=Paper=
{{Paper
|id=Vol-1486/paper_88
|storemode=property
|title=Using a Knowledge Graph to Combat Human Trafficking
|pdfUrl=https://ceur-ws.org/Vol-1486/paper_88.pdf
|volume=Vol-1486
|dblpUrl=https://dblp.org/rec/conf/semweb/SzekelyKSYPSKNM15
}}
==Using a Knowledge Graph to Combat Human Trafficking==
https://ceur-ws.org/Vol-1486/paper_88.pdf
Demonstration Paper:
Using a Knowledge Graph to
Combat Human Trafficking?
Pedro Szekely1 , Craig A. Knoblock1 , Jason Slepicka1 , Andrew Philpot1 ,
Amandeep Singh1 , Chengye Yin1 , Dipsy Kapoor1 , Prem Natarajan1 , Daniel
Marcu1 , Kevin Knight1 , David Stallard1 Subessware S. Karunamoorthy1 ,
Rajagopal Bojanapalli1 , Steven Minton2 , Brian Amanatullah2 , Todd Hughes3 ,
Mike Tamayo3 , David Flynt3 , Rachel Artiss3 , Shih-Fu Chang4 , Tao Chen4 ,
Gerald Hiebel5 , and Lidia Ferreira6
1
University of Southern California, Information Sciences Institute, Marina del Rey,
CA, USA
{pszekely,knoblock,slepicka,philpot,dipsy,pnataraj,marcu,knight,stallard}@isi.edu
{singhama,chengyey,selvamee,bojanapa}@usc.edu
2
InferLink Corporation, El Segundo, CA, USA
{sminton,bamanatullah}@inferlink.com
3
Next Century Corporation Columbia, MD, USA
{todd.hughes,mike.tamayo,david.flynt,rachel.artiss}@nextcentury.com
4
Columbia University, New York, USA
{sfchang,taochen}@ee.columbia.edu
5
Universitat Innsbruck, Austria
gerald.hiebel@uibk.ac.at
6
Federal University of Minas Gerais, Brazil
lidiaferreira@dcc.ufmg.br
Abstract. There is a huge amount of data spread across the web and
stored in databases that we can use to build knowledge graphs. However,
exploiting this data to build knowledge graphs is difficult due to the
heterogeneity of the sources, scale of the amount of data, and noise in the
data. In this work, we developed a system for building knowledge graphs
by exploiting semantic technologies to reconcile the data continuously
crawled from diverse sources, to scale to billions of triples extracted from
the crawled content, and to support interactive queries on the data. We
applied our approach, implemented in the DIG system, to the problem
of combating human trafficking and deployed it to six law enforcement
agencies and several non-governmental organizations to assist them with
finding traffickers and helping victims. The demonstration will show the
resulting application that is currently in use by these law enforcement
agencies.
?
This is a demonstration paper for the In-Use Paper titled Building and Using a
Knowledge Graph to Combat Human Trafficking by Szekely et al. presented at ISWC
2015
2 Szekely, Knoblock et al.
Keywords: Linked Data, Knowledge Graphs, Entity Linkage, Data In-
tegration, Information Extraction
1 Introduction
Human trafficking is a form of modern slavery where people profit from the
control and exploitation of others, forcing them to engage in commercial sex or
to provide services against their will. The statistics of the problem are shock-
ing. In 2014 the International Labor Organization on The Economics of Forced
Labour7 reported that $99 billion came from commercial sexual exploitation.
Polaris8 reports that in the United States 100,000 children are estimated to be
in the sex trade each year, and that the total number of victims is likely much
larger when estimates of both adults and minors as well as sex trafficking and
labor trafficking are aggregated. Estimates indicate that traffickers control an
average of six victims and derive $150,000 from each victim per year. The sex
trafficking industry is estimated to spend about $30 million on online advertising
each year. These advertisements appear in hundreds of web sites that advertise
escort services, massage parlors, etc. The total number of such advertisements
is unknown, but our database of escort ads crawled from the most popular sites
contains over 50 million ads.
The objective of our work is to create generic technology to enable rapid
construction of knowledge graphs for specific domains together with query, visu-
alization and analysis capabilities that enable end-users to solve complex prob-
lems. The challenge is to exploit all available sources, including web pages, doc-
ument collections, databases, delimited text files, structured data such as XML
or JSON, images, and videos. In this work we developed the technologies and
their application to build a large knowledge graph for the human trafficking do-
main. This demonstration shows how the knowledge graph built using semantic
technologies can be applied to combat human trafficking.
