Leveraging Systemic-Functional Linguistics to Enhance Intelligent Database Querying (original) (raw)

An Intelligent Query Interface with Natural Language Support

Flairs, 2006

The project described by the present paper aims at building a bridge between Intelligent Query Interfaces and Natural Language Generation technologies. The idea is to have a query interface enabling the users to access heterogeneous data sources by means of an integrated ontology. This paper shows how we are redesigning our intelligent query interface by rendering the logic-based queries in natural language, leveraging the results achieved to-date by applied Systemic-Functional Linguistics.

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...

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 ...

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.

ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores

In this paper, we present ATHENA, an ontology-driven system for natural language querying of complex relational databases. Natural language interfaces to databases enable users easy access to data, without the need to learn a complex query language, such as SQL. ATHENA uses domain specific ontologies, which describe the semantic entities, and their relationships in a domain. We propose a unique two-stage approach, where the input natural language query (NLQ) is first translated into an intermediate query language over the ontology, called OQL, and subsequently translated into SQL. Our two-stage approach allows us to decouple the physical layout of the data in the relational store from the semantics of the query, providing physical independence. Moreover, ontologies provide richer semantic information, such as inheritance and membership relations, that are lost in a relational schema. By reasoning over the ontologies, our NLQ engine is able to accurately capture the user intent. We study the effectiveness of our approach using three different workloads on top of geographical (GEO), academic (MAS) and financial (FIN) data. ATHENA achieves 100% precision on the GEO and MAS workloads, and 99% precision on the FIN work-load which operates on a complex financial ontology. Moreover, ATHENA attains 87.2%, 88.3%, and 88.9% recall on the GEO,

Creating and Querying Linguistically Motivated Ontologies

This paper argues that a formal ontology (in our case a description logic knowledge base) should be created in a linguistically motivated way so that it can be queried easily by non-specialists. This can best be achieved by using a strict naming convention that is based on those linguistic expressions that occur in the application domain for which the ontology will be created. We will see that ABox and TBox statements that closely follow this naming convention can be written directly in a controlled natural language and that the same controlled natural language can be used to query the description logic knowledge base. Both ABox and TBox statements written in controlled natural language are translated automatically into the Knowledge Representation System Specification (KRSS) syntax and questions are translated into RacerPro's new query language nRQL and answered over the description logic knowledge base. Using a controlled natural language as a high-level interface language abstracts away from any formal notation and allows for true collaboration between humans and machines.

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.

The history and recent advances of Natural Language Interfaces for Databases Querying

2021

Databases have been always the most important topic in the study of information systems, and an indispensable tool in all information management systems. However, the extraction of information stored in these databases is generally carried out using queries expressed in a computer language, such as SQL (Structured Query Language). This generally has the effect of limiting the number of potential users, in particular non-expert database users who must know the database structure to write such requests. One solution to this problem is to use Natural Language Interface (NLI), to communicate with the database, which is the easiest way to get information. So, the appearance of Natural Language Interfaces for Databases (NLIDB) is becoming a real need and an ambitious goal to translate the user’s query given in Natural Language (NL) into the corresponding one in Database Query Language (DBQL). This article provides an overview of the state of the art of Natural Language Interfaces as well ...

An Ontology-Based Architecture for Natural Language Access to Relational Databases

Lecture Notes in Computer Science, 2013

Natural language (NL) access to databases is a problem that has interested researchers for many years. We demonstrate that an ontology-based approach is technically feasible to handle some of the challenges facing NL query processing for database access. This paper presents the architecture, algorithms and results from the prototype thereof which indicate a domain and language independent architecture with high precision and recall rates. Studies are conducted for each of English and Swahili queries, both for same language and cross-lingual retrieval, from which we demonstrate promising precision and recall rates, language and domain independence, and that for language pairs it is sufficient to incorporate a machine translation system at the gazetteer level.

Ontology-Based Natural Language Query Interfaces for Data Exploration

2018

Enterprises are creating domain-specific knowledge bases by curating and integrating all their business data, structured, unstructured and semi-structured, and using them in enterprise applications to derive better business decisions. One distinct characteristic of these enterprise knowledge bases, compared to the open-domain general purpose knowledge bases like DBpedia [16] and Freebase [6], is their deep domain specialization. This deep domain understanding empowers many applications in various domains, such as health care and finance. Exploring such knowledge bases, and operational data stores requires different querying capabilities. In addition to search, these databases also require very precise structured queries, including aggregations, as well as complex graph queries to understand the various relationships between various entities of the domain. For example, in a financial knowledge base, users may want to find out “which startups raised the most VC funding in the first qu...