Grounded Knowledge Bases for Scientific Domains Dana

This thesis is focused on building knowledge bases (KBs) for scientific domains. Specifically, we create structured representations of technical-domain information using unsupervised or semi supervised learning methods. This work is inspired by recent advances in knowledge base construction based on Web text. However, in the technical domains we consider here, we have grounded data about the objects named by text entities. For example, in the software domain, we can consider the implementation of classes in a code repository, and we can observe the way they are being used in programs. In the biomedical realm, biological ontologies define interactions and relations between entities in this domain, and there is experimental information on domain entities such as proteins and genes. The additional resources available in technical domains, present an opportunity for learning, not only how entities are discussed in text, but also what are their real-world properties.