Processing of natural language queries to a relational database (original) (raw)

From Natural Language to Databases via Ontologies

LREC06. 5th Edition of the International …, 2006

This paper describes an approach to Natural Language access to databases based on ontologies. Their role is to make the central part of the translation process independent both of the specific language and of the particular database schema. The input sentence is parsed and the parse tree is semantically annotated via references to the ontology describing the application. This first step is, of course, language dependent: the parsing process depends on the syntax of the language and the annotation depends on the meaning of words, exp ressed as links between words and concepts in the ontology. Then, the annotated tree is used to produce an "ontological query", i.e. a query expressed in terms of paths on the ontology. This second step is entirely language-and DB-independent. Finally, the ontological query is translated into a standard SQL query, on the basis of a concept-to-DB mapping, specifying how each concept and relation is mapped onto the database.

A New Feasible Approach to Natural Language Database Query

2005

A truly natural language interface to databases also needs to be practical for actual implementation. We developed a new feasible approach to solve the problem and tested it successfully in a laboratory environment. The new result is based on metadata search, where the metadata grow in largely linear manner and the search is linguistics-free (allowing for grammatically incorrect and incomplete input). A new class of reference dictionary integrates four types of enterprise metadata: enterprise information models, database values, user-words, and query cases. The layered and scalable information models allow user-words to stay in original forms as users articulated them, as opposed to relying on permutations of individual words contained in the original query. A graphical representation method turns the dictionary into searchable graphs representing all possible interpretations of the input. A branch-and-bound algorithm then identifies optimal interpretations, which lead to SQL implementation of the original queries. Query cases enhance both the metadata and the search of metadata, as well as providing casebased reasoning to directly answer the queries. This design assures feasible solutions at the termination of the search, even when the search is incomplete (i.e., the results contain the correct answer to the original query). The necessary condition is that the text input contains at least one entry in the reference dictionary. The sufficient condition is that the text input contains a set of entries corresponding to a complete, correct single SQL query. Laboratory testing shows that the system obtained accurate results for most cases that satisfied only the necessary condition.

An algorithm to transform natural language into SQL queries for relational databases

Intelligent interface, to enhance efficient interactions between user and databases, is the need of the database applications. Databases must be intelligent enough to make the accessibility faster. However, not every user familiar with the Structured Query Language (SQL) queries as they may not aware of structure of the database and they thus require to learn SQL. So, non-expert users need a system to interact with relational databases in their natural language such as English. For this, Database Management System (DBMS) must have an ability to understand Natural Language (NL). In this research, an intelligent interface is developed using semantic matching technique which translates natural language query to SQL using set of production rules and data dictionary. The data dictionary consists of semantics sets for relations and attributes. A series of steps like lower case conversion, tokenization, speech tagging, database element and SQL element extraction is used to convert Natural Language Query (NLQ) to SQL Query. The transformed query is executed and the results are obtained by the user. Intelligent Interface is the need of database applications to enhance efficient interaction between user and DBMS.

Frameworks for Querying Databases Using Natural Language: A Literature Review

2019

A Natural Language Interface (NLI) facilitates users to pose queries to retrieve information from a database without using any artificial language such as the Structured Query Language (SQL). Several applications in various domains including healthcare, customer support and search engines, require elaborating structured data having information on text. Moreover, many issues have been explored including configuration complexity, processing of intensive algorithms, and popularity of relational databases, due to which translating natural language to database query has become a secondary area of investigation. The emerging trend of querying systems and speech-enabled interfaces revived natural language to database queries research area., The last survey published on this topic was six years ago in 2013. To best of our knowledge, there is no recent study found which discusses the current state of the art translations frameworks for natural language for structured and non-structured query...

FREyA: An interactive way of querying Linked Data using natural language

2012

Natural Language Interfaces are increasingly relevant for information systems fronting rich structured data stores such as RDF and OWL repositories, mainly because of the conception of them being intuitive for human. In the previous work, we developed FREyA, an interactive Natural Language Interface for querying ontologies. It uses syntactic parsing in combination with the ontology-based lookup in order to interpret the question, and involves the user if necessary.

Natural Language Processing For Querying Relational Databases.

Automatically mapping natural language into programming language semantics has always been a major and interesting challenge. Furthermore, as now almost all IT applications are storing and retrieving information from database. Thus retrieving information form the database requires knowledge of technical languages such as Structured Query Language. Moreover most of the users who interact with databases has no knowledge or are not form any technical environment. This has led us to develop the Natural Language Interface for Database where a user from any background is able to query his/her information using natural language. Asking question to databases to in natural Language is very convenient and easy approach of data access from user points of view. Therefore we are developing a Natural Language Interface for Database which will take the query in natural language and automatically map the NL sentence to respective query and show results.

A Domain Independent Natural Language Interface to Databases Capable of Processing Complex Queries

Lecture Notes in Computer Science, 2005

We present a method for creating natural language interfaces to databases (NLIDB) that allow for translating natural language queries into SQL. The method is domain independent, i.e., it avoids the tedious process of configuring the NLIDB for a given domain. We automatically generate the domain dictionary for query translation using semantic metadata of the database. Our semantic representation of a query is a graph including information from database metadata. The query is translated taking into account the parts of speech of its words (obtained with some linguistic processing). Specifically, unlike most existing NLIDBs, we take seriously auxiliary words (prepositions and conjunctions) as set theory operators, which allows for processing more complex queries. Experimental results (conducted on two Spanish databases from different domains) show that treatment of auxiliary words improves correctness of translation by 12.1%. With the developed NLIDB 82of queries were correctly translated (and thus answered). Reconfiguring the NLIDB from one domain to the other took only ten minutes.

Frameworks for Querying Databases Using Natural Language

International Journal of Data Warehousing and Mining, 2021

A natural language interface is useful for a wide range of users to retrieve their desired information from databases without requiring prior knowledge of database query language such as SQL. The advent of user-friendly technologies, such as speech-enabled interfaces, have revived the use of natural language technology for querying databases; however, the most relevant and last work presenting state of the art was published back in 2013 and does not encompass several advancements. In this paper, the authors have reviewed 47 frameworks that have been developed during the last decade and categorized the SQL and NoSQL-based frameworks. Furthermore, the analysis of these frameworks is presented on the basis of criteria such as supporting language, scheme of heuristic rules, interoperability support, scope of the dataset, and overall performance score. The study concludes that the majority of frameworks focus on translating natural language queries to SQL and translates English language ...