Probabilistic Models for Scalable Knowledge Graph Construction

In the past decade, systems that extract information from millions of Internet documents have become commonplace. Knowledge graphs -- structured knowledge bases that describe entities, their attributes and the relationships between them -- are a powerful tool for understanding and organizing this vast amount of information. However, a significant obstacle to knowledge graph construction is the unreliability of the extracted information, due to noise and ambiguity in the underlying data or errors made by the extraction system and the complexity of reasoning about the dependencies between these noisy extractions. My dissertation addresses these challenges by exploiting the interdependencies between facts to improve the quality of the knowledge graph in a scalable framework. I introduce a new approach called knowledge graph identification (KGI), which resolves the entities, attributes and relationships in the knowledge graph by incorporating uncertain extractions from multiple sources, entity co-references, and ontological constraints. I define a probability distribution over possible knowledge graphs and infer the most probable knowledge graph using a combination of probabilistic and logical reasoning. Such probabilistic models are frequently dismissed due to scalability concerns, but my implementation of KGI maintains tractable performance on large problems through the use of hinge-loss Markov random fields, which have a convex inference objective. This allows the inference of large knowledge graphs using 4M facts and 20M ground constraints in 2 hours. To further scale the solution, I develop a distributed approach to the KGI problem which runs in parallel across multiple machines, reducing inference time by 90%. Finally, I extend my model to the streaming setting, where a knowledge graph is continuously updated by incorporating newly extracted facts. I devise a general approach for approximately updating inference in convex probabilistic models, and quantify the approximation error by defining and bounding inference regret for online models. Together, my work retains the attractive features of probabilistic models while providing the scalability necessary for large-scale knowledge graph construction. These models have been applied on a number of real-world knowledge graph projects, including the NELL project at Carnegie Mellon and the Google Knowledge Graph.

Measurement and Simulation of Suppression Effects in a Buoyant Turbulent Line Fire

An experimental and numerical study of turbulent fire suppression is presented. For this work, a novel and canonical facility has been developed, featuring a buoyant, turbulent, methane or propane-fueled diffusion flame suppressed via either nitrogen dilution of the oxidizer or application of a fine water mist. Flames are stabilized on a slot burner surrounded by a co-flowing oxidizer, which allows controlled delivery of either suppressant to achieve a range of conditions from complete combustion through partial and total flame quenching. A minimal supply of pure oxygen is optionally applied along the burner to provide a strengthened flame base that resists liftoff extinction and permits the study of substantially weakened turbulent flames. The carefully designed facility features well-characterized inlet and boundary conditions that are especially amenable to numerical simulation. Non-intrusive diagnostics provide detailed measurements of suppression behavior, yielding insight into the governing suppression processes, and aiding the development and validation of advanced suppression models. Diagnostics include oxidizer composition analysis to determine suppression potential, flame imaging to quantify visible flame structure, luminous and radiative emissions measurements to assess sooting propensity and heat losses, and species-based calorimetry to evaluate global heat release and combustion efficiency. The studied flames experience notable suppression effects, including transition in color from bright yellow to dim blue, expansion in flame height and structural intermittency, and reduction in radiative heat emissions. Still, measurements indicate that the combustion efficiency remains close to unity, and only near the extinction limit do the flames experience an abrupt transition from nearly complete combustion to total extinguishment. Measurements are compared with large eddy simulation results obtained using the Fire Dynamics Simulator, an open-source computational fluid dynamics software package. Comparisons of experimental and simulated results are used to evaluate the performance of available models in predicting fire suppression. Simulations in the present configuration highlight the issue of spurious reignition that is permitted by the classical eddy-dissipation concept for modeling turbulent combustion. To address this issue, simple treatments to prevent spurious reignition are developed and implemented. Simulations incorporating these treatments are shown to produce excellent agreement with the experimentally measured data, including the global combustion efficiency.

Estimating Population Trends in American Woodcock (Scolopax Minor) Using Population Reconstruction Models

The American woodcock (Scolopax minor) population index in North America has declined 0.9% a year since 1968 prompting managers to identify priority information and management needs for the species (Sauer et al 2008). Managers identified a need for a population model that better informs on the status of American woodcock populations (Case et al. 2010). Population reconstruction techniques use long-term age-at-harvest data and harvest effort to estimate abundances with error estimates. Four new models were successfully developed using survey data (1999 to 2013). The optimal model estimates sex specific harvest probability for adult females at 0.148 (SE = 0.017) and all other age-sex cohorts at 0.082 (SE = 0.008) for the most current year 2013. The model estimated a yearly survival rate of 0.528 (SE = 0.008). Total abundance ranged from 5,206,000 woodcock in 2007 to 6,075,800 woodcock in 1999. This study represents the first population estimates of woodcock populations.