising the number of rules supporting a merge. Our main contributions are thus (1) the formalisation and computational analysis of various notions of optimal solution, and (2) an extensive experimental evaluation on real-world datasets, demonstrating the effect of local merges and the new optimality criteria on both accuracy and runtime.
  • Indistinguishability in controlled query evaluation over prioritized description logic ontologies
    Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo
    Journal of Web Semantics, 2025
    In this paper we study Controlled Query Evaluation (CQE) , a declarative approach to privacy-preserving query answering over databases, knowledge bases, and ontologies. CQE is based on the notion of censor , which defines the answers to each query posed to the data/knowledge base. We investigate both semantic and computational properties of CQE in the context of OWL ontologies, and specifically in the description logic DL-Lite R , which underpins the OWL 2 QL profile. In our analysis, we focus on semantics of CQE based on censors (called optimal GA censors ) that enjoy the so-called indistinguishability property, analyzing the trade-off between maximizing the amount of data disclosed by query answers and minimizing the computational cost of privacy-preserving query answering. We first study the data complexity of skeptical entailment of unions of conjunctive queries under all the optimal GA censors, showing that the computational cost of query answering in this setting is intractable. To overcome this computational issue, we then define a different semantics for CQE centered around the notion of intersection of all the optimal GA censors. We show that query answering over OWL 2 QL ontologies under the new intersection-based semantics for CQE enjoys tractability and is first-order rewritable , i.e. amenable to be implemented through SQL query rewriting techniques and the use of standard relational database systems; on the other hand, this approach shows limitations in terms of amount of data disclosed. To improve this aspect, we add preferences between ontology predicates to the CQE framework, and identify a semantics under which query answering over OWL 2 QL ontologies maintains the same computational properties of the intersection-based approach without preferences.
  • Controlled Query Evaluation in DL-Lite through Epistemic Protection Policies (Extended Abstract)
    Ceur Workshop Proceedings, 2025
  • Assessing the Exposure to Public Knowledge in Policy-Protected Description Logic Ontologies
    Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo
    Ijcai International Joint Conference on Artificial Intelligence, 2025
  • Controlled Query Evaluation in Description Logic Ontologies
    Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2025
  • Data Augmentation for Data-Centric AI Through the Lens of Semantic Technologies: A Position Paper
    Ceur Workshop Proceedings, 2025
  • Controlled query evaluation in description logics through consistent query answering
    Gianluca Cima, Domenico Lembo, Riccardo Rosati, Domenico Fabio Savo
    Artificial Intelligence, 2024
  • A Gentle Introduction to Controlled Query Evaluation in DL-Lite Ontologies
    Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo
    SN Computer Science, 2024
  • What Does a Query Answer Tell You? Informativeness of Query Answers for Knowledge Bases
    Luca Andolfi, Gianluca Cima, Marco Console, Maurizio Lenzerini
    Proceedings of the Aaai Conference on Artificial Intelligence, 2024
  • Separability and Its Approximations in Ontology-based Data Management
    Gianluca Cima, Federico Croce, Maurizio Lenzerini
    Semantic Web, 2024
  • Informativeness of Query Answers for Knowledge Bases (Extended Abstract)
    Ceur Workshop Proceedings, 2024
  • Semantic Explanations of Classifiers through the Ontology-Based Data Management Paradigm (Extended Abstract)
    Ceur Workshop Proceedings, 2024
  • ASPEN: ASP-Based System for Collective Entity Resolution
    Proceedings of the International Conference on Knowledge Representation and Reasoning, 2024
  • Enhancing Controlled Query Evaluation through Epistemic Policies
    Ijcai International Joint Conference on Artificial Intelligence, 2024
  • Preface for the First International Workshop on Logical Foundations of Neuro-Symbolic AI (LNSAI 2024)
    Ceur Workshop Proceedings, 2024
  • The notion of Abstraction in Ontology-based Data Management
    Gianluca Cima, Antonella Poggi, Maurizio Lenzerini
    Artificial Intelligence, 2023
  • Epistemic Disjunctive Datalog for Querying Knowledge Bases
    Gianluca Cima, Marco Console, Maurizio Lenzerini, Antonella Poggi
    Proceedings of the 37th Aaai Conference on Artificial Intelligence Aaai 2023, 2023
  • On Combining Collective Entity Resolution and Repairing (Extended Abstract)
    Ceur Workshop Proceedings, 2023
  • REPLACE: A Logical Framework for Combining Collective Entity Resolution and Repairing
    Meghyn Bienvenu, Gianluca Cima, Víctor Gutiérrez-Basulto
    Ijcai International Joint Conference on Artificial Intelligence, 2023
  • Combining Global and Local Merges in Logic-based Entity Resolution
    Proceedings of the International Conference on Knowledge Representation and Reasoning, 2023
  • A review of data abstraction
    Gianluca Cima, Marco Console, Maurizio Lenzerini, Antonella Poggi
    Frontiers in Artificial Intelligence, 2023
  • Dynamic Controlled Query Evaluation over DL-Lite Ontologies
