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 643 (2023). doi:10.1016/j.ins.2022.11.165
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 643 (2023). doi:10.1016/j.ins.2022.11.165
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 
| Information Sciences 
| doi.org | www.sciencedirect.com | CNR ExploRA
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.Source: KR2023 - International Conference on Principles of Knowledge Representation and Reasoning, pp. 355–364, Rhodes, Greece, 2-8/09/2023
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.Source: KR2023 - International Conference on Principles of Knowledge Representation and Reasoning, pp. 355–364, Rhodes, Greece, 2-8/09/2023
DOI: 10.24963/kr.2023/35
Project(s): TAILOR
Metrics:
See at:
ISTI Repository | proceedings.kr.org | CNR ExploRA
2023
Report
Unknown
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.Source: ISTI Annual Reports, 2023
DOI: 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.Source: ISTI Annual Reports, 2023
DOI: 10.32079/isti-ar-2023/002
Project(s): Blue Cloud
, EOSC Future , TAILOR Metrics:
See at: CNR ExploRA
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: SACAIR 2021 - Second Southern African Conference, 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: SACAIR 2021 - Second Southern African Conference, pp. 177–191, Durban, South Africa, 6-10/12/2021
DOI: 10.1007/978-3-030-95070-5_12
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
2022
Report
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.Source: ISTI Technical Report, ISTI-2022-TR/004, pp.1–21, 2022
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.Source: ISTI Technical Report, ISTI-2022-TR/004, pp.1–21, 2022
Project(s): TAILOR
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2022
Report
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: ISTI Technical Report, ISTI-2022-TR/005, pp.1–24, 2022
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.Source: ISTI Technical Report, ISTI-2022-TR/005, pp.1–24, 2022
Project(s): TAILOR
See at:
arxiv.org | ISTI Repository | CNR ExploRA
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.Source: KR 2022 - 19th International Conference on Principles of Knowledge Representation and Reasoning, pp. 112–121, Haifa, Israel, 31/07-05/08/2022
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.Source: KR 2022 - 19th International Conference on Principles of Knowledge Representation and Reasoning, pp. 112–121, Haifa, Israel, 31/07-05/08/2022
DOI: 10.24963/kr.2022/12
DOI: 10.48550/arxiv.2202.07596
Project(s): TAILOR
Metrics:
See at:
arXiv.org e-Print Archive | ISTI Repository | proceedings.kr.org | doi.org | doi.org | CNR ExploRA
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: KR 2022 - 19th 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: KR 2022 - 19th 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 | ISTI Repository | proceedings.kr.org | doi.org | doi.org | CNR ExploRA
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: NMR 2022 - International Workshop on Non-Monotonic Reasoning 2022, 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: NMR 2022 - International Workshop on Non-Monotonic Reasoning 2022, pp. 155–158, Haifa, Israel, 07-09/08/2022
Project(s): TAILOR
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2022
Report
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.Source: ISTI Annual report, 2022
DOI: 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.Source: ISTI Annual report, 2022
DOI: 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:
ISTI Repository | CNR ExploRA
2022
Other
Unknown
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., Gonzalez-Castañé 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 M. C., Ruggieri S., Siebert L. C., 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., Gonzalez-Castañé 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 M. C., Ruggieri S., Siebert L. C., 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 ExploRA | tailor.isti.cnr.it
2022
Report
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.Source: ISTI Annual reports, 2022
DOI: 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.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/004
Project(s): ARIADNEplus
, Blue Cloud , EOSC-Pillar , DESIRA , EOSC Future , RISIS 2 , TAILOR , SoBigData-PlusPlus Metrics:
See at:
ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
Fuzzy OWL-Boost: learning fuzzy concept inclusions via real-valued boosting
Cardillo F. A., Straccia U.
OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given an OWL ontology and a target class T, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T (and to which degree). To do so, we present FUZZY OWL-BOOST that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation with several ontologies.Source: Fuzzy sets and systems 438 (2021): 164–186. doi:10.1016/j.fss.2021.07.002
DOI: 10.1016/j.fss.2021.07.002
DOI: 10.48550/arxiv.2008.05297
Project(s): TAILOR
Metrics:
Cardillo F. A., Straccia U.
OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given an OWL ontology and a target class T, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T (and to which degree). To do so, we present FUZZY OWL-BOOST that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation with several ontologies.Source: Fuzzy sets and systems 438 (2021): 164–186. doi:10.1016/j.fss.2021.07.002
DOI: 10.1016/j.fss.2021.07.002
DOI: 10.48550/arxiv.2008.05297
Project(s): TAILOR
Metrics:
See at:
arXiv.org e-Print Archive | Fuzzy Sets and Systems | ISTI Repository | Fuzzy Sets and Systems | doi.org | www.sciencedirect.com | ZENODO | CNR ExploRA
2021
Report
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 landmark in the field of non-monotonic logics and it has also been re-formulated in the context of Description 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 nonmonotone DL knowledge base into a description logic program (dl-program).Source: ISTI Technical Report, ISTI-2021-TR/019, 2021
DOI: 10.32079/isti-tr-2021/019
Metrics:
Casini G., Straccia U.
Lehmann and Magidor's rational closure is acknowledged as a landmark in the field of non-monotonic logics and it has also been re-formulated in the context of Description 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 nonmonotone DL knowledge base into a description logic program (dl-program).Source: ISTI Technical Report, ISTI-2021-TR/019, 2021
DOI: 10.32079/isti-tr-2021/019
Metrics:
See at:
ISTI Repository | CNR ExploRA
2021
Report
Open Access
InfraScience Research Activity Report 2020
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., Debole F., Dell'Amico A., Frosini L., La Bruzzo S., Lazzeri E., Lelii L., Manghi P., Mangiacrapa F., Mannocci A., Pagano P., Panichi 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 2020 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e\-Sci\-ence, 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, \ie D4Science and OpenAIRE. During 2020 InfraScience members contributed to the publishing of 30 papers, to the research and development activities of 12 research projects (11 funded by EU), to the organization of conferences and training events, to several working groups and task forces.Source: ISTI Annual Report, ISTI-2021-AR/002, pp.1–20, 2021
DOI: 10.32079/isti-ar-2021/002
Project(s): ARIADNEplus, Blue Cloud , PerformFISH , EOSC-Pillar , DESIRA , EOSCsecretariat.eu , RISIS 2 , TAILOR , I-GENE , MOVING , OpenAIRE-Advance , SoBigData-PlusPlus
Metrics:
Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., Debole F., Dell'Amico A., Frosini L., La Bruzzo S., Lazzeri E., Lelii L., Manghi P., Mangiacrapa F., Mannocci A., Pagano P., Panichi 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 2020 to highlight the major results. In particular, the InfraScience group confronted with research challenges characterising Data Infrastructures, e\-Sci\-ence, 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, \ie D4Science and OpenAIRE. During 2020 InfraScience members contributed to the publishing of 30 papers, to the research and development activities of 12 research projects (11 funded by EU), to the organization of conferences and training events, to several working groups and task forces.Source: ISTI Annual Report, ISTI-2021-AR/002, pp.1–20, 2021
DOI: 10.32079/isti-ar-2021/002
Project(s): ARIADNEplus
, Blue Cloud , PerformFISH , EOSC-Pillar , DESIRA , EOSCsecretariat.eu , RISIS 2 , TAILOR , I-GENE , MOVING , OpenAIRE-Advance , SoBigData-PlusPlus Metrics:
See at:
ISTI Repository | CNR ExploRA
2020
Journal article
Open Access
Fudge: Fuzzy ontology building with consensuated fuzzy datatypes
Huitzil I., Bobillo F., Gomez-Romero J., Straccia U.
An important problem in Fuzzy OWL 2 ontology building is the definition of fuzzy membership functions for real-valued fuzzy sets (so-called fuzzy datatypes in Fuzzy OWL 2 terminology). In this paper, we present a tool, called Fudge, whose aim is to support the consensual creation of fuzzy datatypes by aggregating the specifications given by a group of experts. Fudge is freeware and currently supports several linguistic aggregation strategies, including the convex combination, linguistic OWA, weighted mean and fuzzy OWA, and easily allows to build others in. We also propose and have implemented two novel linguistic aggregation operators, based on a left recursive form of the convex combination and of the linguistic OWA.Source: Fuzzy sets and systems 401 (2020): 91–112. doi:10.1016/j.fss.2020.04.001
DOI: 10.1016/j.fss.2020.04.001
Metrics:
Huitzil I., Bobillo F., Gomez-Romero J., Straccia U.
