Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements

TL;DR

This paper introduces a deep sequence tagging framework that accurately extracts fact and condition tuples from biological literature, preserving information losslessly and considering attributes' roles, which previous methods overlooked.

Contribution

A novel tag schema and deep learning approach for structuring biological statements into fact and condition tuples, accounting for attributes and conditions.

Findings

Achieves lossless information extraction from biological texts.

Outperforms existing methods in structuring conditional statements.

Effectively incorporates attributes and conditions into knowledge graphs.

Abstract

Conditions are essential in the statements of biological literature. Without the conditions (e.g., environment, equipment) that were precisely specified, the facts (e.g., observations) in the statements may no longer be valid. One biological statement has one or multiple fact(s) and/or condition(s). Their subject and object can be either a concept or a concept's attribute. Existing information extraction methods do not consider the role of condition in the biological statement nor the role of attribute in the subject/object. In this work, we design a new tag schema and propose a deep sequence tagging framework to structure conditional statement into fact and condition tuples from biological text. Experiments demonstrate that our method yields a information-lossless structure of the literature.