Question answering system is the significant application of Artificial Intelligence. If users ask for information from QA system in the form of natural language query, the syntax and semantics of query is understood by QA system and response is generated. Several existing solutions are proposed for AI-based QA system in E-commerce, Robotics, Autonomous vehicles and IoT-based solutions. However, to the best of our knowledge, very few research work have focused on QA system for NoSQL data stores. NoSQL (Not Only SQL) is data storage technique where Big heterogeneous data is saved by organizations. Researchers have proposed query languages for different types of NoSQL data storage. The limitations of existing solutions is that no standard query language is proposed and different query syntax are available for different data storage. If user asks in the form of natural language query, QA system should map this to respective NoSQL data store using Artificial Intelligence. In this paper, this issue is highlighted and key solutions are provided to resolve this issue. The key finding of our approach is that Question-Answering system interface is used where users can ask in natural language and query is mapped to specific NoSQL data store query using parse tree.
Data generated by social networking sites and real applications are unstructured and it is difficult
for SQL-based system to process data [
‘NoSQL’ term was introduced by Carlo Strozzi [
Several researchers and practitioners have proposed query languages for NoSQL data stores. For example, CQL for Cassandra, UnQL for CouchBase, Cypher for Redis, MongoDB query language etc. The limitation is that syntax is different for these query languages. It is very difficult for data scientist to use multiple data stores due to several types of syntax and rules. There is need for standards of NoSQL query languages. In this paper, this standard is proposed using Artificial Intelligence based natural language query.
Several research works [
CQL 3.2.
The rest of the paper is structured as follows. In Section 2, NoSQL data storage categories are elaborated. Section 3 highlights NoSQL query languages and conversion of plain natural language to NoSQL data store query techniques. Finally, Section 4 concludes the paper with future directions.
In relational database, data can be stored in rows and columns format. Furthermore, entity integrity, referential integrity, joins, keys and normalizations are the essential features of SQL based databases. The difference in SQL database and NoSQL database is in supported data models and query languages [12].
In column-oriented data storage, data is stored column-based instead of row-based. Table’s column reading is faster [13]. The main advantage is that dynamic schema is used in this data storage. There are a lot of sparse entries in row-oriented data stores as null value signifies the values that are unknown. In this data store, unknown values are don’t included in flexible schema. HBase [14], Google BigTable [15] and Cassandra are examples of column-oriented database.In document-based data store, record is stored in document.
Query languages are useful for retrieving data of interest from data storage. Standards are developed for SQL queries and different categories are proposed. There is no standard benchmark for NoSQL queries.
This language is for column-oriented data storage. This language is used for Cassandra. Column is the basic data structure in CQL [16]. It can manage structured and semi- structured data. Key spaces are used as database in CQL.
This language is specifically designed for Couchbase. Unstructured Query Language (UnQL) uses structural recursion to extract information from semi-structured data [17].
Cypher is graph-based language. It is originally implemented for Neo4j [18]. Cypher is well defined query language for processing graph-based databases. Create and select values from node can be easily implemented in Cypher. 3.4.
MongoDB provides JSON based query language [19]. The difference between SQL query and this query language is that collections based queries are used for MongoDB. Several users want to migrate from SQL to NoSQL data stores as NoSQL data stores can handle unstructured data efficiently [24].
Users will just ask for data in plain natural language and further based on AI-based NLP
techniques, query is mapped to specific database query. Natural languages are ambiguous so it is not
easy for synthesizer [
In Figure 1, it is clearly depicted that users need to fetch the record using natural language query. This query is preprocessed to remove stopwords and use stemming and lemmatization. Then NoSQL mapper converts the natural language query into specific NoSQL data store query. It is forwarded to particular NoSQL data store to retrieve values. If data scientist needs to fetch record of details of product ID 101, Query in natural language will be ‘What are the features of product id 101’.
‘What’ is used in natural languages so it is concluded that user wants to select data, so ‘select’ query will be used. ‘feature’ and ‘id’ are used as nouns, so user wants to ask about features. ‘of’ is used as preposition and after this ‘product’ is used as noun, so it is clear that user wants to ask about product id 101. POS tagging will be - ‘What’ and ‘are’ -Verb Phrase, ‘feature’ – Noun, ‘of’ – preposition.
It is depicted in Figure 2 that parse tree is created for query asked in natural language. This parse tree is used to map this query to NoSQL data stores query. ‘S’ as complete sentence is parsed and it derives VP and NP, where VP is combination of ‘what’ and ‘are’ from which it is concluded that ‘select’ has to be used for database query.
It will be further mapped to: (a) SQL: ‘select * from product where id= ‘101’ (b) MongoDB: ‘db.collections.find (‘id=101’) (c) CQL: ‘select * from product where id=101’ (d)Cypher: Match (E: Employee {id: 101}) return E
It clearly validates that natural language query can be mapped to various NoSQL query languages provided stemming, lemmatization, POS Tagging, Named Entity Recognition and semantic analysis is deployed on natural language query.
In this paper, Question Answering system is proposed for mapping natural language query to respective NoSQL data stores. User will ask query to QA system in English like language and using Artificial Intelligence application, query is mapped to NoSQL database using parse tree. This approach will be useful for NoSQL-based organizations as there will be no need to propose QA system for different NoSQL data stores. Our proposed approach will provide comprehensive structure to use any NoSQL database. In this paper, NLP which is significant application of AI is used to propose QA system. In future, different evaluation metrics and deep learning will be used to analyze accuracy of mapping from natural language to NoSQL query language.
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