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 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.

Authors

• Mayank Kejriwal Information Sciences Institute, University of Southern California
• Pedro Szekely
• Craig Knoblock

Keywords

• Ontologies
• Knowledge discovery
• Semantics
• Information retrieval
• Data mining
• Web services
• Prototype
• Information Extraction
• Domain Experts
• Human Trafficking
• Semantic Model
• Named Entity Recognition
• Traditional Field
• Question Answering
• Front End
• Unstructured Data
• Question Categories
• Conversion Scheme
• Domain Ontology
• Semantic Types
• Running Example
• Original Query
• SPARQL Query
• Non-zero Scores
• Triple Store
• Semantic Web
• Structured Information Retrieval
• knowledge graphs
• Illicit Web