Consultas difusas en asistencia al diagnóstico médico

This paper proposes the utilization of a fuzzy database engine for supporting medical diagnoses. Expert know how is stored in a relational database and then it is modeled diagnoses rules with fuzzy queries that pulls out the most accurate information related to the sickness and therefore supporting doctors with the medical diagnostic. A solution prototype has been developed with information related to respiratory disease characterization and it is built with fuzzy queries using SQLf. This case study can be used to define a roadmap for future developments in medical diagnosis supported on fuzzy databases. As always, the diagnosis can only be given by a specialist, these systems only provide help in their work task.Este artículo propone el uso de un motor de base de datos difuso para ayudar en el diagnóstico médico. El conocimiento experto se almacena en una base de datos relacional y luego se modela mediante reglas de diagnóstico con consultas difusa que extraen la información más precisa relacionada con la enfermedad y, por lo tanto, apoyan a los médicos con el diagnóstico médico. Hemos construido un prototipo de sistema con una base de datos que almacena la caracterización de enfermedades respiratorias. Esta aplicación se ha creado utilizando un sistema de gestión de bases de datos que admite el lenguaje de consulta difusa SQLf. Este trabajo encamina desarrollos futuros en el diagnóstico médico soportado sobre bases de datos difusas. Como siempre, el diagnóstico solo puede ser dado por un especialista, estos sistemas solo brindan ayuda en su labor médica

Database search vs. information retrieval : a novel method for studying natural language querying of semi-structured data

The traditional approach of querying a relational database is via a formal language, namely SQL. Recent developments in the design of natural language interfaces to databases show promising results for querying either with keywords or with full natural language queries and thus render relational databases more accessible to non-tech savvy users. Such enhanced relational databases basically use a search paradigm which is commonly used in the field of information retrieval. However, the way systems are evaluated in the database and the information retrieval communities often differs due to a lack of common benchmarks. In this paper, we provide an adapted benchmark data set that is based on a test collection originally used to evaluate information retrieval systems. The data set contains 45 information needs developed on the Internet Movie Database (IMDb), including corresponding relevance assessments. By mapping this benchmark data set to a relational database schema, we enable a novel way of directly comparing database search techniques with information retrieval. To demonstrate the feasibility of our approach, we present an experimental evaluation that compares SODA, a keyword-enabled relational database system, against the Terrier information retrieval system and thus lays the foundation for a future discussion of evaluating database systems that support natural language interfaces

Using domain specific language and sequence to sequence models as a hybrid framework for a natural language interface to a database solution

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe aim of this project is to provide a new approach to solving the problem of converting natural language into a language capable of querying a database or data repository. This problem has been around for a while, in the 1970's the US Navy developed a solution called LADDER and since then there have been an array of solutions, approaches and tweaks that have kept the research community busy. The introduction of electronic assistants into the smart phone in 2010 has given new impetus to this problem. With the increasingly pervasive nature of data and its ever expanding use to answer questions within business science, medicine extracting data is becoming more important. The idea behind this project is to make data more democratised by allowing access to it without the need for specialist languages. The performance and reliability of converting natural language into structured query language can be problematic in handling nuances that are prevalent in natural language. Relational databases are not designed to understand language nuance. This project introduces the following components as part of a holistic approach to improving the conversion of a natural language statement into a language capable of querying a data repository. ● The idea proposed in this project combines the use of sequence to sequence models in conjunction with the natural language part of speech technologies and domain specific languages to convert natural language queries into SQL. The approach being proposed by this chapter is to use natural language processing to perform an initial shallow pass of the incoming query and then use Google's Tensor Flow to refine the query with the use of a sequence to sequence model. ● This thesis is also proposing to use a Domain Specific Language (DSL) as part of the conversion process. The use of the DSL has the potential to allow the natural language query to be translated into more than just an SQL statement, but any query language such as NoSQL or XQuery

How to Find Suitable Ontologies Using an Ontology-based WWW Broker

Knowledge reuse by means of outologies now faces three important problems: (1) there are no standardized identifying features that characterize ontologies from the user point of view; (2) there are no web sites using the same logical organization, presenting relevant information about ontologies; and (3) the search for appropriate ontologies is hard, time-consuming and usually fruitless. To solve the above problems, we present: (1) a living set of features that allow us to characterize ontologies from the user point of view and have the same logical organization; (2) a living domain ontology about ontologies (called ReferenceOntology) that gathers, describes and has links to existing ontologies; and (3) (ONTO)2Agent, the ontology-based www broker about ontologies that uses the Reference Ontology as a source of its knowledge and retrieves descriptions of ontologies that satisfy a given set of constraints. (ONTO)~Agent is available at http://delicias.dia.fi.upm.es/REFERENCE ONTOLOGY

Extending a set-theoretic implementation of Montague Semantics to accommodate n-ary transitive verbs.

Natural-language querying of databases remains an important and challenging area. Many approaches have been proposed over many years yet none of them has provided a comprehensive fully-compositional denotational semantics for a large sub-set of natural language, even for querying first-order non-intentional, non-modal, relational databases. One approach, which has made significant progress, is that which is based on Montague Semantics. Various researchers have helped to develop this approach and have demonstrated its viability. However, none have yet shown how to accommodate transitive verbs of arity greater than two. Our thesis is that existing approaches to the implementation of Montague Semantics in modern functional programming languages can be extended to solve this problem. This thesis is proven through the development of a compositional semantics for n-ary transitive verbs (n ≥ 2) and implementation in the Miranda programming environment. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .R69. Source: Masters Abstracts International, Volume: 44-03, page: 1413. Thesis (M.Sc.)--University of Windsor (Canada), 2005

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft