Identification of research hypotheses and new knowledge from scientific literature

Matthew Shardlow,Riza Batista-Navarro,Raheel Nawaz,Paul Thompson,John Mcnaught,Sophia Ananiadou

doi:10.1186/s12911-018-0639-1

Matthew Shardlow, Riza Batista-Navarro + Show 4 more

Open Access

https://doi.org/10.1186/s12911-018-0639-1

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Abstract

BackgroundText mining (TM) methods have been used extensively to extract relations and events from the literature. In addition, TM techniques have been used to extract various types or dimensions of interpretative information, known as Meta-Knowledge (MK), from the context of relations and events, e.g. negation, speculation, certainty and knowledge type. However, most existing methods have focussed on the extraction of individual dimensions of MK, without investigating how they can be combined to obtain even richer contextual information. In this paper, we describe a novel, supervised method to extract new MK dimensions that encode Research Hypotheses (an author’s intended knowledge gain) and New Knowledge (an author’s findings). The method incorporates various features, including a combination of simple MK dimensions.MethodsWe identify previously explored dimensions and then use a random forest to combine these with linguistic features into a classification model. To facilitate evaluation of the model, we have enriched two existing corpora annotated with relations and events, i.e., a subset of the GENIA-MK corpus and the EU-ADR corpus, by adding attributes to encode whether each relation or event corresponds to Research Hypothesis or New Knowledge. In the GENIA-MK corpus, these new attributes complement simpler MK dimensions that had previously been annotated.ResultsWe show that our approach is able to assign different types of MK dimensions to relations and events with a high degree of accuracy. Firstly, our method is able to improve upon the previously reported state of the art performance for an existing dimension, i.e., Knowledge Type. Secondly, we also demonstrate high F1-score in predicting the new dimensions of Research Hypothesis (GENIA: 0.914, EU-ADR 0.802) and New Knowledge (GENIA: 0.829, EU-ADR 0.836).ConclusionWe have presented a novel approach for predicting New Knowledge and Research Hypothesis, which combines simple MK dimensions to achieve high F1-scores. The extraction of such information is valuable for a number of practical TM applications.

Highlights

Text mining (TM) methods have been used extensively to extract relations and events from the literature
Our experiments to predict the correct values for the Knowledge Type dimension were carried out only using the events in the GENIA-MK corpus, given that Knowledge Type is only annotated in this corpus and not in EU-Adverse Drug Reaction (ADR)
We have presented a novel application of text mining techniques for the discovery of Research Hypotheses and New Knowledge at the level of events and relations

Summary

Introduction

Text mining (TM) methods have been used extensively to extract relations and events from the literature. TM techniques have been used to extract various types or dimensions of interpretative information, known as Meta-Knowledge (MK), from the context of relations and events , e.g. negation, speculation, certainty and knowledge type. Most existing methods have focussed on the extraction of individual dimensions of MK, without investigating how they can be combined to obtain even richer contextual information. We describe a novel, supervised method to extract new MK dimensions that encode Research Hypotheses (an author’s intended knowledge gain) and New Knowledge (an author’s findings). Sentence (3) comes from a paper published 10 years later [2], by which time the association is presented as widely accepted knowledge, presumably on the basis of many further positive experimental results

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