2026
Journal article
Open Access
Belief change based on knowledge measures
Casini Giovanni, Straccia Umberto
Belief Change (BC) is the process of changing beliefs (in our case, in terms of contraction, expansion and revision) taking into account a new piece of knowledge, which possibly may be in contradiction with the current belief. In this work, we present a novel quantitative BC framework based on the principle of minimizing the surprise from an information-theoretic perspective. Central to our approach is the Principle of Minimal Surprise (PMS), which asserts that when confronted with the uncertainty about which is the actual world, an agent should tend to favour the most expected, i.e., least surprising, outcomes. To formalize this, we make use of Knowledge Measures (KMs), which quantify the amount of information contained in a knowledge base. Guided by the PMS, our framework encourages belief change operations that minimize the informational amount deviation from the original belief, i.e., those that introduce the least surprise.Source: JOURNAL OF LOGIC AND COMPUTATION, vol. 36 (issue 2)
DOI: 10.1093/logcom/exag007
Project(s): STARWARS
Metrics:
Casini Giovanni, Straccia Umberto
Belief Change (BC) is the process of changing beliefs (in our case, in terms of contraction, expansion and revision) taking into account a new piece of knowledge, which possibly may be in contradiction with the current belief. In this work, we present a novel quantitative BC framework based on the principle of minimizing the surprise from an information-theoretic perspective. Central to our approach is the Principle of Minimal Surprise (PMS), which asserts that when confronted with the uncertainty about which is the actual world, an agent should tend to favour the most expected, i.e., least surprising, outcomes. To formalize this, we make use of Knowledge Measures (KMs), which quantify the amount of information contained in a knowledge base. Guided by the PMS, our framework encourages belief change operations that minimize the informational amount deviation from the original belief, i.e., those that introduce the least surprise.Source: JOURNAL OF LOGIC AND COMPUTATION, vol. 36 (issue 2)
DOI: 10.1093/logcom/exag007
Project(s): STARWARS
Metrics:
See at:
academic.oup.com 
| CNR IRIS | CNR IRIS 
2026
Conference article
Open Access
Reasoning with conditional interpretations
Casini Giovanni, Straccia Umberto
We illustrate how key forms of reasoning can be captured within the framework of Conditional Interpretations, a semantics for conditional languages that supports the formalization of a wide range of reasoning systems. In particular, we outline how to model fundamental notions of entailment, beginning with relations that satisfy classical closure properties and extending to non-monotonic frameworks such as Rational Closure.DOI: 10.1145/3748522.3779724
Project(s): STormwAteR and WastewAteR networkS heterogeneous data AI-driven management
Metrics:
Casini Giovanni, Straccia Umberto
We illustrate how key forms of reasoning can be captured within the framework of Conditional Interpretations, a semantics for conditional languages that supports the formalization of a wide range of reasoning systems. In particular, we outline how to model fundamental notions of entailment, beginning with relations that satisfy classical closure properties and extending to non-monotonic frameworks such as Rational Closure.DOI: 10.1145/3748522.3779724
Project(s): STormwAteR and WastewAteR networkS heterogeneous data AI-driven management
Metrics:
See at:
dl.acm.org | CNR IRIS | CNR IRIS
2026
Contribution to conference
Open Access
A preliminary analysis of high water events in Venice based on multi-decadal observations and clustering
Cardillo Franco Alberto, Andrigo Angela, De Biasio Francesco, Debole Franca, Favaro Marco, Papa Alvise, Straccia Umberto, Vignudelli Stefano
High water events in Venice are a recurrent phenomenon, as the city is located only slightly above mean sea level and is directly in"uenced by water-level variations within the lagoon. Repeated "ooding has signi!cant economic and social impacts, limits pedestrian and naval tra#c and contributes to the degradation of buildings and cultural heritage. Current forecasting systems primarily estimate water levels and peak values, and these are typically estimated at a limited number of locations. Data-driven approaches, in particular Machine Learning (ML) methods, analyze historical data without relying on prede!ned, human-designed model structures. We present a preliminary analysis based on several clustering approaches, including k-means, DBSCAN, and deep learning–based methods, applied to a multi-decadal atmospheric dataset and to the longest available reconstructed hourly sea-level records for the northern Adriatic Sea, specifically developed for this study.Project(s): Collaborazione scientifica ILC - CPSM - ISTI
Cardillo Franco Alberto, Andrigo Angela, De Biasio Francesco, Debole Franca, Favaro Marco, Papa Alvise, Straccia Umberto, Vignudelli Stefano
High water events in Venice are a recurrent phenomenon, as the city is located only slightly above mean sea level and is directly in"uenced by water-level variations within the lagoon. Repeated "ooding has signi!cant economic and social impacts, limits pedestrian and naval tra#c and contributes to the degradation of buildings and cultural heritage. Current forecasting systems primarily estimate water levels and peak values, and these are typically estimated at a limited number of locations. Data-driven approaches, in particular Machine Learning (ML) methods, analyze historical data without relying on prede!ned, human-designed model structures. We present a preliminary analysis based on several clustering approaches, including k-means, DBSCAN, and deep learning–based methods, applied to a multi-decadal atmospheric dataset and to the longest available reconstructed hourly sea-level records for the northern Adriatic Sea, specifically developed for this study.Project(s): Collaborazione scientifica ILC - CPSM - ISTI
2025
Other
Open Access
MILP-SAT-GNN: yet another neural SAT solver
Cardillo F. A., Khyari H., Straccia U.
