ARES: a relational database with the capability of performing flexible interpretation of queries
IEEE Transactions on Software Engineering
Using approximate reasoning to represent default knowledge
Artificial Intelligence
Providing Quality Responses with Natural Language Interfaces: The Null Value Problem
IEEE Transactions on Software Engineering
Indefinite and maybe information in relational databases
ACM Transactions on Database Systems (TODS)
A preliminary annotated bibliography on domain engineering
ACM SIGSOFT Software Engineering Notes
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
On deductive databases with incomplete information
ACM Transactions on Information Systems (TOIS)
An analytic framework for specifying and analyzing imprecise requirements
Proceedings of the 18th international conference on Software engineering
ICSE '91 Proceedings of the 13th international conference on Software engineering
Contemporary Application-Domain Taxonomies
IEEE Software
FLEX: A Tolerant and Cooperative User Interface to Databases
IEEE Transactions on Knowledge and Data Engineering
Controlled Generation of Intensional Answers
IEEE Transactions on Knowledge and Data Engineering
Intensional Answers to Database Queries
IEEE Transactions on Knowledge and Data Engineering
Current Approaches to Handling Imperfect Information in Data and Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Intelligent Query Answering by Knowledge Discovery Techniques
IEEE Transactions on Knowledge and Data Engineering
The Mathematical Bases for Qualitative Reasoning
IEEE Expert: Intelligent Systems and Their Applications
Inconsistency Handling in Multiperspective Specifications
IEEE Transactions on Software Engineering
IEEE Transactions on Knowledge and Data Engineering
On Representing Indefinite and Maybe Information in Relational Databases
Proceedings of the Fourth International Conference on Data Engineering
SQLf: a relational database language for fuzzy querying
IEEE Transactions on Fuzzy Systems
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We are formulating design guidelines for a knowledge system that is to provide answers to natural language queries in context. A query that starts out being very vague is to be sharpened with the assistance of the system. Also, the response to a query is more meaningful when presented in context. We recognize three types of context: essential, reference, and source. Essential context associates the response to a query with a time and place. Reference context provides reference values that help the user determine whether the response to a fuzzy query is true or false. Source context relates to the dependability of the response.