Using Contexts and Constraints for Improved Geotagging of Human Trafficking Webpages

Extracting geographical tags from webpages is a well-motivated application in many domains. In illicit domains with unusual language models, like human trafficking, extracting geotags with both high precision and recall is a challenging problem. In this paper, we describe a geotag extraction framework in which context, constraints and the openly available Geonames knowledge base work in tandem in an Integer Linear Programming (ILP) model to achieve good performance. In preliminary empirical investigations, the framework improves precision by 28.57% and F-measure by 36.9% on a difficult human trafficking geotagging task compared to a machine learning-based baseline. The method is already being integrated into an existing knowledge base construction system widely used by US law enforcement agencies to combat human trafficking.Comment: 6 pages, GeoRich 2017 workshop at ACM SIGMOD conferenc

Scalable Generation of Type Embeddings Using the ABox

Structured knowledge bases gain their expressive power from both the ABox and TBox. While the ABox is rich in data, the TBox contains the ontological assertions that are often necessary for logical inference. The crucial links between the ABox and the TBox are served by is-a statements (formally a part of the ABox) that connect instances to types, also referred to as classes or concepts. Latent space embedding algorithms, such as RDF2Vec and TransE, have been used to great effect to model instances in the ABox. Such algorithms work well on large-scale knowledge bases like DBpedia and Geonames, as they are robust to noise and are low-dimensional and real-valued. In this paper, we investigate a supervised algorithm for deriving type embeddings in the same latent space as a given set of entity embeddings. We show that our algorithm generalizes to hundreds of types, and via incremental execution, achieves near-linear scaling on graphs with millions of instances and facts. We also present a theoretical foundation for our proposed model, and the means of validating the model. The empirical utility of the embeddings is illustrated on five partitions of the English DBpedia ABox. We use visualization and clustering to show that our embeddings are in good agreement with the manually curated TBox. We also use the embeddings to perform a soft clustering on 4 million DBpedia instances in terms of the 415 types explicitly participating in is-a relationships in the DBpedia ABox. Lastly, we present a set of results obtained by using the embeddings to recommend types for untyped instances. Our method is shown to outperform another feature-agnostic baseline while achieving 15x speedup without any growth in memory usage

Declarative models of presentation

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in IUI '97 Proceedings of the 2nd international conference on Intelligent user interfaces, http://dx.doi.org/10.1145/238218.238315.Current interface development tools cannot be used to specify complex displays without resorting to programming using a toolkit or graphics package. Interface builders and multi-media authoring tools only support the construction of static displays where the components of the display are known at design time (e.g., buttons, menus). This paper describes a presentation modeling system where complex displays of dynamically changing data can be modeled declaratively. The system incorporates principles of graphic design such as guides and grids, supports constraint-based layout and automatic update when data changes, has facilities for easily specifying the layout of collections of data, and has facilities for making displays sensitive to the characteristics of the data being presented and the presentation context (e.g., amount of space available). Finally, the models are designed to be amenable to interactive specification and specification using demonstrational techniques

Customized web-based data presentation

This is an electronic version of the paper presented at the World Conference on the WWW and Internet & Intranet (WebNet'98) held in Orlando, FL (United States) on 1998Reprinted from the WebNet 98 : World Conference of the WWW, Internet, & Intranet with permission of AACE (http://www.aace.org).This paper presents a language for specifying the presentation of data in Web pages. The language is an extension of HTML that includes constructs for specifying how to present one or more instances of a given class of data, and constructs for tailoring the presentation to the features of the data, to information in user profiles and to the capabilities of the user s platform. We describe the architecture of the system, the features of the page specification language, and present examples of generated pages

Automatic generation of help from interface design models

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CHI '94, http://dx.doi.org/10.1145/191666.191751Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsModel-based interface design can save substantial effort in building help systems for interactive applications by generating help automatically from the model used to implement the interface, and by providing a framework for developers to easily refine the automatically-generated help texts. This paper describes a system that generates hypertext-based help about data presented in application displays, commands to manipulate data, and interaction techniques to invoke commands. The refinement component provides several levels of customization, including programming-by-example techniques to let developers edit directly help windows that the system produces, and the possibility to refine help generation rulesRoberto Moriyon is supported by a grant from the Spanish Ministry of Education and Science. Pedro Szekely and Robert Neches are supported by ARPA through Contract Numbers NCC 2-719 and NO0174-91-0014