We proposes a novel method that enables Graph Neural Networks (GNNs) to solve SAT problems by leveraging a technique developed for applying GNNs to Mixed Integer Linear Programming (MILP). Specifically, k-CNF formulae are mapped into MILP problems, which are then encoded as weighted bipartite graphs and subsequently fed into a GNN for training and testing. From a theoretical perspective: (i) we establish permutation and equivalence invariance results, demonstrating that the method produces outputs that are stable under reordering of clauses and variables; (ii) we identify a theoretical limitation, showing that for a class of formulae called foldable formulae, standard GNNs cannot always distinguish satisfiable from unsatisfiable instances; (iii) we prove a universal approximation theorem, establishing that with Random Node Initialization (RNI), the method can approximate SAT solving to arbitrary precision on finite datasets, that is, the GNN becomes approximately sound and complete on such datasets. Furthermore, we show that for unfoldable formulae, the same approximation guarantee can be achieved without the need for RNI. Finally, we conduct an experimental evaluation of our approach, which show that, despite the simplicity of the neural architecture, the method achieves promising results.DOI: 10.48550/arxiv.2507.01825
Project(s): Future Artificial Intelligence Research, STARWARS
Metrics:
Cardillo F. A., Khyari H., Straccia U.
We proposes a novel method that enables Graph Neural Networks (GNNs) to solve SAT problems by leveraging a technique developed for applying GNNs to Mixed Integer Linear Programming (MILP). Specifically, k-CNF formulae are mapped into MILP problems, which are then encoded as weighted bipartite graphs and subsequently fed into a GNN for training and testing. From a theoretical perspective: (i) we establish permutation and equivalence invariance results, demonstrating that the method produces outputs that are stable under reordering of clauses and variables; (ii) we identify a theoretical limitation, showing that for a class of formulae called foldable formulae, standard GNNs cannot always distinguish satisfiable from unsatisfiable instances; (iii) we prove a universal approximation theorem, establishing that with Random Node Initialization (RNI), the method can approximate SAT solving to arbitrary precision on finite datasets, that is, the GNN becomes approximately sound and complete on such datasets. Furthermore, we show that for unfoldable formulae, the same approximation guarantee can be achieved without the need for RNI. Finally, we conduct an experimental evaluation of our approach, which show that, despite the simplicity of the neural architecture, the method achieves promising results.DOI: 10.48550/arxiv.2507.01825
Project(s): Future Artificial Intelligence Research, STARWARS
Metrics:
See at:
arXiv.org e-Print Archive | arxiv.org | CNR IRIS | doi.org | CNR IRIS
2025
Conference article
Open Access
A note on methods for explainable malware analysis
Homola M., Anthony P., Bečková I., Kľuka J., Mojžiš J., Švec P., Balogh S., Cardillo F. A., Debole F., Straccia U., Kenyeres M., Giannini F., Diligenti M., Gori M., Bisták T., Trizna D., Adams Z.
The inevitable rise of machine learning in malware analysis puts forward the need for human-understandable explanations of the learned results. We point out how the ontological representation of malware data provides a suitable language for the construction of such explanations. We then focus on possible methods that enable producing such explanations and we reflect on our experience with them in the context of the EMBER dataset.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4176. Catania, Italy, 08-12/09/2025
Project(s): XAI
Homola M., Anthony P., Bečková I., Kľuka J., Mojžiš J., Švec P., Balogh S., Cardillo F. A., Debole F., Straccia U., Kenyeres M., Giannini F., Diligenti M., Gori M., Bisták T., Trizna D., Adams Z.
