A combinatorial approach to biological structures and networks in predictive medicine

This work concerns the study of combinatorial models for biological structures and networks as motivated by questions in predictive medicine. Through multiple examples, the power of combinatorial models to simplify problems and facilitate computation is explored. First, continuous time Markov models are used as a model to study the progression of Alzheimer’s disease and identify which variables best predict progression at each stage. Next, RNA secondary structures are modeled by a thermodynamic Gibbs distribution on plane trees. The limiting distribution (as the number of edges in the tree goes to infinity) is studied to gain insight into the limits of the model. Additionally, a Markov chain is developed to sample from the distribution in the finite case, creating a tool for understanding what tree properties emerge from the thermodynamics. Finally, knowledge graphs are used to encode relationships extracted from the biomedical literature, and algorithms for efficient computation on these graphs are explored.Ph.D

Knowledge exploration in public linked data ontologies

The internet is constantly expanding across millions of web pages. Using the internet effectively is a hard skill to learn both for humans and machines. The Semantic Web is an attempt to standardize the data across the web making it accessible and usable. This thesis is meant as a practical guide to using knowledge graphs the main building blocks of the Semantic Web. A knowledge graph is a large data source with facts about millions of real and fictional entities. Wikidata and DBpedia are two publicly available knowledge graphs that we use to look at three major aspects: Understanding knowledge graphs, finding relevant information from knowledge graphs, and creating features from knowledge graphs. These three aspects will be explained by using the task of finding similar entities,specifically similar artists

PLATFORMIZING KNOWLEDGE: MESS AND MEANING IN WEB 3.0 INFRASTRUCTURES

This panel focuses on the way that platforms have become key players in the representation of knowledge. Recently, there have been calls to combine infrastructure and platform-based frameworks to understand the nature of information exchange on the web through digital tools for knowledge sharing. The present panel builds and extends work on platform and infrastructure studies in what has been referred to as “knowledge as programmable object” (Plantin, et al., 2018), specifically focusing on how metadata and semantic information are shaped and exchanged in specific web contexts. As Bucher (2012; 2013) and Helmond (2015) show, data portability in the context of web platforms requires a certain level of semantic annotation. Semantic interoperability is the defining feature of so-called "Web 3.0"—traditionally referred to as the semantic web (Antoniou et al, 2012; Szeredi et al, 2014). Since its inception, the semantic web has privileged the status of metadata for providing the fine-grained levels of contextual expressivity needed for machine-readable web data, and can be found in products as diverse as Google's Knowledge Graph, online research repositories like Figshare, and other sources that engage in platformizing knowledge. The first paper in this panel examines the international Schema.org collaboration. The second paper investigates the epistemological implications when platforms organize data sharing. The third paper argues for the use of patents to inform research methodologies for understanding knowledge graphs. The fourth paper discusses private platforms’ extraction and collection of user metadata and the enclosure of data access