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.

TEACHING HIGH SCHOOL STUDENTS THE BEST CHORAL REPERTOIRE FROM THE GREAT COMPOSERS: MASTERWORKS AVAILABLE FOR IMMEDIATE, FREE ACCESS FROM THE CHORAL PUBLIC DOMAIN LIBRARY

Studying the choral works of the great composers of the past is always a worthy endeavor. For those aspiring to create an excellent high school choral program, it is critical to a student's musical foundation and heritage. Choral educators who teach high school are often bombarded with the most recently published new choral works, when they have a trove of excellent pieces right at their fingertips through websites like the Choral Public Domain Library (CPDL), all available at no cost. This project will explore the pedagogical reasons why this canon of public domain choral music should be taught at the high school level. A thorough guide to CPDL and an anthology of 200 works available on CPDL will provide the conductor with resources for programming this music. Though choral music in the public domain is free to all, publishers still publish this music and adhere copyright claims. This can create mistrust of legitimate editions on CPDL; why are they available at no cost when publishers are claiming copyright on similar editions? These issues will be thoroughly discussed in this project. For any given work on CPDL, there may be multiple editions available on the site. Choosing the right edition requires knowledge about basic editorial principles, especially for works written during the Renaissance period. A detailed discussion of these principles will provide the conductor with the tools needed to choose the best edition for his or her ensemble.