Approach for Semi-automatic Construction of Anti-infective Drug Ontology Based on Entity Linking
This work addresses the need for improved medical ontology construction in NLP for anti-infective drugs, but it is incremental as it builds on existing methods with feature combinations.
The study tackled the problem of low precision and recall in medical synonym resources for NLP by developing a semi-automatic approach to construct an anti-infective drug ontology using entity linking with selected features, achieving a precision of 86.77%, recall of 89.03%, and F1 score of 87.89%.
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.