Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data
This work addresses the challenge of ontology learning for industry applications, such as information retrieval, but appears incremental as it builds on classification methods without major paradigm shifts.
The authors tackled the problem of automatically learning domain-specific ontologies from unstructured short text data, proposing a two-stage classification system that was deployed as a prototype in the automotive industry and validated on complaint and repair verbatim data.
Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with 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 data. We first collect candidate concepts, which are classified into concepts and irrelevant collocates by our first classifier. The concepts from the first classifier are further classified by the second classifier into different concept types. The proposed system is deployed as a prototype at a company and its performance is validated by using complaint and repair verbatim data collected in automotive industry from different data sources.