Topical Classification of Food Safety Publications with a Knowledge Base

TL;DR

This paper presents a scalable, knowledge base-oriented classifier for automatically categorizing food safety publications, demonstrating promising speed and accuracy, with potential for broader domain adaptation.

Contribution

The work introduces a novel classification method that leverages a knowledge base, enhancing scalability and adaptability in the domain of food safety publications.

Findings

Classification speed is satisfactory.

Accuracy is competitive in food safety domain.

Method shows potential for broader application.

Abstract

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.