2 A Knowledge Graph for Human Trafficking
Figure 1 shows a screenshot of the DIG query interface. The interface paradigm
is similar to that of popular web sites such as Amazon (amazon.com). Users
can search using keywords, and can filter results by clicking on check-boxes
to constrain the results. For example, the figure shows that the user clicked
on “latina”, so the results are filtered to contain only those with the selected
ethnicity. The user interface issues queries to the ElasticSearch index, responding
to queries over the 1.4 billion node graph in under 2 seconds.
The application of DIG to combat human trafficking is in use today. The
system has currently been deployed to six law enforcement agencies and several
NGOs (non-governmental organization) that are all using the tools in various
7
http://bit.ly/1oa2cR3
8
http://www.polarisproject.org/index.php
Using a Knowledge Graph to Combat Human Trafficking 3
Fig. 1. Screenshot of DIG query interface showing results a query on the keyword
“jessica”, filtered by city/region, ethnicity and date to focus on a small number of ads
ways to fight human trafficking, such as by locating victims or researching or-
ganizations that engaging in human trafficking. The program manager for the
project has also received requests from more than 100 other government agen-
cies that are interested in using the tools produced under the DARPA Memex
program, including DIG. We have had reports that the DIG tool has already
been successfully used to identify several victims of human trafficking, but due
to privacy concerns and the sensitivity of the topic, we have been asked not to
reveal the law enforcement agencies involved or the details of any cases.
All of the data used in the deployed application comes from publicly available
web sites that contain advertisements for services. The knowledge graph statistics
on 30 April 2015 are the following:
– Number of ads: 52 million, new ads per day: 162,000, updated every hour
– Number of objects (RDF subjects): 1.4 billion
– Number of Feature objects: 222 million
– Number of phone numbers: 1.5 million
3 Demonstration
The full paper presented in the In Use track describes the end-to-end methods
for building knowledge graphs from online sources. This demonstration will give
people a sense for how such knowledge graphs can be used for a real-world
4 Szekely, Knoblock et al.
application and show the general query interface that we use for exploring a
knowledge graph.
There are a number of use cases for the human trafficking knowledge graph
and we will demonstrate how the system can be applied to several of these use
cases. First, we will consider the case of a underage runaway that has been
lured into the sex trade and show how the DIG knowledge graph can be used
to locate such victims so they can be rescued. Second, we will consider the case
of a organization involved in human trafficking and show how DIG can be used
to research and find the individuals being controlled by such an organization. In
the case of sex trafficking, such individuals are often moved from city to city, and
DIG allows a law enforcement agency to find and track these individuals. The
DIG knowledge graph allows law enforcement agencies to conduct the needed
research to understand the full extent of the operations of a organization and to
assemble a case to prosecute the individuals involved in those organizations.
4 Discussion
In this demonstration we will show how the knowledge graph built using the
DIG system can be used for combating human trafficking. DIG can pull data
from a combination of web pages and databases, extract, clean and integrate the
data across sources, find similarities among entities, build a graph of all of the
data, and then query the data to solve specific analytical problems.
DIG is not limited to this specific application and has already been ap-
plied to other problems including understanding the research trends in the field
of material science, combating arms trafficking, and identifying patent trolls
(also known as non-practicing entities). The underlying tools and technology
are widely applicable and can be applied to many other applications, such as
creating a knowledge graph of companies to build a accurate competitive land-
scape of companies based on their products and services or a knowledge graph
of cultural heritage data that could be used by art historians. If there is interest,
we can also demonstrate some of these other applications of the technology.
Acknowledgements This research is supported in part by the Defense Ad-
vanced Research Projects Agency (DARPA) and the Air Force Research Labo-
ratory (AFRL) under contract number FA8750-14-C-0240, and in part by the Na-
tional Science Foundation under Grant No. 1117913. Cloud computing resources
were provided in part by Microsoft under a Microsoft Azure for Research Award.
The views and conclusions contained herein are those of the authors and should
not be interpreted as necessarily representing the official policies or endorse-
ments, either expressed or implied, of DARPA, NSF, or the U.S. Government.