Attribute Exploration with Multiple Contradicting Partial Experts
This work addresses knowledge discovery in domains with conflicting expert opinions, but it appears incremental as it builds on existing attribute exploration methods.
The paper tackles the problem of attribute exploration with multiple experts who have contradicting views, extending Formal Concept Analysis to handle group perspectives and explore shared dependencies.
Attribute exploration is a method from Formal Concept Analysis (FCA) that helps a domain expert discover structural dependencies in knowledge domains which can be represented as formal contexts (cross tables of objects and attributes). In this paper we present an extension of attribute exploration that allows for a group of domain experts and explores their shared views. Each expert has their own view of the domain and the views of multiple experts may contain contradicting information.