Concept Discovery through Information Extraction in Restaurant Domain
This work addresses the tedious task of manually sorting and classifying domain-related words for knowledge base construction in the restaurant domain, offering a semi-automatic solution.
The paper tackles the problem of concept identification in the restaurant domain by proposing an automated approach using word embeddings, hierarchical clustering, and classification algorithms to build a concept hierarchy and classify unseen words, reducing manual effort.
Concept identification is a crucial step in understanding and building a knowledge base for any particular domain. However, it is not a simple task in very large domains such as restaurants and hotel. In this paper, a novel approach of identifying a concept hierarchy and classifying unseen words into identified concepts related to restaurant domain is presented. Sorting, identifying, classifying of domain-related words manually is tedious and therefore, the proposed process is automated to a great extent. Word embedding, hierarchical clustering, classification algorithms are effectively used to obtain concepts related to the restaurant domain. Further, this approach can also be extended to create a semi-automatic ontology on restaurant domain.