2 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
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Visual SQL analyzer project report
Structure Query Language (SQL) is widely used to access data stored in relational database systems. Although a powerful and flexible language, SQL can also be complex and hard to learn. For most new SQL users, it's easy to write SQL statement by following SQL grammar and syntax rules, but it's hard to know if the statement expresses what the user really wants.
In most cases, a visual representation can help the user interpret and understand the SQL Statement being represented. An effective graphical representation often conveys a concept immediately and more clearly than a literal explanation. To help new users to learn SQL, we have developed a tool to help them understand the SQL statements, and check if the semantics matches their intentions. For this project we developed a simple Visual SQL Analyzer tool to translate SQL statement into Visual diagrams to show what the database will do with th.is statement, so that the user could understand and confirm their intentions. The implementation of our tool has five tables and ten fields per table limitation and only implements the six most common SQL statements. However, it can be extended. The visual representation used in our project was introduced in the book "A Visual Introduction to SQL", and has been used in other system such as MS SQL Server. our system is the only web_based and free tool.2001 best estimate for issue date and commencement year based on available information