The inevitable rise of machine learning in malware analysis puts forward the need for human-understandable explanations of the learned results. We point out how the ontological representation of malware data provides a suitable language for the construction of such explanations. We then focus on possible methods that enable producing such explanations and we reflect on our experience with them in the context of the EMBER dataset.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4176. Catania, Italy, 08-12/09/2025
Project(s): XAI
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2025
Other
Restricted
InfraScience research activity report 2024
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
2024
Conference article
Open Access
A fuzzy logic-based approach to semantic query answering with missing values
Bobillo F., Bobed C., Mena E., Straccia U.
Semantic Query Answering consists of retrieving individuals from a Knowledge Base, usually an OWL 2 ontology or an RDF knowledge graph, that satisfy a Semantic Query expressed via query terms. This paper proposes a novel approach to improve Semantic Query Answering in cases where individuals have missing values with respect to the query terms occurring in a Semantic Query. In our approach, the retrieved instances satisfy some query terms, but not necessarily all. Our general approach, based on the use of fuzzy aggregation operators, is complemented with concrete strategies to evaluate such Semantic Queries, together with an implemented prototype.DOI: 10.1109/fuzz-ieee60900.2024.10611869
Project(s): TAILOR
Metrics:
Bobillo F., Bobed C., Mena E., Straccia U.
Semantic Query Answering consists of retrieving individuals from a Knowledge Base, usually an OWL 2 ontology or an RDF knowledge graph, that satisfy a Semantic Query expressed via query terms. This paper proposes a novel approach to improve Semantic Query Answering in cases where individuals have missing values with respect to the query terms occurring in a Semantic Query. In our approach, the retrieved instances satisfy some query terms, but not necessarily all. Our general approach, based on the use of fuzzy aggregation operators, is complemented with concrete strategies to evaluate such Semantic Queries, together with an implemented prototype.DOI: 10.1109/fuzz-ieee60900.2024.10611869
Project(s): TAILOR
Metrics:
See at:
IRIS Cnr | IRIS Cnr | IRIS Cnr | doi.org | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
PN-OWL: a two-stage algorithm to learn fuzzy concept inclusions from OWL 2 ontologies
Cardillo F. A., Debole F., Straccia U.
Given a target class T of an OWL 2 ontology, positive (and possibly negative) examples of T, we address the problem of learning, viz. inducing, from the examples, fuzzy class inclusion rules that aim to describe conditions for being an individual classified as an instance of the class T. To do so, we present PN-OWL which is a two-stage learning algorithm consisting of a P-stage and an N-stage. In the P-stage, the algorithm learns fuzzy class inclusion rules (the P-rules). These rules aim to cover as many positive examples as possible, increasing recall, without compromising too much precision. In the N-stage, the algorithm learns fuzzy class inclusion rules (the N-rules), that try to rule out as many false positives, covered by the rules learnt at the P-stage, as possible. Roughly, the P-rules tell why an individual should be classified as an instance of T, while the N-rules tell why it should not. PN-OWL then aggregates the P-rules and the N-rules by combining them via an aggregation function to allow for a final decision on whether an individual is an instance of T or not. We also illustrate the effectiveness of PN-OWL through extensive experimentation.Source: FUZZY SETS AND SYSTEMS, vol. 490 (issue 109048)
DOI: 10.1016/j.fss.2024.109048
Project(s): TAILOR, Future Artificial Intelligence Research, STARWARS
Metrics:
Cardillo F. A., Debole F., Straccia U.
