Abstract Attribute Exploration with Partial Object Descriptions
This work addresses a theoretical challenge in knowledge acquisition for formal concept analysis, but it is incremental as it builds on existing studies without introducing new algorithms.
The paper tackles the complexity of attribute exploration when incorporating background knowledge and partially described counter-examples by providing an abstract, axiomatic framework to clarify the method's strategy, without focusing on algorithmic implementation.
Attribute exploration has been investigated in several studies, with particular emphasis on the algorithmic aspects of this knowledge acquisition method. In its basic version the method itself is rather simple and transparent. But when background knowledge and partially described counter-examples are admitted, it gets more difficult. Here we discuss this case in an abstract, somewhat "axiomatic" setting, providing a terminology that clarifies the abstract strategy of the method rather than its algorithmic implementation.