6,960 research outputs found
Automatic Taxonomy Generation - A Use-Case in the Legal Domain
A key challenge in the legal domain is the adaptation and representation of
the legal knowledge expressed through texts, in order for legal practitioners
and researchers to access this information easier and faster to help with
compliance related issues. One way to approach this goal is in the form of a
taxonomy of legal concepts. While this task usually requires a manual
construction of terms and their relations by domain experts, this paper
describes a methodology to automatically generate a taxonomy of legal noun
concepts. We apply and compare two approaches on a corpus consisting of
statutory instruments for UK, Wales, Scotland and Northern Ireland laws.Comment: 9 page
A framework for the forensic investigation of unstructured email relationship data
Our continued reliance on email communications ensures that it remains a major source of evidence during a digital investigation. Emails comprise both structured and unstructured data. Structured data provides qualitative information to the forensics examiner and is typically viewed through existing tools. Unstructured data is more complex as it comprises information associated with social networks, such as relationships within the network, identification of key actors and power relations, and there are currently no standardised tools for its forensic analysis. Moreover, email investigations may involve many hundreds of actors and thousands of messages. This paper posits a framework for the forensic investigation of email data. In particular, it focuses on the triage and analysis of unstructured data to identify key actors and relationships within an email network. This paper demonstrates the applicability of the approach by applying relevant stages of the framework to the Enron email corpus. The paper illustrates the advantage of triaging this data to identify (and discount) actors and potential sources of further evidence. It then applies social network analysis techniques to key actors within the data set. This paper posits that visualisation of unstructured data can greatly aid the examiner in their analysis of evidence discovered during an investigation
The web of federal crimes in Brazil: topology, weaknesses, and control
Law enforcement and intelligence agencies worldwide struggle to find
effective ways to fight and control organized crime. However, illegal networks
operate outside the law and much of the data collected is classified.
Therefore, little is known about criminal networks structure, topological
weaknesses, and control. In this contribution we present a unique criminal
network of federal crimes in Brazil. We study its structure, its response to
different attack strategies, and its controllability. Surprisingly, the network
composed of multiple crimes of federal jurisdiction has a giant component,
enclosing more than a half of all its edges. This component shows some typical
social network characteristics, such as small-worldness and high clustering
coefficient, however it is much "darker" than common social networks, having
low levels of edge density and network efficiency. On the other side, it has a
very high modularity value, . Comparing multiple attack strategies, we
show that it is possible to disrupt the giant component of the network by
removing only of its edges or nodes, according to a module-based
prescription, precisely due to its high modularity. Finally, we show that the
component is controllable, in the sense of the exact network control theory, by
getting access to of the driver nodes.Comment: 9 pages, 5 figure
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