Automatic Ontology Learning from Domain-specific Short Unstructured Text Data

Yiming Xu, Dnyanesh Rajpathak, Ian Gibbs, Diego Klabjan


Ontology learning is a critical task in industry, which deals with identifying and extracting concepts reported in text such that these concepts can be used in different tasks, e.g. information retrieval. The problem of ontology learning is non-trivial due to several reasons with a limited amount of prior research work that automatically learns a domain specific ontology from data. In our work, we propose a two-stage classification system to automatically learn an ontology from unstructured text. In our model, the first-stage classifier classifies candidate concepts into relevant and irrelevant concepts and then the second-stage classifier assigns specific classes to the relevant concepts. The proposed system is deployed as a prototype in General Motors and its performance is validated by using complaint and repair verbatim data collected from different data sources. On average, our system shows the F1-score of 0.75, even when data distributions are vastly different.


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