Given a target class T of an OWL 2 ontology, positive (and possibly negative) examples of T, we address the problem of learning, viz. inducing, from the examples, fuzzy class inclusion rules that aim to describe conditions for being an individual classified as an instance of the class T. To do so, we present PN-OWL which is a two-stage learning algorithm consisting of a P-stage and an N-stage. In the P-stage, the algorithm learns fuzzy class inclusion rules (the P-rules). These rules aim to cover as many positive examples as possible, increasing recall, without compromising too much precision. In the N-stage, the algorithm learns fuzzy class inclusion rules (the N-rules), that try to rule out as many false positives, covered by the rules learnt at the P-stage, as possible. Roughly, the P-rules tell why an individual should be classified as an instance of T, while the N-rules tell why it should not. PN-OWL then aggregates the P-rules and the N-rules by combining them via an aggregation function to allow for a final decision on whether an individual is an instance of T or not. We also illustrate the effectiveness of PN-OWL through extensive experimentation.Source: FUZZY SETS AND SYSTEMS, vol. 490 (issue 109048)
DOI: 10.1016/j.fss.2024.109048
Project(s): TAILOR
, Future Artificial Intelligence Research, STARWARS Metrics:
See at:
arXiv.org e-Print Archive | IRIS Cnr | doi.org | Hal | Hal | Hal | HAL Descartes | HAL Descartes | IRIS Cnr | IRIS Cnr | Fuzzy Sets and Systems | CNR IRIS | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Defeasible RDFS via rational closure
Casini G, Straccia U
In the field of non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as a notable approach. In recent years, RC has gained popularity in the context of Description Logics (DLs), the logic underpinning the standard semantic Web Ontology Language OWL 2, whose main ingredients are classes, the relationship among classes and roles, which are used to describe the properties of classes.In this work, we show instead how to integrate RC within the triple language RDFS (Resource Description Framework Schema), which together with OWL 2 is a major standard semantic web ontology language.To do so, we start from rdf, a minimal, but significant RDFS fragment that covers the essential features of RDFS, and then extend it to rdf_\bot, allowing to state that two entities are incompatible/disjoint with each other. Eventually, we propose defeasible rdf_\bot via a typical RC construction allowing to state default class/property inclusions.Furthermore, to overcome the main weaknesses of RC in our context, i.e., the "drowning problem" (viz. the "inheritance blocking problem"), we further extend our construction by leveraging Defeasible Inheritance Networks (DIN) defining a new non-monotonic inference relation that combines the advantages of both (RC and DIN). To the best of our knowledge this is the first time of such an attempt.In summary, the main features of our approach are: (i) the defeasible rdf_\bot we propose here remains syntactically a triple language by extending it with new predicate symbols with specific semantics; (ii) the logic is defined in such a way that any RDFS reasoner/store may handle the new predicates as ordinary terms if it does not want to take account of the extra non-monotonic capabilities; (iii) the defeasible entailment decision procedure is built on top of the rdf_\bot entailment decision procedure, which in turn is an extension of the one for rdf via some additional inference rules favouring a potential implementation; (iv) the computational complexity of deciding entailment in rdf and rdf_\bot are the same; and (v) defeasible entailment can be decided via a polynomial number of calls to an oracle deciding ground triple entailment in rdf_\bot and, in particular, deciding defeasible entailment can be done in polynomial time.Source: INFORMATION SCIENCES, vol. 643
DOI: 10.1016/j.ins.2022.11.165
DOI: 10.48550/arxiv.2007.07573
Project(s): TAILOR
Metrics:
Casini G, Straccia U
In the field of non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as a notable approach. In recent years, RC has gained popularity in the context of Description Logics (DLs), the logic underpinning the standard semantic Web Ontology Language OWL 2, whose main ingredients are classes, the relationship among classes and roles, which are used to describe the properties of classes.In this work, we show instead how to integrate RC within the triple language RDFS (Resource Description Framework Schema), which together with OWL 2 is a major standard semantic web ontology language.To do so, we start from rdf, a minimal, but significant RDFS fragment that covers the essential features of RDFS, and then extend it to rdf_\bot, allowing to state that two entities are incompatible/disjoint with each other. Eventually, we propose defeasible rdf_\bot via a typical RC construction allowing to state default class/property inclusions.Furthermore, to overcome the main weaknesses of RC in our context, i.e., the "drowning problem" (viz. the "inheritance blocking problem"), we further extend our construction by leveraging Defeasible Inheritance Networks (DIN) defining a new non-monotonic inference relation that combines the advantages of both (RC and DIN). To the best of our knowledge this is the first time of such an attempt.In summary, the main features of our approach are: (i) the defeasible rdf_\bot we propose here remains syntactically a triple language by extending it with new predicate symbols with specific semantics; (ii) the logic is defined in such a way that any RDFS reasoner/store may handle the new predicates as ordinary terms if it does not want to take account of the extra non-monotonic capabilities; (iii) the defeasible entailment decision procedure is built on top of the rdf_\bot entailment decision procedure, which in turn is an extension of the one for rdf via some additional inference rules favouring a potential implementation; (iv) the computational complexity of deciding entailment in rdf and rdf_\bot are the same; and (v) defeasible entailment can be decided via a polynomial number of calls to an oracle deciding ground triple entailment in rdf_\bot and, in particular, deciding defeasible entailment can be done in polynomial time.Source: INFORMATION SCIENCES, vol. 643