    Ceur Workshop Proceedings, 2023
  • Analysis of Relationship between Training Load and Recovery Status in Adult Soccer Players: a Machine Learning Approach
    M. Mandorino, A.J. Figueiredo, G. Cima, A. Tessitore
    International Journal of Computer Science in Sport, 2022
  • Monotone Abstractions in Ontology-Based Data Management
    Gianluca Cima, Marco Console, Maurizio Lenzerini, Antonella Poggi
    Proceedings of the 36th Aaai Conference on Artificial Intelligence Aaai 2022, 2022
  • LACE: A Logical Approach to Collective Entity Resolution
    Meghyn Bienvenu, Gianluca Cima, Víctor Gutiérrez-Basulto
    Proceedings of the ACM SIGACT SIGMOD SIGART Symposium on Principles of Database Systems, 2022
  • Abstraction in Ontology-based Data Management
    Frontiers in Artificial Intelligence and Applications, 2022
  • Investigating Monotone Abstractions
    Ceur Workshop Proceedings, 2022
  • Predictive analytic techniques to identify hidden relationships between training load, fatigue and muscle strains in young soccer players
    Mauro Mandorino, António J. Figueiredo, Gianluca Cima, Antonio Tessitore
    Sports, 2022
  • Controlled Query Evaluation in OWL 2 QL: A “Longest Honeymoon” Approach
    Piero Bonatti, Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Luigi Sauro, Domenico Fabio Savo
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2022
  • A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players
    M. Mandorino, A.J. Figueiredo, G. Cima, A. Tessitore
    International Journal of Computer Science in Sport, 2021
  • Query Definability and Its Approximations in Ontology-based Data Management
    Gianluca Cima, Federico Croce, Maurizio Lenzerini
    International Conference on Information and Knowledge Management Proceedings, 2021
  • Abstraction in Data Integration
    Gianluca Cima, Marco Console, Maurizio Lenzerini, Antonella Poggi
    Proceedings Symposium on Logic in Computer Science, 2021
  • Privacy preserving query answering in description logics through instance indistinguishability
    Ceur Workshop Proceedings, 2021
  • Controlled Query Evaluation over Prioritized Ontologies with Expressive Data Protection Policies
    Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo
    Lecture Notes in Computer Science, 2021
  • Controlled Query Evaluation over Ontologies through Policies with Numerical Restrictions
    Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo, Daniele Sinibaldi
    Proceedings 2021 IEEE 4th International Conference on Artificial Intelligence and Knowledge Engineering Aike 2021, 2021
  • Non-monotonic ontology-based abstractions of data services
    17th International Conference on Principles of Knowledge Representation and Reasoning Kr 2020, 2020
  • Answering conjunctive queries with inequalities in DL-LiteR
    Aaai 2020 34th Aaai Conference on Artificial Intelligence, 2020
  • Controlled query evaluation in description logics through instance indistinguishability
    Ijcai International Joint Conference on Artificial Intelligence, 2020
  • Controlled query evaluation in description logics through instance indistinguishability
    Ceur Workshop Proceedings, 2020
  • Ontology-based explanation of classifiers
    Ceur Workshop Proceedings, 2020
  • Controlled Query Evaluation in Ontology-Based Data Access
    Gianluca Cima, Domenico Lembo, Lorenzo Marconi, Riccardo Rosati, Domenico Fabio Savo
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020
  • Reverse engineering of data services
    Ceur Workshop Proceedings, 2019
  • Semantic characterization of data services through ontologies
    Gianluca Cima, Maurizio Lenzerini, Antonella Poggi
    Ijcai International Joint Conference on Artificial Intelligence, 2019
  • Exploiting ontologies for explaining data sources semantics
    Ceur Workshop Proceedings, 2019
  • On queries with inequalities in DL-LiteR≠
    Ceur Workshop Proceedings, 2019
  • Bag Semantics of DL-Lite with Functionality Axioms
    Gianluca Cima, Charalampos Nikolaou, Egor V. Kostylev, Mark Kaminski, Bernardo Cuenca Grau, Ian Horrocks
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
  • Bagging the DL-lite family further
    Ceur Workshop Proceedings, 2019
  • Semantic technology for open data publishing
    Gianluca Cima, Maurizio Lenzerini, Antonella Poggi
    ACM International Conference Proceeding Series, 2017
  • On the SPARQL Metamodeling Semantics Entailment Regime for OWL 2 QL ontologies
    Gianluca Cima, Giuseppe De Giacomo, Maurizio Lenzerini, Antonella Poggi
    ACM International Conference Proceeding Series, 2017
  • Preliminary results on ontology-based open data publishing
    Ceur Workshop Proceedings, 2017
  • Querying OWL 2 QL ontologies under the SPARQL Metamodeling Semantics Entailment Regime
    Ceur Workshop Proceedings, 2017

RECENT SCHOLAR PUBLICATIONS

  • Foundations of Formal Reasoning over Knowledge Bases Combining Symbolic and Sub-Symbolic Knowledge
    G Cima, M Console, L Papi
    Fortieth AAAI Conference on Artificial Intelligence (AAAI 2026) 40 (23 … , 2026
    2026
  • Expressive Recursive Answers for Ontological Knowledge Bases
    L Andolfi, G Cima, M Console, M Lenzerini
    Fortieth AAAI Conference on Artificial Intelligence (AAAI 2026) 40 (23 … , 2026
    2026
  • Data Augmentation for Data-Centric AI Through the Lens of Semantic Technologies: A Position Paper
    L Cabibbo, D Bertillo, G Cima, V Crescenzi, M Console, RM Delfino, ...
