# Text-mining and ontologies: new approaches to knowledge discovery of microbial diversity

arXiv: https://arxiv.org/abs/1805.04107 · HTML: https://mlsanity.com/paper/1805.04107/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2018.

## Built on by (most cited first, 1 of 1)

- [Taec: a Manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature](https://mlsanity.com/paper/2401.07447/) (2024)

## Abstract (arXiv)

Microbiology research has access to a very large amount of public information on the habitats of microorganisms. Many areas of microbiology research uses this information, primarily in biodiversity studies. However the habitat information is expressed in unstructured natural language form, which hinders its exploitation at large-scale. It is very common for similar habitats to be described by different terms, which makes them hard to compare automatically, e.g. intestine and gut. The use of a common reference to standardize these habitat descriptions as claimed by (Ivana et al., 2010) is a necessity. We propose the ontology called OntoBiotope that we have been developing since 2010. The OntoBiotope ontology is in a formal machine-readable representation that enables indexing of information as well as conceptualization and reasoning.