A F José Martínez

Natural Language Interfaces to Databases: An Analysis of the State of the Art

People constantly make decisions based on information, most of which is stored in databases. Accessing this information requires the use of query languages to databases such as SQL. In order to avoid the difficulty of using these languages for users who are not computing experts, Natural Language Interfaces for Databases (NLIDB) have been developed, which permit to query databases through queries formulated in natural language. Although since the 60s many NLIDBs have been developed, their performance has not been satisfactory, there still remain very difficult problems that have not been solved by NLIDB technology, and there does not yet exist a standardized method of evaluation that permits to compare the performance of different NLIDBs. This chapter presents an analysis of NLIDBs, which includes their classification, techniques, advantages, disadvantages, and a proposal for a proper evaluation of them.

Dialogue manager for a NLIDB for solving the semantic ellipsis problem in query formulation

A query written in natural language (NL) may involve several linguistic problems that cause a query not being interpreted or translated correctly into SQL. One of these problems is implicit information or semantic ellipsis, which can be understood as the omission of important words in the wording of a query written in NL. An exhaustive survey on NLIDB works has revealed that most of these works has not systematically dealt with semantic ellipsis. In experiments conducted on commercial NLIDBs, very poor results have been obtained (7% to 16.9%) when dealing with query corpora that involve semantic ellipsis. In this paper we propose a dialogue manager (DM) for a NLIDB for solving semantic ellipsis problems. The operation of this DM is based on a typification of elliptical problems found in queries, which permits to systematically deal with this problem. Additionally, the typification has two important characteristics: domain independence, which permits the typification to be applied to queries of different databases, and generality, which means that it holds for different languages such as English, French, Italian, Spanish, etc. These characteristics are inherited to the dialogue processes implemented in the DM, since they are based on this typification. In experiments conducted with this DM and a NLIDB on a corpus of elliptical queries, an increase of correctly answered queries of 30-35% was attained.

Survey of polynomial transformations between NP-complete problems

This paper aims at being a guide to understand polynomial transformations and polynomial reductions between NP-complete problems by presenting the methodologies for polynomial reductions/transformations and the differences between reductions and transformations. To this end the article shows examples of polynomial reductions/transformations and the restrictions to reduce/transform between NP-complete problems. Finally, this paper includes a digraph with the historical reductions/transformations between instances of NP-complete problems and introduces the term family of polynomial transformations.

Modeling the Nonlinear Nature of Response Time in the Vertical Fragmentation Design of Distributed Databases

The generalized use of the Internet has facilitated the implementation of distributed database (DDB) systems, which are becoming increasingly common-place. Unfortunately, though there exist many models for optimizing the design of DDBs (i.e., the distribution of data), they usually seek to optimize the transmission and processing costs of queries and overlook the delays incurred by their transmission and processing, which can be a major concern for Internet-based systems. In this paper a mathematical model is presented, which describes the behavior of a DDB with vertical fragmentation and permits to optimize its design taking into account the roundtrip response time (query transmission time, query processing time, and response transmission time).

Translation of Natural Language Queries to SQL that Involve Aggregate Functions, Grouping and Subqueries for a Natural Language Interface to Databases

Currently, huge amounts of information are stored in databases (DBs). In order to facilitate access to information to all users, natural language interfaces to databases (NLIDBs) have been developed. To this end, these interfaces translate natural language queries to a DB query language. For businesses, the main application of NLIDBs is for decision making by facilitating access to information in a flexible manner. For a NLIDB to be considered complete, it must deal with queries that involve aggregate functions: COUNT, MIN, MAX, SUM and AVG. The prototype developed at the Instituto Tecnologico de Cd. Madero (ITCM) can translate queries in natural language to SQL; however, it did not have a module for dealing with aggregate functions, grouping and subqueries. In this paper a new module of this NLIDB for dealing with aggregate functions, grouping and subqueries is described, and experimental results are presented, which show that this interface has a performance (recall) better than that of C-Phrase.

