Ontology Learning Using Word Net Lexical Expansion and Text Mining

Exploring Binary Relations for Ontology Extension and Improved Adaptation to Clinical Text

10.1101/2020.12.04.411751 ◽

2020 ◽

Author(s):

Luke T Slater ◽

Robert Hoehndorf ◽

Andreas Karwath ◽

Georgios V Gkoutos

Keyword(s):

Text Mining ◽

Semantic Analysis ◽

Classification Performance ◽

Human Interaction ◽

Semantic Features ◽

Ontology Learning ◽

Disease Ontology ◽

Text Corpora ◽

Clinical Narrative ◽

Improved Performance

AbstractBackgroundThe controlled domain vocabularies provided by ontologies make them an indispensable tool for text mining. Ontologies also include semantic features in the form of taxonomy and axioms, which make annotated entities in text corpora useful for semantic analysis. Extending those semantic features may improve performance for characterisation and analytic tasks. Ontology learning techniques have previously been explored for novel ontology construction from text, though most recent approaches have focused on literature, with applications in information retrieval or human interaction tasks. We hypothesise that extension of existing ontologies using information mined from clinical narrative text may help to adapt those ontologies such that they better characterise those texts, and lead to improved classification performance.ResultsWe develop and present a framework for identifying new classes in text corpora, which can be integrated into existing ontology hierarchies. To do this, we employ the Stanford Open Information Extraction algorithm and integrate its implementation into the Komenti semantic text mining framework. To identify whether our approach leads to better characterisation of text, we present a case study, using the method to learn an adaptation to the Disease Ontology using text associated with a sample of 1,000 patient visits from the MIMIC-III critical care database. We use the adapted ontology to annotate and classify shared first diagnosis on patient visits with semantic similarity, revealing an improved performance over use of the base Disease Ontology on the set of visits the ontology was constructed from. Moreover, we show that the adapted ontology also improved performance for the same task over two additional unseen samples of 1,000 and 2,500 patient visits.ConclusionsWe report a promising new method for ontology learning and extension from text. We demonstrate that we can successfully use the method to adapt an existing ontology to a textual dataset, improving its ability to characterise the dataset, and leading to improved analytic performance, even on unseen portions of the dataset.

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