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Marina di Pisa, Italy

Adderley R.,A E Solutions BI Ltd. | Badii A.,University of Reading | Evangelio R.H.,TU Berlin | Raffaelli M.,Synthema srl | And 2 more authors.
Communications in Computer and Information Science | Year: 2014

With increasing complexity of systems under surveillance, demand grows for automated video-based surveillance systems which are able to support end users in making sense of situational context from the amount of available data and incoming data streams. Traditionally, those systems have been developed based on techniques derived from the fields of image processing and pattern recognition. This paper presents MOSAIC (Multi-Modal Situation Assessment and Analytics Platform), a system which aims at exploiting multi-modal data analysis comprising advanced tools for video analytics, text mining, social network analysis, and decision support in order to provide from a richer context an understanding of behaviour of the system under surveillance and to support police personnel in decision making processes. © Springer International Publishing Switzerland 2014. Source


Aliprandi C.,Synthema srl | Ronzano F.,CNR Institute of Neuroscience | Marchetti A.,CNR Institute of Neuroscience | Tesconi M.,CNR Institute of Neuroscience | Minutoli S.,CNR Institute of Neuroscience
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

Many attempts have been made to extract structured data from Web resources, exposing them as RDF triples and interlinking them with other RDF datasets: in this way it is possible to create clouds of highly integrated Semantic Web data collections. In this paper we describe an approach to enhance the extraction of semantic contents from unstructured textual documents, in particular considering Wikipedia articles and focusing on event mining. Starting from the deep parsing of a set of English Wikipedia articles, we produce a semantic annotation compliant with the Knowledge Annotation Format (KAF). We extract events from the KAF semantic annotation and then we structure each event as a set of RDF triples linked to both DBpedia and WordNet. We point out examples of automatically mined events, providing some general evaluation of how our approach may discover new events and link them to existing contents. © 2011 Springer-Verlag. Source


Aliprandi C.,Synthema srl | Marchetti A.,CNR Institute of Neuroscience
Communications in Computer and Information Science | Year: 2011

We introduce the CAPER project (Collaborative information, Acquisition, Processing, Exploitation and Reporting), partially funded by the European Commission. The goal of CAPER is to create a common platform for the prevention of organized crime through sharing, exploitation and linking of Open and Closed information Sources. CAPER will support collaborative multilingual analysis of unstructured and audiovisual contents, based on Text Mining and Visual Analytics technologies. CAPER will allow Law Enforcement Agencies (LEAs) to share informational, investigative and experiential knowledge. © 2011 Springer-Verlag. Source


Aliprandi C.,Synthema srl | Arraiza Irujo J.,Vicomtech | Cuadros M.,Vicomtech | Maier S.,Fraunhofer Institute for Computer Graphics Research | And 2 more authors.
Communications in Computer and Information Science | Year: 2014

Law Enforcement Agencies (LEAs) are increasingly more reliant on information and communication technologies and affected by a society shaped by the Internet and Social Media. The richness and quantity of information available from open sources, if properly gathered and processed, can provide valuable intelligence and help drawing inference from existing closed source intelligence. This paper presents CAPER, a state-of-the-art platform for the prevention of organised crime, created in cooperation with European LEAs. CAPER supports information sharing and multi-modal analysis of open and closed information sources, mainly based on Natural Language Processing (NLP) and Visual Analytics (VA) technologies. © Springer International Publishing Switzerland 2014. Source


Seidler P.,A E Solutions BI Ltd. | Adderley R.,A E Solutions BI Ltd. | Badii A.,University of Reading | Raffaelli M.,Synthema srl
ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining | Year: 2014

With increasing complexity of the social systems under surveillance, demand grows for automated tools which are able to support end users in making sense of situational context from the amount of available data and incoming data streams. This paper presents MOSAIC (Multi-Modal Situation Assessment and Analytics Platform), a semantically integrated system which aims at exploiting multi-modal data analysis comprising advanced tools for text and data mining, criminal network analysis, and decision support. The aim is to provide, from an enriched context, an understanding of behaviour of the system under surveillance thus supporting authorities in their decision making processes. Specific measures and algorithms have been developed to support analysts in retrieving, analysing, and disrupting criminal networks, identifying offenders that pose the greatest harm aligned with domain-specific strategies, as well as enabling the investigation of intervention strategies. A case study is provided in order to illustrate the system in practice. © 2014 IEEE. Source

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