3 Search Results for "Romanelli, Marco"

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Survey

Semantic Web: Past, Present, and Future

Authors: Ansgar Scherp, Gerd Groener, Petr Škoda, Katja Hose, and Maria-Esther Vidal

Published in: TGDK, Volume 2, Issue 1 (2024): Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge, Volume 2, Issue 1

Abstract

Ever since the vision was formulated, the Semantic Web has inspired many generations of innovations. Semantic technologies have been used to share vast amounts of information on the Web, enhance them with semantics to give them meaning, and enable inference and reasoning on them. Throughout the years, semantic technologies, and in particular knowledge graphs, have been used in search engines, data integration, enterprise settings, and machine learning. In this paper, we recap the classical concepts and foundations of the Semantic Web as well as modern and recent concepts and applications, building upon these foundations. The classical topics we cover include knowledge representation, creating and validating knowledge on the Web, reasoning and linking, and distributed querying. We enhance this classical view of the so-called "Semantic Web Layer Cake" with an update of recent concepts that include provenance, security and trust, as well as a discussion of practical impacts from industry-led contributions. We conclude with an outlook on the future directions of the Semantic Web. This is a living document. If you like to contribute, please contact the first author and visit: https://github.com/ascherp/semantic-web-primer

Cite as

Ansgar Scherp, Gerd Groener, Petr Škoda, Katja Hose, and Maria-Esther Vidal. Semantic Web: Past, Present, and Future. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 3:1-3:37, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{scherp_et_al:TGDK.2.1.3,
  author =	{Scherp, Ansgar and Groener, Gerd and \v{S}koda, Petr and Hose, Katja and Vidal, Maria-Esther},
  title =	{{Semantic Web: Past, Present, and Future}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{3:1--3:37},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.3},
  URN =		{urn:nbn:de:0030-drops-198607},
  doi =		{10.4230/TGDK.2.1.3},
  annote =	{Keywords: Linked Open Data, Semantic Web Graphs, Knowledge Graphs}
}

Document

DOI: 10.4230/OASIcs.Gabbrielli.11

Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy

Authors: Moreno Falaschi, Catuscia Palamidessi, and Marco Romanelli

Published in: OASIcs, Volume 86, Recent Developments in the Design and Implementation of Programming Languages (2020)

Abstract

This paper shows how we can combine the power of machine learning with the flexibility of constraints. More specifically, we show how machine learning models can be represented by first-order logic theories, and how to derive these theories. The advantage of this representation is that it can be augmented with additional formulae, representing constraints of some kind on the data domain. For instance, new knowledge, or potential attackers, or fairness desiderata. We consider various kinds of learning algorithms (neural networks, k-nearest-neighbours, decision trees, support vector machines) and for each of them we show how to infer the FOL formulae. Then we focus on one particular application domain, namely the field of security and privacy. The idea is to represent the potentialities and goals of the attacker as a set of constraints, then use a constraint solver (more precisely, a solver modulo theories) to verify the satisfiability. If a solution exists, then it means that an attack is possible, otherwise, the system is safe. We show various examples from different areas of security and privacy; specifically, we consider a side-channel attack on a password checker, a malware attack on smart health systems, and a model-inversion attack on a neural network.

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Moreno Falaschi, Catuscia Palamidessi, and Marco Romanelli. Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy. In Recent Developments in the Design and Implementation of Programming Languages. Open Access Series in Informatics (OASIcs), Volume 86, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{falaschi_et_al:OASIcs.Gabbrielli.11,
  author =	{Falaschi, Moreno and Palamidessi, Catuscia and Romanelli, Marco},
  title =	{{Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy}},
  booktitle =	{Recent Developments in the Design and Implementation of Programming Languages},
  pages =	{11:1--11:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-171-9},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{86},
  editor =	{de Boer, Frank S. and Mauro, Jacopo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Gabbrielli.11},
  URN =		{urn:nbn:de:0030-drops-132338},
  doi =		{10.4230/OASIcs.Gabbrielli.11},
  annote =	{Keywords: Constraints, machine learning, privacy, security}
}

Document

Invited Paper

DOI: 10.4230/LIPIcs.CONCUR.2020.4

Modern Applications of Game-Theoretic Principles (Invited Paper)

Authors: Catuscia Palamidessi and Marco Romanelli

Published in: LIPIcs, Volume 171, 31st International Conference on Concurrency Theory (CONCUR 2020)

Abstract

Game theory is the study of the strategic behavior of rational decision makers who are aware that their decisions affect one another. Its simple but universal principles have found applications in the most diverse disciplines, including economics, social sciences, evolutionary biology, as well as logic, system science and computer science. Despite its long-standing tradition and its many advances, game theory is still a young and developing science. In this paper, we describe some recent and exciting applications in the fields of machine learning and privacy.

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Catuscia Palamidessi and Marco Romanelli. Modern Applications of Game-Theoretic Principles (Invited Paper). In 31st International Conference on Concurrency Theory (CONCUR 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 171, pp. 4:1-4:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{palamidessi_et_al:LIPIcs.CONCUR.2020.4,
  author =	{Palamidessi, Catuscia and Romanelli, Marco},
  title =	{{Modern Applications of Game-Theoretic Principles}},
  booktitle =	{31st International Conference on Concurrency Theory (CONCUR 2020)},
  pages =	{4:1--4:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-160-3},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{171},
  editor =	{Konnov, Igor and Kov\'{a}cs, Laura},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2020.4},
  URN =		{urn:nbn:de:0030-drops-128167},
  doi =		{10.4230/LIPIcs.CONCUR.2020.4},
  annote =	{Keywords: Game theory, machine learning, privacy, security}
}

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