An important problem in Fuzzy OWL 2 ontology building is the definition of fuzzy membership functions for real-valued fuzzy sets (so-called fuzzy datatypes in Fuzzy OWL 2 terminology). In this paper, we present a tool, called Fudge, whose aim is to support the consensual creation of fuzzy datatypes by aggregating the specifications given by a group of experts. Fudge is freeware and currently supports several linguistic aggregation strategies, including the convex combination, linguistic OWA, weighted mean and fuzzy OWA, and easily allows to build others in. We also propose and have implemented two novel linguistic aggregation operators, based on a left recursive form of the convex combination and of the linguistic OWA.Source: Fuzzy sets and systems 401 (2020): 91–112. doi:10.1016/j.fss.2020.04.001
DOI: 10.1016/j.fss.2020.04.001
Metrics:
See at:
ISTI Repository | www.sciencedirect.com | Recolector de Ciencia Abierta, RECOLECTA | Fuzzy Sets and Systems | Fuzzy Sets and Systems | CNR ExploRA
2020
Report
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 prominent approach. In recent years, RC has gained even more popularity in the context of Description Logics (DLs), the logic underpinning the semantic web standard ontology language OWL 2, whose main ingredients are classes and roles. In this work, we show how to integrate RC within the triple language RDFS, which together with OWL2 are the two major standard semantic web ontology languages. To do so, we start from ?df, which is the logic behind RDFS, and then extend it to ?df?, allowing to state that two entities are incompatible. Eventually, we propose defeasible ?df? via a typical RC construction. The main features of our approach are: (i) unlike most other approaches that add an extra non-monotone rule layer on top of monotone RDFS, defeasible ?df? remains syntactically a triple language and is a simple extension of ?df? by introducing some new predicate symbols with specific semantics. In particular, any RDFS reasoner/store may handle them as ordinary terms if it does not want to take account for the extra semantics of the new predicate symbols; (ii) the defeasible ?df? entailment decision procedure is build on top of the ?df? entailment decision procedure, which in turn is an extension of the one for ?df via some additional inference rules favouring an potential implementation; and (iii) defeasible ?df? entailment can be decided in polynomial time.Source: ISTI Technical Reports 008/2020, 2020
DOI: 10.32079/isti-tr-2020/008
Metrics:
Casini G., Straccia U.
In the field of non-monotonic logics, the notion of Rational Closure (RC) is acknowledged as a prominent approach. In recent years, RC has gained even more popularity in the context of Description Logics (DLs), the logic underpinning the semantic web standard ontology language OWL 2, whose main ingredients are classes and roles. In this work, we show how to integrate RC within the triple language RDFS, which together with OWL2 are the two major standard semantic web ontology languages. To do so, we start from ?df, which is the logic behind RDFS, and then extend it to ?df?, allowing to state that two entities are incompatible. Eventually, we propose defeasible ?df? via a typical RC construction. The main features of our approach are: (i) unlike most other approaches that add an extra non-monotone rule layer on top of monotone RDFS, defeasible ?df? remains syntactically a triple language and is a simple extension of ?df? by introducing some new predicate symbols with specific semantics. In particular, any RDFS reasoner/store may handle them as ordinary terms if it does not want to take account for the extra semantics of the new predicate symbols; (ii) the defeasible ?df? entailment decision procedure is build on top of the ?df? entailment decision procedure, which in turn is an extension of the one for ?df via some additional inference rules favouring an potential implementation; and (iii) defeasible ?df? entailment can be decided in polynomial time.Source: ISTI Technical Reports 008/2020, 2020
DOI: 10.32079/isti-tr-2020/008
Metrics:
See at:
ISTI Repository | CNR ExploRA
2020
Conference article
Open Access
The serializable and incremental semantic reasoner fuzzyDL
Huitzil I., Straccia U., Bobed C., Mena E., Bobillo F.
