Natural language query handling using extended knowledge provider system

Autor: Atanu Chattopadhyay, Debashis Nandi, Prasenjit Mukherjee, Baisakhi Chakraborty
Rok vydání: 2021
Předmět:
Zdroj: International Journal of Knowledge-based and Intelligent Engineering Systems. 25:1-19
ISSN: 1875-8827
1327-2314
DOI: 10.3233/kes-210049
Popis: Extraction of knowledge data from knowledge database using natural language query is a difficult task. Different types of natural language processing (NLP) techniques have been developed to handle this knowledge data extraction task. This paper proposes an automated query-response model termed Extended Automated Knowledge Provider System (EAKPS) that can manage various types of natural language queries from user. The EAKPS uses combination based technique and it can handle assertive, interrogative, imperative, compound and complex type query sentences. The algorithm of EAKPS generates structure query language (SQL) for each natural language query to extract knowledge data from the knowledge database resident within the EAKPS. Extraction of noun or noun phrases is another issue in natural language query processing. Most of the times, determiner, preposition and conjunction are prefixed to a noun or noun phrase and it is difficult to identify the noun/noun phrase with prefix during query processing. The proposed system is able to identify these prefixes and extract exact noun or noun phrases from natural language queries without any manual intervention.
Databáze: OpenAIRE
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