5 research outputs found
Knowledge Rich Natural Language Queries over Structured Biological Databases
Increasingly, keyword, natural language and NoSQL queries are being used for
information retrieval from traditional as well as non-traditional databases
such as web, document, image, GIS, legal, and health databases. While their
popularity are undeniable for obvious reasons, their engineering is far from
simple. In most part, semantics and intent preserving mapping of a well
understood natural language query expressed over a structured database schema
to a structured query language is still a difficult task, and research to tame
the complexity is intense. In this paper, we propose a multi-level
knowledge-based middleware to facilitate such mappings that separate the
conceptual level from the physical level. We augment these multi-level
abstractions with a concept reasoner and a query strategy engine to dynamically
link arbitrary natural language querying to well defined structured queries. We
demonstrate the feasibility of our approach by presenting a Datalog based
prototype system, called BioSmart, that can compute responses to arbitrary
natural language queries over arbitrary databases once a syntactic
classification of the natural language query is made
fr2sql : Interrogation de bases de données en français
National audienceDatabases are increasingly common and are becoming increasingly important in actual applications and Web sites. They often used by people who do not have great competence in this domain and who do not know exactly their structure. This is why translators from natural language to SQL queries are developed. Unfortunately, most of these translators is confined to a single database due to the specificity of the base architecture. In this paper, we propose a method to query any database from french. We evaluate our application on two different databases and we also show that it supports more operations than most other translators.Les bases de données sont de plus en plus courantes et prennent de plus en plus d'ampleur au sein des applications et sites Web actuels. Elles sont souvent amenées à être utilisées par des personnes n'ayant pas une grande compétence en la matière et ne connaissant pas rigoureusement leur structure. C'est pour cette raison que des traducteurs du langage naturel aux requêtes SQL sont développés. Malheureusement, la plupart de ces traducteurs se cantonnent à une seule base du fait de la spécificité de l'architecture de celle-ci. Dans cet article, nous proposons une méthode visant à pouvoir interroger n'importe quelle base de données à partir de questions en français. Nous évaluons notre application sur deux bases à la structure différente et nous montrons également qu'elle supporte plus d'opérations que la plupart des autres traducteurs