DOI: 10.1016/j.ins.2022.11.165
DOI: 10.48550/arxiv.2007.07573
Project(s): TAILOR
Metrics:
See at:
arXiv.org e-Print Archive | CNR IRIS | www.sciencedirect.com | Information Sciences | doi.org | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
Revising typical beliefs: one revision to rule them all
Heyninck J, Casini G, Meyer T, Straccia U
Propositional Typicality Logic (PTL) extends propositional logic with a connective $\bullet$ expressing the most typical (alias normal or conventional) situations in which a given sentence holds. As such, it generalises e.g. preferential logics that formalise reasoning with conditionals such as "birds typically fly". In this paper we study the revision of sets of PTL sentences. We first show why it is necessary to extend the PTL language with a possibility operator and then define the revision of PTL sentences syntactically and characterise it semantically. We show that this allows us to represent a wide variety of existing revision methods, such as propositional revision and revision of epistemic states. Furthermore, we provide several examples showing why our approach is innovative. In more detail, we study the revision of a set of conditionals under preferential closure and the addition and contraction of possible worlds from an epistemic state.DOI: 10.24963/kr.2023/35
Project(s): TAILOR
Metrics:
Heyninck J, Casini G, Meyer T, Straccia U
Propositional Typicality Logic (PTL) extends propositional logic with a connective $\bullet$ expressing the most typical (alias normal or conventional) situations in which a given sentence holds. As such, it generalises e.g. preferential logics that formalise reasoning with conditionals such as "birds typically fly". In this paper we study the revision of sets of PTL sentences. We first show why it is necessary to extend the PTL language with a possibility operator and then define the revision of PTL sentences syntactically and characterise it semantically. We show that this allows us to represent a wide variety of existing revision methods, such as propositional revision and revision of epistemic states. Furthermore, we provide several examples showing why our approach is innovative. In more detail, we study the revision of a set of conditionals under preferential closure and the addition and contraction of possible worlds from an epistemic state.DOI: 10.24963/kr.2023/35
Project(s): TAILOR
Metrics:
See at:
CNR IRIS | ISTI Repository | proceedings.kr.org | CNR IRIS
2023
Other
Open Access
InfraScience research activity report 2023
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim A. S. T., La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Pagano P., Panichi G., Paratore M. T., Pavone G., Piccioli T., Sinibaldi F., Straccia U., Vannini G. L.
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2023 to highlight the major results. In particular, the InfraScience group engaged in research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2023 InfraScience members contributed to the publishing of several papers, to the research and development activities of several research projects (primarily funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2023/002
DOI: https://doi.org/10.32079/isti-ar-2023/002
Project(s): Blue Cloud, EOSC Future , TAILOR
Metrics:
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim A. S. T., La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Pagano P., Panichi G., Paratore M. T., Pavone G., Piccioli T., Sinibaldi F., Straccia U., Vannini G. L.
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2023 to highlight the major results. In particular, the InfraScience group engaged in research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2023 InfraScience members contributed to the publishing of several papers, to the research and development activities of several research projects (primarily funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2023/002
DOI: https://doi.org/10.32079/isti-ar-2023/002
Project(s): Blue Cloud
, EOSC Future , TAILOR Metrics:
2022
Conference article
Open Access
A rational entailment for expressive description logics via description logic programs
Casini G, Straccia U
Lehmann and Magidor's rational closure is acknowledged as a land-mark in the field of non-monotonic logics and it has also been re-formulated in the context ofDescription Logics (DLs). We show here how to model a rational form of entailment for expressive DLs, such as SROIQ, providing a novel reasoning procedure that compiles a non-monotone DL knowledge base into a description logic program(dl-program).Source: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE (PRINT), pp. 177-191. Durban, South Africa, 6-10/12/2021
DOI: 10.1007/978-3-030-95070-5_12
Metrics:
Casini G, Straccia U
