We assess the size and quality of each KB, in terms of number of facts and precision. We ran extensive experiments not only for scientific publications, but also for encyclopedic health portals and online communities, creating different KB’s based on different configurations. Our approach can be easily extended to incorporate additional relations and constraints. To our knowledge, this is the first method that uses consistency checking for biomedical relations. We extend previous techniques for pattern-based IE with confidence statistics, and we combine this recall-oriented stage with logical reasoning for consistency constraint checking to achieve high precision.

Using a small number of seed facts for distant supervision of pattern-based extraction, we harvest a huge number of facts in an automated manner without requiring any explicit training.

We address these three limitations by a versatile and scalable approach to automatic KB construction. Third, most prior work on IE has focused on the molecular level or chemogenomics only, like protein-protein interactions or gene-drug relationships, or solely address highly specific topics such as drug effects. Second, for automatic information extraction (IE), the text genre of choice has been scientific publications, neglecting sources like health portals and online communities. First, most biomedical KBs are manually built and curated, and cannot keep up with the rate at which new findings are published. Prior work on KB construction has three major limitations. Biomedical knowledge bases (KB’s) have become important assets in life sciences.

Recalled by Cambria Hebert