This chapter discusses joint learning research in biomedical domains. A brief review of the field of joint learning research is given, with emphases on the large-scale data and knowledge resources used for learning and the central biological questions involved. Two representative joint learning case studies are presented with algorithmic details. The two case studies involved two representative...

Methods for discovering causal knowledge from observational data have been a persistent topic of AI research for several decades. Essentially all of this work focuses on knowledge representations for propositional domains. In this paper, we present several key algorithmic and theoretical innovations that extend causal discovery to relational domains. We provide strong evidence that effective le...

Chapter Overview This chapter discusses joint learning research in biomedical domains. A brief review of the field of joint learning research is given, with emphases on the large-scale data and knowledge resources used for learning and the central biological questions involved. Two representative joint learning case studies are presented with algorithmic details. The two case studies involved t...

We consider an algorithmic approach for revising inconsistent data and restoring its consistency. This approach detects the \spoiled" part of the data (i.e., the set of assertions that cause inconsistency), deletes it from the knowledge-base, and then draws classical conclusions from the \recovered" information. The essence of this approach is its coherence with the original (possibly inconsist...