Lehmann and Magidor's rational closure is acknowledged as a land-mark in the field of non-monotonic logics and it has also been re-formulated in the context ofDescription Logics (DLs). We show here how to model a rational form of entailment for expressive DLs, such as SROIQ, providing a novel reasoning procedure that compiles a non-monotone DL knowledge base into a description logic program(dl-program).Source: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE (PRINT), pp. 177-191. Durban, South Africa, 6-10/12/2021
DOI: 10.1007/978-3-030-95070-5_12
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2022
Other
Open Access
A general framework for modelling conditional reasoning - Preliminary report
Casini G, Straccia U
We introduce and investigate here a formalisation for conditionals that allows the definition of a broad class of reasoning systems. This framework covers the most popular kinds of conditional reasoning in logic-based KR: the semantics we propose is appropriate for a structural analysis of those conditionals that do not satisfy closure properties associated to classical logics.Project(s): TAILOR
Casini G, Straccia U
We introduce and investigate here a formalisation for conditionals that allows the definition of a broad class of reasoning systems. This framework covers the most popular kinds of conditional reasoning in logic-based KR: the semantics we propose is appropriate for a structural analysis of those conditionals that do not satisfy closure properties associated to classical logics.Project(s): TAILOR
See at:
arxiv.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
A minimal deductive system for RDFS with negative statements
Straccia U, Casini G
The triple language RDFS is designed to represent and reason with \emph{positive} statements only (e.g."antipyretics are drugs"). In this paper we show how to extend RDFS to express and reason with various forms of negative statements under the Open World Assumption (OWA). To do so, we start from rdf, a minimal, but significant RDFS fragment that covers all essential features of RDFS, and then extend it to ?rdfbotneg, allowing express also statements such as "radio therapies are non drug treatments", "Ebola has no treatment", or "opioids and antipyretics are disjoint classes". The main and, to the best of our knowledge, unique features of our proposal are: (i) rdfbotneg remains syntactically a triple language by extending rdf with new symbols with specific semantics and there is no need to revert to the reification method to represent negative triples; (ii) the logic is defined in such a way that any RDFS reasoner/store may handle the new predicates as ordinary terms if it does not want to take account of the extra capabilities; (iii) despite negated statements, every rdfbotneg knowledge base is satisfiable; (iv) the rdfbotneg entailment decision procedure is obtained from rdf via additional inference rules favouring a potential implementation; and (v) deciding entailment in rdfbotneg ranges from P to NP.Project(s): TAILOR
Straccia U, Casini G
The triple language RDFS is designed to represent and reason with \emph{positive} statements only (e.g."antipyretics are drugs"). In this paper we show how to extend RDFS to express and reason with various forms of negative statements under the Open World Assumption (OWA). To do so, we start from rdf, a minimal, but significant RDFS fragment that covers all essential features of RDFS, and then extend it to ?rdfbotneg, allowing express also statements such as "radio therapies are non drug treatments", "Ebola has no treatment", or "opioids and antipyretics are disjoint classes". The main and, to the best of our knowledge, unique features of our proposal are: (i) rdfbotneg remains syntactically a triple language by extending rdf with new symbols with specific semantics and there is no need to revert to the reification method to represent negative triples; (ii) the logic is defined in such a way that any RDFS reasoner/store may handle the new predicates as ordinary terms if it does not want to take account of the extra capabilities; (iii) despite negated statements, every rdfbotneg knowledge base is satisfiable; (iv) the rdfbotneg entailment decision procedure is obtained from rdf via additional inference rules favouring a potential implementation; and (v) deciding entailment in rdfbotneg ranges from P to NP.Project(s): TAILOR
See at:
arxiv.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Conference article
Open Access
A general framework for modelling conditional reasoning - Preliminary report
Casini G, Straccia U
We introduce and investigate here a formalisation for conditionals that allows the definition of a broad class of reasoning systems. This framework covers the most popular kinds of conditional reasoning in logic-based KR: the semantics we propose is appropriate for a structural analysis of those conditionals that do not satisfy closure properties associated to classical logics.DOI: 10.24963/kr.2022/12
DOI: 10.48550/arxiv.2202.07596
Project(s): TAILOR
Metrics:
Casini G, Straccia U
We introduce and investigate here a formalisation for conditionals that allows the definition of a broad class of reasoning systems. This framework covers the most popular kinds of conditional reasoning in logic-based KR: the semantics we propose is appropriate for a structural analysis of those conditionals that do not satisfy closure properties associated to classical logics.DOI: 10.24963/kr.2022/12
DOI: 10.48550/arxiv.2202.07596
Project(s): TAILOR
Metrics:
See at:
arXiv.org e-Print Archive | CNR IRIS | ISTI Repository | proceedings.kr.org | doi.org | doi.org | CNR IRIS
2022
Conference article
Open Access
A minimal deductive system for RDFS with negative statements
Straccia U., Casini G.
