The Meta-knowledge of Causality in Biomedical Scientific Discourse

Abstract

Causality lies at the heart of biomedical knowledge, being involved in diagnosis, pathology or systems biology. Thus, automatic causality recognition can greatly reduce the human workload by suggesting possible causal connections and aiding in the curation of pathway models. For this, we rely on corpora that are annotated with classified, structured representations of important facts and findings contained within text. However, it is impossible to correctly interpret these annotations without additional information, e.g., classification of an event as fact, hypothesis, experimental result or analysis of results, confidence of authors about the validity of their analyses etc. In this study, we analyse and automatically detect this type of information, collectively termed meta-knowledge (MK), in the context of existing discourse causality annotations. Our effort proves the feasibility of identifying such pieces of information, without which the understanding of causal relations is limited.

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