Approach for Semi-automatic Construction of Anti-infective Drug Ontology Based on Entity Linking

TL;DR

This paper presents a semi-automatic method for constructing an anti-infective drug ontology using entity linking and NLP techniques, achieving high precision and recall in synonym extraction.

Contribution

It introduces a novel NLP-based approach combining semantic relation analysis and feature selection for effective ontology construction.

Findings

Achieved 86.77% precision in synonym extraction

Attained 89.03% recall rate

F1 score of 87.89% in entity linking

Abstract

Ontology can be used for the interpretation of natural language. To construct an anti-infective drug ontology, one needs to design and deploy a methodological step to carry out the entity discovery and linking. Medical synonym resources have been an important part of medical natural language processing (NLP). However, there are problems such as low precision and low recall rate. In this study, an NLP approach is adopted to generate candidate entities. Open ontology is analyzed to extract semantic relations. Six-word vector features and word-level features are selected to perform the entity linking. The extraction results of synonyms with a single feature and different combinations of features are studied. Experiments show that our selected features have achieved a precision rate of 86.77%, a recall rate of 89.03% and an F1 score of 87.89%. This paper finally presents the structure of the proposed ontology and its relevant statistical data.