The triple language RDFS is designed to represent and reason with \emph{positive} statements only (e.g."antipyretics are drugs"). In this paper we show how to extend RDFS to express and reason with various forms of negative statements under the Open World Assumption (OWA). To do so, we start from rdf, a minimal, but significant RDFS fragment that covers all essential features of RDFS, and then extend it to ?rdfbotneg, allowing express also statements such as "radio therapies are non drug treatments", "Ebola has no treatment", or "opioids and antipyretics are disjoint classes". The main and, to the best of our knowledge, unique features of our proposal are: (i) rdfbotneg remains syntactically a triple language by extending rdf with new symbols with specific semantics and there is no need to revert to the reification method to represent negative triples; (ii) the logic is defined in such a way that any RDFS reasoner/store may handle the new predicates as ordinary terms if it does not want to take account of the extra capabilities; (iii) despite negated statements, every rdfbotneg knowledge base is satisfiable; (iv) the rdfbotneg entailment decision procedure is obtained from rdf via additional inference rules favouring a potential implementation; and (v) deciding entailment in rdfbotneg ranges from P to NP.Source: PROCEEDINGS-INTERNATIONAL CONFERENCE ON PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, pp. 351-361. Haifa, Israel, 31/07-05/08/2022
DOI: 10.24963/kr.2022/35
DOI: 10.48550/arxiv.2202.13750
Project(s): TAILOR
Metrics:
Straccia U., Casini G.
The triple language RDFS is designed to represent and reason with \emph{positive} statements only (e.g."antipyretics are drugs"). In this paper we show how to extend RDFS to express and reason with various forms of negative statements under the Open World Assumption (OWA). To do so, we start from rdf, a minimal, but significant RDFS fragment that covers all essential features of RDFS, and then extend it to ?rdfbotneg, allowing express also statements such as "radio therapies are non drug treatments", "Ebola has no treatment", or "opioids and antipyretics are disjoint classes". The main and, to the best of our knowledge, unique features of our proposal are: (i) rdfbotneg remains syntactically a triple language by extending rdf with new symbols with specific semantics and there is no need to revert to the reification method to represent negative triples; (ii) the logic is defined in such a way that any RDFS reasoner/store may handle the new predicates as ordinary terms if it does not want to take account of the extra capabilities; (iii) despite negated statements, every rdfbotneg knowledge base is satisfiable; (iv) the rdfbotneg entailment decision procedure is obtained from rdf via additional inference rules favouring a potential implementation; and (v) deciding entailment in rdfbotneg ranges from P to NP.Source: PROCEEDINGS-INTERNATIONAL CONFERENCE ON PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, pp. 351-361. Haifa, Israel, 31/07-05/08/2022
DOI: 10.24963/kr.2022/35
DOI: 10.48550/arxiv.2202.13750
Project(s): TAILOR
Metrics:
See at:
arXiv.org e-Print Archive | CNR IRIS | ISTI Repository | proceedings.kr.org | doi.org | doi.org | CNR IRIS
2022
Conference article
Open Access
Defeasible reasoning in RDFS
Casini G, Straccia U
For non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as one of the main approaches. In this work we present an integration of RC within the triple language RDFS (Resource Description Framework Schema), which together with OWL 2 is a major standard semantic web ontology language. To do so, we start from ?df, an RDFS fragment that covers the essential features of RDFS, and extend it to ?df?, allowing to state that two entities are incompatible/disjoint with each other. Eventually, we propose defeasible ?df? via a typical RC construction allowing to state default class/property inclusions.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3197, pp. 155-158. Haifa, Israel, 07-09/08/2022
Project(s): TAILOR
Casini G, Straccia U
For non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as one of the main approaches. In this work we present an integration of RC within the triple language RDFS (Resource Description Framework Schema), which together with OWL 2 is a major standard semantic web ontology language. To do so, we start from ?df, an RDFS fragment that covers the essential features of RDFS, and extend it to ?df?, allowing to state that two entities are incompatible/disjoint with each other. Eventually, we propose defeasible ?df? via a typical RC construction allowing to state default class/property inclusions.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3197, pp. 155-158. Haifa, Israel, 07-09/08/2022
Project(s): TAILOR
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
InfraScience research activity report 2021
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lazzeri E, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2021 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, eScience, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2021 InfraScience members contributed to the publishing of 25 papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/001
