Genomics Inform.  2020 Jun;18(2):e20. 10.5808/GI.2020.18.2.e20.

Improving accessibility and distinction between negative results in biomedical relation extraction

• Sousa D
• Lamurias A
• Couto FM

• KMID: 2503645
• DOI: http://doi.org/10.5808/GI.2020.18.2.e20

Abstract

Accessible negative results are relevant for researchers and clinicians not only to limit their search space but also to prevent the costly re-exploration of research hypotheses. However, most biomedical relation extraction datasets do not seek to distinguish between a false and a negative relation among two biomedical entities. Furthermore, datasets created using distant supervision techniques also have some false negative relations that constitute undocumented/unknown relations (missing from a knowledge base). We propose to improve the distinction between these concepts, by revising a subset of the relations marked as false on the phenotype-gene relations corpus and give the first steps to automatically distinguish between the false (F), negative (N), and unknown (U) results. Our work resulted in a sample of 127 manually annotated FNU relations and a weighted-F1 of 0.5609 for their automatic distinction. This work was developed during the 6th Biomedical Linked Annotation Hackathon (BLAH6).

Keyword

biomedical research; knowledge base; negative results; relation extraction