Serializable and incremental semantic reasoners make it easier to reason on a mobile device with limited resources, as they allow the reuse of previous inferences computed by another device without starting from scratch. This paper describes an extension of the fuzzy ontology reasoner fuzzyDL to make it the first serializable and incremental semantic reasoner. We empirically show that the size of the serialized files is smaller than in another serializable semantic reasoner (JFact), and that there is a significant decrease in the reasoning time.Source: FUZZ-IEEE 2020 - IEEE International Conference on Fuzzy Systems, Glasgow, UK, 19-24 July 2020
DOI: 10.1109/fuzz48607.2020.9177835
Metrics:
Huitzil I., Straccia U., Bobed C., Mena E., Bobillo F.
Serializable and incremental semantic reasoners make it easier to reason on a mobile device with limited resources, as they allow the reuse of previous inferences computed by another device without starting from scratch. This paper describes an extension of the fuzzy ontology reasoner fuzzyDL to make it the first serializable and incremental semantic reasoner. We empirically show that the size of the serialized files is smaller than in another serializable semantic reasoner (JFact), and that there is a significant decrease in the reasoning time.Source: FUZZ-IEEE 2020 - IEEE International Conference on Fuzzy Systems, Glasgow, UK, 19-24 July 2020
DOI: 10.1109/fuzz48607.2020.9177835
Metrics:
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Recolector de Ciencia Abierta, RECOLECTA | zaguan.unizar.es | doi.org | ieeexplore.ieee.org | CNR ExploRA
2020
Report
Open Access
Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting
Cardillo F. A., Straccia U.
OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given a target class T of an OWL ontology, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T. To do so, we present Fuzzy OWL-BOOST that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation. An interesting feature is that the learned rules can be represented directly into Fuzzy OWL 2. As a consequence, any Fuzzy OWL 2 reasoner can then be used to automatically determine/classify (and to which degree) whether an individual belongs to the target class T.Source: Research report, pp.1–26, 2020
Cardillo F. A., Straccia U.
OWL ontologies are nowadays a quite popular way to describe structured knowledge in terms of classes, relations among classes and class instances. In this paper, given a target class T of an OWL ontology, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T. To do so, we present Fuzzy OWL-BOOST that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation. An interesting feature is that the learned rules can be represented directly into Fuzzy OWL 2. As a consequence, any Fuzzy OWL 2 reasoner can then be used to automatically determine/classify (and to which degree) whether an individual belongs to the target class T.Source: Research report, pp.1–26, 2020
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2020
Conference article
Open Access
How Much Knowledge is in a Knowledge Base? Introducing Knowledge Measures (Preliminary Report)
Straccia U.
In this work we address the following question: can we measure how much knowledge a knowledge base represents? We answer to this question (i) by describing properties (axioms) that a knowledge measure we believe should have in measuring the amount of knowledge of a knowledge base (kb); and (ii) provide a concrete example of such a measure, based on the notion of entropy. We also introduce related kb notions such as (i) accuracy; (ii) conciseness; and (iii) Pareto optimality. Informally, they address the following questions: (i) how precise is a kb in describing the actual world? (ii) how succinct is a kb w.r.t. the knowledge it represents? and (iii) can we increase accuracy without decreasing conciseness, or vice-versa?Source: European Conference on Artificial Intelligence (ECAI-20), pp. 905–912, Santiago de Compostela, SPAIN, 29/08/2020 - 08/09/2020
DOI: 10.3233/faia200182
Metrics:
Straccia U.
In this work we address the following question: can we measure how much knowledge a knowledge base represents? We answer to this question (i) by describing properties (axioms) that a knowledge measure we believe should have in measuring the amount of knowledge of a knowledge base (kb); and (ii) provide a concrete example of such a measure, based on the notion of entropy. We also introduce related kb notions such as (i) accuracy; (ii) conciseness; and (iii) Pareto optimality. Informally, they address the following questions: (i) how precise is a kb in describing the actual world? (ii) how succinct is a kb w.r.t. the knowledge it represents? and (iii) can we increase accuracy without decreasing conciseness, or vice-versa?Source: European Conference on Artificial Intelligence (ECAI-20), pp. 905–912, Santiago de Compostela, SPAIN, 29/08/2020 - 08/09/2020
DOI: 10.3233/faia200182
Metrics:
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ebooks.iospress.nl | ISTI Repository | CNR ExploRA