Andrew Philpot

The Ariadne approach to Web- based information integration

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to do this is to build specialized applications, which are time-consuming to develop and difficult to maintain. We have addressed this problem by creating the technology and tools for rapidly constructing information agents that extract, query, and integrate data from web sources. Our approach is based on a uniform representation that makes it simple and efficient to integrate multiple sources. Instead of building specialized algorithms for handling web sources, we have developed methods for mapping web sources into this uniform representation. This approach builds on work from knowledge representation, databases, machine learning and automated planning. The resulting system, called Ariadne, makes it fast and easy to build new information agents that access existing web sources. Ariadne also makes it easy to maintain these agents and incorporate new sources as they become available.

Ariadne: a system for constructing mediators for Internet sources

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to achieve this integration is by building specialized applications, which are time-consuming to develop and difficult to maintain. We are addressing this problem by creating the technology and tools for rapidly constructing information mediators that extract, query, and integrate data from web sources. The resulting system, called Ariadne, makes it feasible to rapidly build information mediators that access existing web sources.

Ontology and the Lexicon: The Omega ontology

We present the Omega ontology, a large terminological ontology obtained by remerging WordNet and Mikrokosmos, adding information from various other sources, and subordinating the result to a newly designed feature-oriented upper model. We explain the organizing principles of the representation used for Omega and discuss the methodology used to merge the constituent conceptual hierarchies. We survey a range of auxiliary knowledge sources (including instances, verb frame annotations, and domainspecific sub-ontologies) incorporated into the basic conceptual structure and applications that have benefited from Omega. Omega is available for browsing at http://omega.isi.edu/.

Simplifying data access: the Energy Data Collection project

Using technology developed at the Digital Government Research Center, a team of researchers is seeking to make government statistical data more accessible through the Internet. In collaboration with government experts, they are conducting research into advanced information systems, developing standards, interfaces and a shared infrastructure, and building and managing pilot systems.

Data Integration and Access

This chapter describes the progress of the Digital Government Research Center in tackling the challenges of integrating and accessing the massive amount of statistical and text data available from government agencies. In particular, we address the issues of database heterogeneity, size, distribution, and control of terminology. In this chapter we provide an overview of our results in addressing problems such as (1) ontological mappings for terminology standardization, (2) data integration across data bases with high speed query processing, and (3) interfaces for query input and presentation of results. The DGRC is a collaboration between researchers from Columbia University and the Information Sciences Institute of the University of Southern California employing technology developed at both locations, in particular the SENSUS ontology, the SIMS multi-database access planner, the LEXING automated dictionary and terminology analysis system, the main-memory query processing component and others. The pilot application targets gasoline data from the Bureau of Labor Statistics, the Energy Information Administration of the Department of Energy, the Census Bureau, and other government agencies.

Compiling Source Descriptions for Efficient and Flexible Information Integration

Integrating data from heterogeneous data sources is a critical problem that has received a great deal of attention in recent years. There are two competing approaches to address this problem. The traditional approach, which first appeared in Multibase and more recently in HERMES and TSIMMIS, often called global-as-view, defines the global model as a view on the sources. A more recent approach, sometimes referred to as local-as-view or view rewriting, defines the sources as views on the global model. The disadvantage of the first approach is that a person must re-engineer the definitions of the global model whenever any of the sources change or when new sources are added. The view rewriting approach does not suffer from this drawback, but the problem of rewriting queries into equivalent plans using views is computationally hard and must be performed for each query at run-time.In this paper we propose a hybrid approach that amortizes the cost of query processing over all queries by pre-compiling the source descriptions into a minimal set of integration axioms. Using this approach, the sources are defined in terms of the global model and then compiled into axioms that define the global model in terms of the sources. These axioms can be efficiently instantiated at run-time to determine the most appropriate rewriting to answer a query and facilitate traditional cost-based query optimization. Our approach combines the flexibility of the local-as-view approach with the run-time efficiency of the query processing in global-as-view systems. We have implemented this approach for the SIMS and Ariadne information mediators and provide empirical results that demonstrate that in practice the approach scales to large numbers of sources and that the approach can compile the axioms for a variety of real-world domains in a matter of seconds.

Building and Using a Knowledge Graph to Combat Human Trafficking

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 paper we present an approach to 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.

Large-scale multimedia content analysis using scientific workflows

Analyzing web content, particularly multimedia content, for security applications is of great interest. However, it often requires deep expertise in data analytics that is not always accessible to non-experts. Our approach is to use scientific workflows that capture expert-level methods to examine web content. We use workflows to analyze the image and text components of multimedia web posts separately, as well as by a multimodal fusion of both image and text data. In particular, we re-purpose workflow fragments to do the multimedia analysis and create additional components for the fusion of the image and text modalities. In this paper, we present preliminary work which focuses on a Human Trafficking Detection task to help deter human trafficking of minors by thus fusing image and text content from the web. We also examine how workflow fragments save time and effort in multimedia content analysis while bringing together multiple areas of machine learning and computer vision. We further export these workflow fragments using linked data as web objects.

Matching and integration across heterogeneous data sources

A sea of undifferentiated information is forming from the body of data that is collected by people and organizations, across government, for different purposes, at different times, and using different methodologies. The resulting massive data heterogeneity requires automatic methods for data alignment, matching and/or merging. In this poster, we describe two systems, Guspin™ and Sift™, for automatically identifying equivalence classes and for aligning data across databases. Our technology, based on principles of information theory, measures the relative importance of data, leveraging them to quantify the similarity between entities. These systems have been applied to solve real problems faced by the Environmental Protection Agency and its counterparts at the state and local government level.

Data integration from open internet sources and network detection to combat underage sex trafficking

Victims of sex trafficking are compelled into sexual exploitation by the use of coercion, deception or fraud, and often find themselves forced into a situation of imprisonment and slavery. This form of human trafficking is a serious felony and law enforcement agents have active ongoing work to combat such crimes, with especial focus on under-age sexual trafficking. Sexual exploitation from traffickers can be often masked as services such as classified ads and escort and massage services (EMS) on the Internet. In this paper, we describe the prototype of a law enforcement support system that aims to make daily extractions of online data, filter relevant information, find hidden patterns and display relevant leads to law enforcement agents. The system uses information retrieval and integration, natural language processing, image analysis, and data linking techniques to allow various forms of relevant information visualization for supporting the combat of sex trafficking. It has been used by law enforcement agencies on specific occasions, and is being developed to suit certain operational needs.