@inproceedings{fda9e784fb0c4116a3f820170e0e486e,
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 = "Daniele Toti and Andrea Ciapetti and Giulia Ruggiero",
year = "2019",
doi = "10.1007/978-3-030-05716-9_10",
language = "English",
isbn = "978-3-030-05715-2",
volume = "11296",
series = "LECTURE NOTES IN COMPUTER SCIENCE",
pages = "120--131",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
note = "25th International Conference on MultiMedia Modeling, MMM 2019 ; Conference date: 08-01-2019 Through 11-01-2019",
}