Issues in Querying Databases with Design Anomalies Using Natural Language Interfaces

Accessing information is a vital activity in businesses; therefore, databases (DBs) have become necessary tools for storing their information. However, for accessing the information stored in a database, it is necessary to use a DB query language, such as SQL. Natural language interfaces to databases (NLIDBs) allow inexperienced users to obtain information from a DB using natural language expressions without the need of using a DB query language. Despite the relative effectiveness of NLIDBs, most of the approaches proposed for designing NLIDBs ignore the possibility that the DB to be queried could be poorly designed; i.e., it could have design anomalies. Unfortunately, various experiments (described in this paper) show that DB anomalies degrade the performance (recall) of NLIDBs. The purpose of this paper is to analyze the most common DB design anomalies for proposing solutions to this problem and avoid performance degradation of NLIDBs when accessing such DBs.Accessing information is a vital activity in businesses; therefore, databases (DBs) have become necessary tools for storing their information. However, for accessing the information stored in a database, it is necessary to use a DB query language, such as SQL. Natural language interfaces to databases (NLIDBs) allow inexperienced users to obtain information from a DB using natural language expressions without the need of using a DB query language. Despite the relative effectiveness of NLIDBs, most of the approaches proposed for designing NLIDBs ignore the possibility that the DB to be queried could be poorly designed; i.e., it could have design anomalies. Unfortunately, various experiments (described in this paper) show that DB anomalies degrade the performance (recall) of NLIDBs. The purpose of this paper is to analyze the most common DB design anomalies for proposing solutions to this problem and avoid performance degradation of NLIDBs when accessing such DBs.

Interface for Composing Queries for Complex Databases for Inexperienced Users

In most business activities, decision-making has a very important role, since it may benefit or harm the business. Nowadays decision-making is based on information obtained from databases, which are only accessible directly by computer experts; however, the end-user that requires information from a database is not always a computer expert, so the need arises to allow inexperienced users to obtain information directly from a database. To this end, several tools are commercially available such as visual query building and natural language interfaces to databases (NLIDBs). However, the first kind of tools requires at least a basic level of knowledge of some formal query language, while NLIDBs, despite the fact that users do not require training for using the interface, have not obtained the desired performance due to problems inherent to natural language processing. In this paper an intuitive interface is presented, which allows inexperienced users to easily compose queries in SQL, without the need of training on its operation nor having knowledge of SQL.

Analysis of Some Database Schemas Used to Evaluate Natural Language Interfaces to Databases

Most research work about the development of Natural Language Interface to Databases (NLIDB) has been focused on the study of the interpretation and translation from natural language queries to SQL queries. For this purpose and in order to improve the performance in a NLIDB, researchers have addressed different issues related to natural language processing. In addition to this, we consider that the performance of a NLIDB also depends on its ability to adapt to a database schema. For this reason, we analyzed the Geobase, ATIS and Northwind database schemas, commonly used to evaluate NLIDBs. As a result of this analysis, we present in this paper some issues arising from the three database schemas analyzed, which they should be considered in the implementation of a NLIDB to improve its performance.

Automatic Extraction of Hyponymy-Hypernymy Lexical Relations between Nouns from a Spanish Dictionary

In this paper a method is presented which permits to automatically extract lexical-semantic relations between nouns (specifically for concrete nouns since they have a well structured taxonomy). From the definitions of the entries in a Spanish dictionary, the hypernym of an entry is extracted from the entry definition according to the basic assumption that the first noun in the definition is the entry hypernym. After obtaining the hypernym for each entry, multilayered hyponymy-hyperonymy relations are generated from a noun, which is considered the root of the domain. The domains for which this approach was tested were zoology and botany. Five levels of hyponymy-hypernymy relations were generated for each domain. For the zoology domain a total of 1,326 relations was obtained with an average percentage of correctly generated relations (precision) of 84.31% for the five levels. 91.32% of all the relations of this domain were obtained in the first three levels, and for each of these levels the precision exceeds 96%. For the botany domain a total of 1,199 relations was obtained, with an average precision of 71.31% for the five levels. 90.76% of all the relations of this domain were obtained in the first level, and for this level the precision exceeds 99%.

Towards the Simulation of Social Interactions through Embodied Conversational Agents

In this paper, we present a pluridisciplinary approach in order to model the behavior of an embodied conversational agent that expresses emotional and social interactions. We present our methodology to reproduce credible social interactions. Particularly, we discuss the role of the context, the culture, and the emotions in the model of the management of speech acts. This model has been implemented in the context of virtual therapy to simulate the interaction between a therapist and a post-CVA patient.