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.

Multiraciality Enters the University: Mixed Race Identity and Knowledge Production in Higher Education

“Multiraciality Enters the University: Mixed Race Identity and Knowledge Production in Higher Education,” explores how the category of “mixed race” has underpinned university politics in California, through student organizing, admissions debates, and the development of a new field of study. By treating the concept of privatization as central to both multiraciality and the neoliberal university, this project asks how and in what capacity has the discourses of multiracialism and the growing recognition of mixed race student populations shaped administrative, social, and academic debates at the state’s flagship universities—the University of California at Berkeley and Los Angeles. This project argues that the mixed race population symbolizing so-called “post-racial societies” is fundamentally attached to the concept of self-authorship, which can work to challenge the rights and resources for college students of color. Through a close reading of texts, including archival materials, policy and media debates, and interviews, I assert that the contemporary deployment of mixed race within the US academy represents a particularly post-civil rights development, undergirded by a genealogy of U.S. liberal individualism. This project ultimately reveals the pressing need to rethink ways to disrupt institutionalized racism in the new millennium.