Constructing biological knowledge bases by extracting information from text sources.
Recently, there has been much effort in making databases for molecular biology more accessible and interoperable. However, information in text form, such as MEDLINE records, remains a greatly underutilized source of biological information. We have begun a research effort aimed at automatically mapping information from text sources into structured representations, such as knowledge bases. Our approach to this task is to use machine-learning methods to induce routines for extracting facts from text. We describe two learning methods that we have applied to this task--a statistical text classification method, and a relational learning method--and our initial experiments in learning such information-extraction routines. We also present an approach to decreasing the cost of learning information-extraction routines by learning from "weakly" labeled training data.
Craven
M
M
School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3891, USA. mark.craven@cs.cmu.edu
Kumlien
J
J
D001185:Artificial Intelligence; D001331:Automation; D001499:Bayes Theorem; D016208:Databases, Factual; D016239:MEDLINE; D015233:Models, Statistical; D011642:Publications; D012984:Software