Investigative Knowledge Discovery for Combating Illicit Activities

Developing scalable, semi-automatic approaches to derive insights from a domain-specific Web corpus is a longstanding research problem in the knowledge discovery community. The problem is particularly challenging in illicit fields, such as human trafficking, where traditional assumptions concerning...

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Bibliographic Details
Published in:IEEE intelligent systems p. 1
Main Authors: Kejriwal, Mayank, Szekely, Pedro, Knoblock, Craig
Format: Journal Article
Language:English
Published: IEEE 12.01.2018
Subjects:
artificial intelligence
computing methodologies
data
Data mining
database applications
database management
Information retrieval
information technology and systems
Intelligent systems
intelligent web services and semantic web
knowledge and data engineering tools and techniques
Knowledge discovery
Object recognition
Ontologies
Semantics
ISSN:1541-1672
Online Access:Get full text
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Summary:Developing scalable, semi-automatic approaches to derive insights from a domain-specific Web corpus is a longstanding research problem in the knowledge discovery community. The problem is particularly challenging in illicit fields, such as human trafficking, where traditional assumptions concerning information representation are frequently violated. In this article, we describe an end-to-end investigative knowledge discovery system for illicit Web domains. We built and evaluated a prototype, involving separate components for information extraction, semantic modeling and query execution, on a real-world human trafficking Web corpus containing 1.3 million pages, with promising results. The prototype includes a GUI currently used by US law enforcement agencies to combat illicit activity.
ISSN:1541-1672
DOI:10.1109/MIS.2018.111144708