# Topical Classification of Food Safety Publications with a Knowledge Base

arXiv: https://arxiv.org/abs/2201.00374 · HTML: https://mlsanity.com/paper/2201.00374/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 2 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles](https://mlsanity.com/paper/2104.00948/) (2021)
- [Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models](https://mlsanity.com/paper/1910.01274/) (2019)
- [Medical Knowledge-enriched Textual Entailment Framework](https://mlsanity.com/paper/2011.05257/) (2020)
- [Foundations of an Alternative Approach to Reification in RDF](https://mlsanity.com/paper/1406.3399/) (2014)

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

- [Ontology Reuse: the Real Test of Ontological Design](https://mlsanity.com/paper/2205.02892/) (2022)

## Abstract (arXiv)

The vast body of scientific publications presents an increasing challenge of finding those that are relevant to a given research question, and making informed decisions on their basis. This becomes extremely difficult without the use of automated tools. Here, one possible area for improvement is automatic classification of publication abstracts according to their topic. This work introduces a novel, knowledge base-oriented publication classifier. The proposed method focuses on achieving scalability and easy adaptability to other domains. Classification speed and accuracy are shown to be satisfactory, in the very demanding field of food safety. Further development and evaluation of the method is needed, as the proposed approach shows much potential.