Assigning factuality values to semantic relations extracted from biomedical research literature

Biomedical knowledge claims are often expressed as hypotheses, speculations, or opinions, rather than explicit facts (propositions). Much biomedical text mining has focused on extracting propositions from biomedical literature. One such system is SemRep, which extracts propositional content in the f...

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Bibliographic Details
Published in:PloS one 2017-07, Vol.12 (7), p.e0179926-e0179926
Main Authors: Kilicoglu, Halil, Rosemblat, Graciela, Rindflesch, Thomas C
Format: Article
Language:English
Subjects:
Annotations
Artificial intelligence
Associations
Bioinformatics
Biomedical Research
Breast cancer
Composition effects
Computer and Information Sciences
Data Mining
Disease
Feasibility studies
Humans
Hypotheses
Knowledge
Language
Learning algorithms
Linguistics
Machine Learning
Management
Medical journals
Medical research
Medicine
Medicine and Health Sciences
Methods
National libraries
Natural Language Processing
Search engine optimization
Semantic relations
Semantics
Social Sciences
Texts
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Summary:Biomedical knowledge claims are often expressed as hypotheses, speculations, or opinions, rather than explicit facts (propositions). Much biomedical text mining has focused on extracting propositions from biomedical literature. One such system is SemRep, which extracts propositional content in the form of subject-predicate-object triples called predications. In this study, we investigated the feasibility of assessing the factuality level of SemRep predications to provide more nuanced distinctions between predications for downstream applications. We annotated semantic predications extracted from 500 PubMed abstracts with seven factuality values (fact, probable, possible, doubtful, counterfact, uncommitted, and conditional). We extended a rule-based, compositional approach that uses lexical and syntactic information to predict factuality levels. We compared this approach to a supervised machine learning method that uses a rich feature set based on the annotated corpus. Our results indicate that the compositional approach is more effective than the machine learning method in recognizing the factuality values of predications. The annotated corpus as well as the source code and binaries for factuality assignment are publicly available. We will also incorporate the results of the better performing compositional approach into SemMedDB, a PubMed-scale repository of semantic predications extracted using SemRep.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0179926