Name
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. |
Year | Venue | Field |
|---|---|---|
2019 | MMM | Semantic memory,World Wide Web,Pattern recognition,Computer science,Social network analysis,Terrorism,Semantic information,Information extraction,Knowledge extraction,Deep Web,Artificial intelligence,Named-entity recognition |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Ciapetti | 1 | 0 | 1.01 |
| Giulia Ruggiero | 2 | 0 | 0.34 |
| Daniele Toti | 3 | 105 | 13.86 |