Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Foundations of semantic web databases
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Query-Answering CG Knowledge Bases
ICCS '08 Proceedings of the 16th international conference on Conceptual Structures: Knowledge Visualization and Reasoning
A conceptual graph approach to the generation of referring expressions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The SG family: extensions of simple conceptual graphs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
OWL-QL-a language for deductive query answering on the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
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In knowledge bases (KB), the open world assumption and the ability to express variables may lead to an answer redundancy problem. This problem occurs when the returned answers are comparable. In this paper, we define a framework to distinguish amongst answers. Our method is based on adding contextual knowledge extracted from the KB. The construction of such descriptions allows clarification of the notion of redundancy between answers, based not only on the images of the requested pattern but also on the whole KB. We propose a definition for the set of answers to be computed from a query, which ensures both properties of non-redundancy and completeness. While all answers of this set can be distinguished from others with a description, an open question remains concerning what is a good description to return to an end-user. We introduce the notion of smart answer and give an algorithm that computes a set of smart answers based on a vertex neighborhood distance.