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Conditions are essential in the statements of biological literature. Withoutthe conditions (e.g., environment, equipment) that were precisely specified,the facts (e.g., observations) in the statements may no longer be valid. Onebiological statement has one or multiple fact(s) and/or condition(s). Theirsubject and object can be either a concept or a concept's attribute. Existinginformation extraction methods do not consider the role of condition in thebiological statement nor the role of attribute in the subject/object. In thiswork, we design a new tag schema and propose a deep sequence tagging frameworkto structure conditional statement into fact and condition tuples frombiological text. Experiments demonstrate that our method yields ainformation-lossless structure of the literature.

Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements