A Survey of Operations Research and Analytics Literature Related to Anti-Human Trafficking

Human trafficking is a compound social, economic, and human rights issue occurring in all regions of the world. Understanding and addressing such a complex crime requires effort from multiple domains and perspectives. As of this writing, no systematic review exists of the Operations Research and Analytics literature applied to the domain of human trafficking. The purpose of this work is to fill this gap through a systematic literature review. Studies matching our search criteria were found ranging from 2010 to March 2021. These studies were gathered and analyzed to help answer the following three research questions: (i) What aspects of human trafficking are being studied by Operations Research and Analytics researchers? (ii) What Operations Research and Analytics methods are being applied in the anti-human trafficking domain? and (iii) What are the existing research gaps associated with (i) and (ii)? By answering these questions, we illuminate the extent to which these topics have been addressed in the literature, as well as inform future research opportunities in applying analytical methods to advance the fight against human trafficking.Comment: 28 pages, 6 Figures, 2 Table

Named Entity Resolution in Personal Knowledge Graphs

Entity Resolution (ER) is the problem of determining when two entities refer to the same underlying entity. The problem has been studied for over 50 years, and most recently, has taken on new importance in an era of large, heterogeneous 'knowledge graphs' published on the Web and used widely in domains as wide ranging as social media, e-commerce and search. This chapter will discuss the specific problem of named ER in the context of personal knowledge graphs (PKGs). We begin with a formal definition of the problem, and the components necessary for doing high-quality and efficient ER. We also discuss some challenges that are expected to arise for Web-scale data. Next, we provide a brief literature review, with a special focus on how existing techniques can potentially apply to PKGs. We conclude the chapter by covering some applications, as well as promising directions for future research.Comment: To appear as a book chapter by the same name in an upcoming (Oct. 2023) book `Personal Knowledge Graphs (PKGs): Methodology, tools and applications' edited by Tiwari et a

The Dark Web and Human Trafficking

This is a quantitative-comparative analysis that focuses on Artificial Intelligence (AI) platforms that assist law enforcement agencies as they combat human trafficking. Human trafficking is a Transnational Organized Crime (TOC) which means it can impact every country in the world, and in doing so, impact every person in the world. AI uses machine-learning capabilities to identify clusters, odd and/or unusual font, words, numbers, and other markers in advertisements that promote the sale of human beings. Human trafficking affects males, females, and children of all ages and can include different types of trafficking such as sex and labor trafficking. By using these AI platforms, law enforcement officers are able to identify and help more human beings than ever before in a quicker timeframe. This quantitative-comparative analysis compared Spotlight, Traffick Jam, Traffick Cam, and Domain Insight Graph (DIG) to determine if these platforms were helping law enforcement. The study revolved around the questions of accuracy, consistency, and effectiveness with each platform and found that the majority of AI platforms led the way to promote better, more efficient platforms by the same companies that learned how changes could assist law enforcement more in the future. While each platform assisted in their own ways, there were deltas in each area that leads to the need for future research in the area of AI and how it can be used to help victims of human trafficking and convict human traffickers more in later years

Making linked-data accessible: A review

Linked-Data (LD) is a paradigm that utilises the RDF triplestore to describe numerous pieces of knowledge linked together. When an entity is retrieved in LD, its associated data becomes instantly obtainable. SPARQL is the query language that allows users to access LD. On the other hand, SPARQL has a complicated syntax that necessitates previous knowledge. Thus, in order to encourage the end-users to use LD, it is crucial to allow them to obtain the data efficiently, in addition to improving their overall experience. Instead of manually constructing SPARQL queries, this paper investigates and reviews existing methods in which LD can be accessed using various tools and techniques, including query builders, visualisation approaches, and several LD applications. We then identify gaps within the literature and highlight future research directions