    CEUR WORKSHOP PROCEEDINGS 4182 , 2026
    2026
  • Ontology-based schema-level data quality: The case of consistency
    G Cima, M Console, M Lenzerini
    ACM Journal of Data and Information Quality 17 (4), 1-25 , 2025
    2025
    Citations: 2
  • Recent advances in logic-based entity resolution
    M Bienvenu, G Cima, V Gutiérrez-Basulto, Y Ibáñez-García, Z Xiang
    SIGMOD Record 54 (3), 7-21 , 2025
    2025
  • Answering MetaQueries over RDFS Ontologies Under the SPARQL Metamodeling Semantics Entailment Regime
    D Calvanese, G Cima, J Corman, RM Delfino, M Lenzerini, L Marconi, ...
    Fourteenth International Joint Conference on Knowledge Graphs (IJCKG 2025 … , 2025
    2025
  • Assessing the exposure to public knowledge in policy-protected description logic ontologies
    G Cima, D Lembo, L Marconi, R Rosati, DF Savo
    Thirty-Fourth International Joint Conference on Artificial Intelligence … , 2025
    2025
  • Advances in logic-based entity resolution: Enhancing ASPen with local merges and optimality criteria
    Z Xiang, M Bienvenu, G Cima, V Gutiérrez-Basulto, Y Ibáñez-García
    Twenty-Second International Conference on Principles of Knowledge … , 2025
    2025
    Citations: 1
  • Enhancing Cooperativity in Controlled Query Evaluation over Ontologies
    P Bonatti, G Cima, D Lembo, F Magliocca, L Marconi, R Rosati, L Sauro, ...
    Artificial Intelligence 348, 104402 , 2025
    2025
  • Answering Queries with Negation and Inequalities over RDFS Knowledge Bases
    G Cima, M Console, RM Delfino, M Lenzerini, A Poggi
    Proceedings of The International Research and Industry Symposium on … , 2025
    2025
  • Answering Conjunctive Queries with Safe Negation and Inequalities over RDFS Knowledge Bases
    G Cima, M Console, RM Delfino, M Lenzerini, A Poggi
    Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2025) 39 (14 … , 2025
    2025
    Citations: 9
  • Proceedings of the 33nd Symposium on Advanced Database Systems Ischia, Italy, June 16th to 19th, 2025
    I Bartolini, G Cima, D Firmani, D Lembo, D Martinenghi, M Mecella, ...
