An Intelligent Query Interface with Natural Language Support (original) (raw)

Leveraging Systemic-Functional Linguistics to Enhance Intelligent Database Querying

Sixth International Conference on Intelligent Systems Design and Applications, 2006

In this paper we describe our efforts to build a bridge between Intelligent Query Interfaces and Natural Language Generation technologies. We have developed a query interface that enables users to access heterogeneous data sources by means of an integrated ontology. Currently, we are re-designing this intelligent query interface so as to render its logic-based queries in natural language, leveraging results achieved to date by applied Systemic-Functional Linguistics.

Ontology Based Queries–Investigating a Natural Language Interface

2010

ABSTRACT In this paper we look at what may be learned from a comparative study examining non-technical users with a background in social science browsing and querying metadata. Four query tasks were carried out with a natural language interface and with an interface that uses a web paradigm with hyperlinks.

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 ...

Improving the customization of natural language interface to databases using an ontology

2007

Natural language interfaces to databases are considered one of the best alternatives for final users who wish to make complex, uncommon and frequent queries, which is a very common need in organizations. The use of this type of interfaces has been very limited, due to their limited publicizing and the complexity to adapt them to users' needs, and because their precision varies widely.

Quelo Natural Language Interface: Generating queries and answer descriptions

We present an intelligent NLI interface, namely Quelo NLI, for querying and exploring semantic data. Its intelligence lies in the use of reasoning services over an ontology. These support the intentional navigation of the underlying datasource and the formulation of queries that are consistent with respect to it. Its Natural Language Generation (NLG) module masks the formulation of queries as the composition of English text and generates descriptions of query answers. An important feature of Quelo NLI is that it is portable as it is not bound to an ontology of a specific domain. We describe Quelo NLI functionality and present a grammar-based natural language generation approach that better supports the domain-independent generation of fluent queries and naturally extends for the generation of answers descriptions. We concentrate on describing the generation resources, namely a domain-independent handwritten grammar and a lexicon that is automatically extracted from concepts and relations of the underlying ontology.

A Model of a Generic Natural Language Interface for Querying Database

International Journal of Intelligent Systems and Applications, 2016

Extracting information from database is typically done by using a structured language such as SQL (Structured Query Language). But non expert users can't use this later. Wherefore using Natural Language (NL) for communicating with database can be a powerful tool. But without any help, computers can't understand this language; that is why it is essential to develop an interface able to translate user's query given in NL into an equivalent one in Database Query Language (DBQL). This paper presents a model of a generic natural language query interface for querying database. This model is based on machine learning approach which allows interface to automatically improve its knowledge base through experience. The advantage of this interface is that it functions independently of the database language, content and model. Experimentations are realized to study the performance of this interface and make necessary corrections for its amelioration

Ontology Based Natural Language Interface for Relational Databases

Procedia Computer Science, 2016

Developing Natural Language Query Interface to Relational Databases has gained much interest in research community since forty years. This can be termed as structured free query interface as it allows the users to retrieve the data from the database without knowing the underlying schema. Structured free query interface should address majorly two problems. Querying the system with Natural Language Interfaces (NLIs) is comfortable for the naive users but it is difficult for the machine to understand. The other problem is that the users can query the system with different expressions to retrieve the same information. The different words used in the query can have same meaning and also the same word can have multiple meanings. Hence it is the responsibility of the NLI to understand the exact meaning of the word in the particular context. In this paper, a generic NLI Database system has proposed which contains various phases. The exact meaning of the word used in the query in particular context is obtained using ontology constructed for customer database. The proposed system is evaluated using customer database with precision, recall and f1-measure.

Tooling framework for instantiating natural language querying system

Proceedings of the VLDB Endowment

Recent times have seen a growing demand for natural language querying (NLQ) interfaces to retrieve information from the structured data sources such as knowledge bases. Using this interface, business users can directly interact with a database without the knowledge of the query language or the data schema. Our earlier work describes a natural language query engine called ATHENA which has several shortcoming around ease of use and compatibility with data stores, formats and flows. In this demonstration paper, we present a tooling framework to address these challenges so that one can instantiate an NLQ system with utmost ease. Our framework makes it easy and practically applicable to all NLIDB scenarios involving different sources of structured data, file formats, and ontologies to enable natural language querying on top of them with minimal human configuration. We present the tool design and the solution to the challenges towards building such a system and demonstrate its applicabili...

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.

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...