title: Constructing biological knowledge bases by extracting information from text sources.
abstract: 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.
authors.affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3891, USA. mark.craven@cs.cmu.edu
authors.forename: M
authors.initials: M
authors.lastname: Craven
authors.forename: J
authors.initials: J
authors.lastname: Kumlien
mesh_terms: D001185:Artificial Intelligence; D001331:Automation; D001499:Bayes Theorem; D016208:Databases, Factual; D016239:MEDLINE; D015233:Models, Statistical; D011642:Publications; D012984:Software