A semantic knowledge discovery framework for detecting online terrorist networks

Andrea Ciapetti; Giulia Ruggiero; Daniele Toti

doi:10.1007/978-3-030-05716-9_10

A semantic knowledge discovery framework for detecting online terrorist networks

Andrea Ciapetti, Giulia Ruggiero, Daniele Toti

Innovation Engineering S.r.l.

Risultato della ricerca: Contributo in libro › Contributo a convegno

Abstract

This paper presents a knowledge discovery framework, with the purpose of detecting terrorist presence in terms of potential suspects and networks on the open and Deep Web. The framework combines information extraction methods and tools and natural language processing techniques, together with semantic information derived from social network analysis, in order to automatically process online content coming from disparate sources and identify people and relationships that may be linked to terrorist activities. This framework has been developed within the context of the DANTE Horizon 2020 project, as part of a larger international effort to detect and analyze terrorist-related content from online sources and help international police organizations in their investigations against crime and terrorism.

Lingua originale	English
Titolo della pubblicazione ospite	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pagine	120-131
Numero di pagine	12
Volume	11296
DOI	https://doi.org/10.1007/978-3-030-05716-9_10
Stato di pubblicazione	Pubblicato - 2019
Evento	25th International Conference on MultiMedia Modeling, MMM 2019 - Thessaloniki Durata: 8 gen 2019 → 11 gen 2019

Serie di pubblicazioni

Nome	LECTURE NOTES IN COMPUTER SCIENCE

Convegno

Convegno	25th International Conference on MultiMedia Modeling, MMM 2019
Città	Thessaloniki
Periodo	8/1/19 → 11/1/19

Keywords

Group discovery
Knowledge discovery
Named entity recognition
Natural language processing
Ontology building

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10.1007/978-3-030-05716-9_10

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Ciapetti, A., Ruggiero, G., & Toti, D. (2019). A semantic knowledge discovery framework for detecting online terrorist networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11296, pagg. 120-131). (LECTURE NOTES IN COMPUTER SCIENCE). https://doi.org/10.1007/978-3-030-05716-9_10

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title = "A semantic knowledge discovery framework for detecting online terrorist networks",

abstract = "This paper presents a knowledge discovery framework, with the purpose of detecting terrorist presence in terms of potential suspects and networks on the open and Deep Web. The framework combines information extraction methods and tools and natural language processing techniques, together with semantic information derived from social network analysis, in order to automatically process online content coming from disparate sources and identify people and relationships that may be linked to terrorist activities. This framework has been developed within the context of the DANTE Horizon 2020 project, as part of a larger international effort to detect and analyze terrorist-related content from online sources and help international police organizations in their investigations against crime and terrorism.",

keywords = "Group discovery, Knowledge discovery, Named entity recognition, Natural language processing, Ontology building, Group discovery, Knowledge discovery, Named entity recognition, Natural language processing, Ontology building",

author = "Andrea Ciapetti and Giulia Ruggiero and Daniele Toti",

year = "2019",

doi = "10.1007/978-3-030-05716-9_10",

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note = "25th International Conference on MultiMedia Modeling, MMM 2019 ; Conference date: 08-01-2019 Through 11-01-2019",

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Ciapetti, A, Ruggiero, G & Toti, D 2019, A semantic knowledge discovery framework for detecting online terrorist networks. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 11296, LECTURE NOTES IN COMPUTER SCIENCE, pagg. 120-131, 25th International Conference on MultiMedia Modeling, MMM 2019, Thessaloniki, 8/1/19. https://doi.org/10.1007/978-3-030-05716-9_10

A semantic knowledge discovery framework for detecting online terrorist networks. / Ciapetti, Andrea; Ruggiero, Giulia; Toti, Daniele.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 11296 2019. pag. 120-131 (LECTURE NOTES IN COMPUTER SCIENCE).

Risultato della ricerca: Contributo in libro › Contributo a convegno

TY - GEN

T1 - A semantic knowledge discovery framework for detecting online terrorist networks

AU - Ciapetti, Andrea

AU - Ruggiero, Giulia

AU - Toti, Daniele

PY - 2019

Y1 - 2019

N2 - This paper presents a knowledge discovery framework, with the purpose of detecting terrorist presence in terms of potential suspects and networks on the open and Deep Web. The framework combines information extraction methods and tools and natural language processing techniques, together with semantic information derived from social network analysis, in order to automatically process online content coming from disparate sources and identify people and relationships that may be linked to terrorist activities. This framework has been developed within the context of the DANTE Horizon 2020 project, as part of a larger international effort to detect and analyze terrorist-related content from online sources and help international police organizations in their investigations against crime and terrorism.

AB - This paper presents a knowledge discovery framework, with the purpose of detecting terrorist presence in terms of potential suspects and networks on the open and Deep Web. The framework combines information extraction methods and tools and natural language processing techniques, together with semantic information derived from social network analysis, in order to automatically process online content coming from disparate sources and identify people and relationships that may be linked to terrorist activities. This framework has been developed within the context of the DANTE Horizon 2020 project, as part of a larger international effort to detect and analyze terrorist-related content from online sources and help international police organizations in their investigations against crime and terrorism.

KW - Group discovery

KW - Knowledge discovery

KW - Named entity recognition

KW - Natural language processing

KW - Ontology building

KW - Group discovery

KW - Knowledge discovery

KW - Named entity recognition

KW - Natural language processing

KW - Ontology building

UR - http://hdl.handle.net/10807/165865

U2 - 10.1007/978-3-030-05716-9_10

DO - 10.1007/978-3-030-05716-9_10

M3 - Conference contribution

SN - 978-3-030-05715-2

VL - 11296

T3 - LECTURE NOTES IN COMPUTER SCIENCE

SP - 120

EP - 131

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

T2 - 25th International Conference on MultiMedia Modeling, MMM 2019

Y2 - 8 January 2019 through 11 January 2019

ER -

A semantic knowledge discovery framework for detecting online terrorist networks

Abstract

Serie di pubblicazioni

Convegno

Keywords

OSS delle Nazioni Unite

Accesso al documento

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