    CEUR-WS , 2025
    2025
  • Answering Expressive Conjunctive Queries over RDFS Knowledge Bases
    G Cima, M Console, RM Delfino, M Lenzerini, A Poggi
    Thirty-Eighth International Workshop on Description Logics (DL 2025) 4091 , 2025
    2025
  • Indistinguishability in controlled query evaluation over prioritized description logic ontologies
    G Cima, D Lembo, L Marconi, R Rosati, DF Savo
    Journal of Web Semantics 84, 100841 , 2025
    2025
    Citations: 4
  • Separability and its approximations in ontology-based data management
    G Cima, F Croce, M Lenzerini
    Semantic Web 15 (4), 1021-1056 , 2024
    2024
    Citations: 5
  • Controlled query evaluation in description logics through consistent query answering
    G Cima, D Lembo, R Rosati, DF Savo
    Artificial Intelligence 334, 104176 , 2024
    2024
    Citations: 5
  • ASPEN: ASP-based system for collective entity resolution
    Z Xiang, M Bienvenu, G Cima, V Gutiérrez-Basulto, Y Ibáñez-García
    Twenty-First International Conference on Principles of Knowledge … , 2024
    2024
    Citations: 2
  • Controlled query evaluation through epistemic dependencies
    G Cima, D Lembo, L Marconi, R Rosati, DF Savo
    arXiv preprint arXiv:2405.02458 , 2024
    2024
    Citations: 1
  • A gentle introduction to controlled query evaluation in DL-Lite ontologies
    G Cima, D Lembo, L Marconi, R Rosati, DF Savo
    SN Computer Science 5 (4), 335 , 2024
    2024
    Citations: 2
  • What Does a Query Answer Tell You? Informativeness of Query Answers for Knowledge Bases
    L Andolfi, G Cima, M Console, M Lenzerini
    Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI 2024) 38 (9 … , 2024
    2024
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Predictive analytic techniques to identify hidden relationships between training load, fatigue and muscle strains in young soccer players
    M Mandorino, AJ Figueiredo, G Cima, A Tessitore
    Sports 10 (1), 3 , 2022
    2022
    Citations: 37
  • A data mining approach to predict non-contact injuries in young soccer players
    M Mandorino, AJ Figueiredo, G Cima, A Tessitore
    International Journal of Computer Science in Sport 20 (2), 147-163 , 2021
    2021
    Citations: 31
  • Controlled Query Evaluation in Description Logics Through Instance Indistinguishability
    G Cima, D Lembo, R Rosati, DF Savo
    Twenty-Ninth International Joint Conference on Artificial Intelligence … , 2020
    2020
    Citations: 30
  • Controlled Query Evaluation in Ontology-Based Data Access
    G Cima, D Lembo, L Marconi, R Rosati, DF Savo
    Nineteenth International Semantic Web Conference (ISWC 2020) 12506, 128-146 , 2020
    2020
    Citations: 25
  • On the SPARQL metamodeling semantics entailment regime for OWL 2 QL ontologies
    G Cima, G De Giacomo, M Lenzerini, A Poggi
    Seventh International Conference on Web Intelligence, Mining and Semantics … , 2017
    2017
    Citations: 23
  • Semantic Characterization of Data Services through Ontologies
    G Cima, M Lenzerini, A Poggi
    Twenty-Eighth International Joint Conference on Artificial Intelligence … , 2019
    2019
    Citations: 22
  • Preliminary Results on Ontology-based Open Data Publishing
    G Cima
    Thirtieth International Workshop on Description Logics (DL 2017) 1879 , 2017
    2017
    Citations: 21
  • Answering Conjunctive Queries with Inequalities in DL-Lite ℛ
    G Cima, M Lenzerini, A Poggi
    Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020) 34 (03 … , 2020
    2020
    Citations: 15
  • Monotone Abstractions in Ontology-based Data Management
    G Cima, M Console, M Lenzerini, A Poggi
    Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022) 36 (5 … , 2022
    2022
    Citations: 14
  • Abstraction in Data Integration
    G Cima, M Console, M Lenzerini, A Poggi
    Thirty-Sixth Annual ACM/IEEE Symposium on Logic in Computer Science (LICS … , 2021
    2021
    Citations: 14
  • Controlled Query Evaluation over Prioritized Ontologies with Expressive Data Protection Policies
    G Cima, D Lembo, L Marconi, R Rosati, DF Savo
    Twentieth International Semantic Web Conference (ISWC 2021) 12922, 374-391 , 2021
    2021
    Citations: 13
  • Analysis of relationship between training load and recovery status in adult soccer players: a machine learning approach
    M Mandorino, AJ Figueiredo, G Cima, A Tessitore
    International Journal of Computer Science in Sport 21 (2), 1-16 , 2022
    2022
    Citations: 12
  • Query Definability and Its Approximations in Ontology-based Data Management
    G Cima, F Croce, M Lenzerini
    Thirtieth ACM International Conference on Information and Knowledge … , 2021
    2021
    Citations: 12
  • LACE: A Logical Approach to Collective Entity Resolution
    M Bienvenu, G Cima, V Gutiérrez-Basulto
    Forty-First ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database … , 2022
    2022
    Citations: 11
  • Non-Monotonic Ontology-based Abstractions of Data Services
    G Cima, M Lenzerini, A Poggi
    Seventeenth International Conference on Principles of Knowledge … , 2020
    2020
    Citations: 11
  • Abstraction in Ontology-Based Data Management
    G Cima
    IOS Press, FAIA series , 2022
    2022
    Citations: 10
  • Controlled Query Evaluation in OWL 2 QL: A “Longest Honeymoon” Approach
    P Bonatti, G Cima, D Lembo, L Marconi, R Rosati, L Sauro, DF Savo
    Twenty-First International Semantic Web Conference (ISWC 2022) 13489, 428-444 , 2022
    2022
    Citations: 10
  • Answering Conjunctive Queries with Safe Negation and Inequalities over RDFS Knowledge Bases
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