Concept Discovery through Information Extraction in Restaurant Domain

TL;DR

This paper presents an automated method for concept discovery in the restaurant domain using word embeddings, clustering, and classification to build a hierarchical knowledge base and facilitate semi-automatic ontology creation.

Contribution

It introduces a novel automated approach combining word embedding, clustering, and classification for concept identification in large domains like restaurants.

Findings

Effective concept hierarchy generation

Automated classification of unseen words

Potential for semi-automatic ontology creation

Abstract

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.