  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.