DOI: https://doi.org/10.32079/isti-ar-2022/001
Project(s): ARIADNEplus, Blue Cloud , PerformFISH , EOSC-Pillar , DESIRA , EOSC Future , EOSCsecretariat.eu , EcoScope , RISIS 2 , OpenAIRE-Advance , OpenAIRE Nexus , SoBigData-PlusPlus
Metrics:
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lazzeri E, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2021 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, eScience, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2021 InfraScience members contributed to the publishing of 25 papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/001
DOI: https://doi.org/10.32079/isti-ar-2022/001
Project(s): ARIADNEplus
, Blue Cloud , PerformFISH , EOSC-Pillar , DESIRA , EOSC Future , EOSCsecretariat.eu , EcoScope , RISIS 2 , OpenAIRE-Advance , OpenAIRE Nexus , SoBigData-PlusPlus Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Metadata Only Access
The TAILOR Handbook of Trustworthy AI
Albertoni R, Allard T, Alves G, Bringas Colmenarejo A, Buijsman S, Casares P A M, Colantonio S, Couceiro M, Escobar S, Gonzalezcastañé G, Guidotti R, Heintz F, Hernandez Orallo J, Kuilman S, Makhlouf K, Martinez Plumed F, Monreale A, Pellungrini R, Pratesi F, Ramachandran Pillai R, Rossi A, Rousset Mc, Ruggieri S, Siebert Lc, Skrzypczyski P, Stefanowski J, Straccia U, Òsullivan B, Visentin A, Zgonnikov A, Zhioua S
The main goal of the Handbook of Trustworthy AI is to provide to non experts, especially researchers and students, an overview of the problem related to the developing of ethical and trustworty AI systems. In particular, we want to provide an overview of the main dimensions of trustworthiness, starting with a understandable explaination of the dimension itsleves, and then presenting the characterization of the problems (staring with a brief summary and the explaination of the importance of the dimension, presenting a taxonomy and some guidelines, if they are available and consolidated), summarizing what are the major challenges and solutions in the field, as well as what are the lastest research developments.Project(s): TAILOR
Albertoni R, Allard T, Alves G, Bringas Colmenarejo A, Buijsman S, Casares P A M, Colantonio S, Couceiro M, Escobar S, Gonzalezcastañé G, Guidotti R, Heintz F, Hernandez Orallo J, Kuilman S, Makhlouf K, Martinez Plumed F, Monreale A, Pellungrini R, Pratesi F, Ramachandran Pillai R, Rossi A, Rousset Mc, Ruggieri S, Siebert Lc, Skrzypczyski P, Stefanowski J, Straccia U, Òsullivan B, Visentin A, Zgonnikov A, Zhioua S
The main goal of the Handbook of Trustworthy AI is to provide to non experts, especially researchers and students, an overview of the problem related to the developing of ethical and trustworty AI systems. In particular, we want to provide an overview of the main dimensions of trustworthiness, starting with a understandable explaination of the dimension itsleves, and then presenting the characterization of the problems (staring with a brief summary and the explaination of the importance of the dimension, presenting a taxonomy and some guidelines, if they are available and consolidated), summarizing what are the major challenges and solutions in the field, as well as what are the lastest research developments.Project(s): TAILOR
See at:
CNR IRIS | tailor.isti.cnr.it
2022
Other
Open Access
InfraScience research activity report 2022
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U, Zoppi F
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2022 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2022 InfraScience members contributed to the publishing of several papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/004
DOI: https://doi.org/10.32079/isti-ar-2022/004
Project(s): ARIADNEplus, Blue Cloud , EOSC-Pillar , DESIRA , EOSC Future , RISIS 2 , TAILOR , SoBigData-PlusPlus
Metrics:
Artini M, Assante M, Atzori C, Baglioni M, Bardi A, Bove P, Candela L, Casini G, Castelli D, Cirillo R, Coro G, De Bonis M, Debole F, Dell'Amico A, Frosini L, La Bruzzo S, Lelii L, Manghi P, Mangiacrapa F, Mangione D, Mannocci A, Ottonello E, Pagano P, Panichi G, Pavone G, Piccioli T, Sinibaldi F, Straccia U, Zoppi F
InfraScience is a research group of the National Research Council of Italy - Institute of Information Science and Technologies (CNR - ISTI) based in Pisa, Italy. This report documents the research activity performed by this group in 2022 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e-Science, and Intelligent Systems. The group activity is pursued by closely connecting research and development and by promoting and supporting open science. In fact, the group is leading the development of two large scale infrastructures for Open Science, i.e. D4Science and OpenAIRE. During 2022 InfraScience members contributed to the publishing of several papers, to the research and development activities of 18 research projects (15 funded by EU), to the organization of conferences and training events, to several working groups and task forces.DOI: 10.32079/isti-ar-2022/004
DOI: https://doi.org/10.32079/isti-ar-2022/004
Project(s): ARIADNEplus
, Blue Cloud , EOSC-Pillar , DESIRA , EOSC Future , RISIS 2 , TAILOR , SoBigData-PlusPlus Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS