Weighted Defeasible Knowledge Bases and a Multipreference Semantics for a Deep Neural Network Model

Laura Giordano; Daniele Theseider Dupré

doi:10.1007/978-3-030-75775-5_16

Weighted Defeasible Knowledge Bases and a Multipreference Semantics for a Deep Neural Network Model

Department of Science and Technological Innovation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper we investigate the relationships between a multipreferential semantics for defeasible reasoning in knowledge representation and a deep neural network model. Weighted knowledge bases for description logics are considered under a “concept-wise” multipreference semantics. The semantics is further extended to fuzzy interpretations and exploited to provide a preferential interpretation of Multilayer Perceptrons, under some condition.

Original language	English
Title of host publication	Logics in Artiﬁcial Intelligence - 17th European Conference, JELIA 2021, Proceedings
Editors	Wolfgang Faber, Gerhard Friedrich, Martin Gebser, Michael Morak
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	225-242
Number of pages	18
ISBN (Print)	9783030757748
DOIs	https://doi.org/10.1007/978-3-030-75775-5_16
Publication status	Published - 2021
Event	17th European Conference on Logics in Artiﬁcial Intelligence, JELIA 2021 - Virtual, Online Duration: 17 May 2021 → 20 May 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12678 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th European Conference on Logics in Artiﬁcial Intelligence, JELIA 2021
City	Virtual, Online
Period	17/05/21 → 20/05/21

Access to Document

10.1007/978-3-030-75775-5_16

Cite this

Giordano, L., & Theseider Dupré, D. (2021). Weighted Defeasible Knowledge Bases and a Multipreference Semantics for a Deep Neural Network Model. In W. Faber, G. Friedrich, M. Gebser, & M. Morak (Eds.), Logics in Artiﬁcial Intelligence - 17th European Conference, JELIA 2021, Proceedings (pp. 225-242). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12678 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-75775-5_16

Giordano, Laura ; Theseider Dupré, Daniele. / Weighted Defeasible Knowledge Bases and a Multipreference Semantics for a Deep Neural Network Model. Logics in Artiﬁcial Intelligence - 17th European Conference, JELIA 2021, Proceedings. editor / Wolfgang Faber ; Gerhard Friedrich ; Martin Gebser ; Michael Morak. Springer Science and Business Media Deutschland GmbH, 2021. pp. 225-242 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Weighted Defeasible Knowledge Bases and a Multipreference Semantics for a Deep Neural Network Model",

abstract = "In this paper we investigate the relationships between a multipreferential semantics for defeasible reasoning in knowledge representation and a deep neural network model. Weighted knowledge bases for description logics are considered under a “concept-wise” multipreference semantics. The semantics is further extended to fuzzy interpretations and exploited to provide a preferential interpretation of Multilayer Perceptrons, under some condition.",

author = "Laura Giordano and \{Theseider Dupr{\'e}\}, Daniele",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 17th European Conference on Logics in Artiﬁcial Intelligence, JELIA 2021 ; Conference date: 17-05-2021 Through 20-05-2021",

year = "2021",

doi = "10.1007/978-3-030-75775-5\_16",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "225--242",

editor = "Wolfgang Faber and Gerhard Friedrich and Martin Gebser and Michael Morak",

booktitle = "Logics in Artiﬁcial Intelligence - 17th European Conference, JELIA 2021, Proceedings",

address = "Germany",

}

Giordano, L & Theseider Dupré, D 2021, Weighted Defeasible Knowledge Bases and a Multipreference Semantics for a Deep Neural Network Model. in W Faber, G Friedrich, M Gebser & M Morak (eds), Logics in Artiﬁcial Intelligence - 17th European Conference, JELIA 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12678 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 225-242, 17th European Conference on Logics in Artiﬁcial Intelligence, JELIA 2021, Virtual, Online, 17/05/21. https://doi.org/10.1007/978-3-030-75775-5_16

Weighted Defeasible Knowledge Bases and a Multipreference Semantics for a Deep Neural Network Model. / Giordano, Laura ; Theseider Dupré, Daniele.
Logics in Artiﬁcial Intelligence - 17th European Conference, JELIA 2021, Proceedings. ed. / Wolfgang Faber; Gerhard Friedrich; Martin Gebser; Michael Morak. Springer Science and Business Media Deutschland GmbH, 2021. p. 225-242 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12678 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Weighted Defeasible Knowledge Bases and a Multipreference Semantics for a Deep Neural Network Model

AU - Giordano, Laura

AU - Theseider Dupré, Daniele

PY - 2021

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N2 - In this paper we investigate the relationships between a multipreferential semantics for defeasible reasoning in knowledge representation and a deep neural network model. Weighted knowledge bases for description logics are considered under a “concept-wise” multipreference semantics. The semantics is further extended to fuzzy interpretations and exploited to provide a preferential interpretation of Multilayer Perceptrons, under some condition.

AB - In this paper we investigate the relationships between a multipreferential semantics for defeasible reasoning in knowledge representation and a deep neural network model. Weighted knowledge bases for description logics are considered under a “concept-wise” multipreference semantics. The semantics is further extended to fuzzy interpretations and exploited to provide a preferential interpretation of Multilayer Perceptrons, under some condition.

UR - https://www.scopus.com/pages/publications/85111126198

U2 - 10.1007/978-3-030-75775-5_16

DO - 10.1007/978-3-030-75775-5_16

M3 - Conference contribution

AN - SCOPUS:85111126198

SN - 9783030757748

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 225

EP - 242

BT - Logics in Artiﬁcial Intelligence - 17th European Conference, JELIA 2021, Proceedings

A2 - Faber, Wolfgang

A2 - Friedrich, Gerhard

A2 - Gebser, Martin

A2 - Morak, Michael

PB - Springer Science and Business Media Deutschland GmbH

T2 - 17th European Conference on Logics in Artiﬁcial Intelligence, JELIA 2021

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ER -

Giordano L , Theseider Dupré D. Weighted Defeasible Knowledge Bases and a Multipreference Semantics for a Deep Neural Network Model. In Faber W, Friedrich G, Gebser M, Morak M, editors, Logics in Artiﬁcial Intelligence - 17th European Conference, JELIA 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 225-242. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-75775-5_16

Weighted Defeasible Knowledge Bases and a Multipreference Semantics for a Deep Neural Network Model

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