DeepAI AI Chat
Log In Sign Up

Attribute Exploration with Multiple Contradicting Partial Experts

05/31/2022
by   Maximilian Felde, et al.
Universität Kassel
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

02/04/2021

Triadic Exploration and Exploration with Multiple Experts

Formal Concept Analysis (FCA) provides a method called attribute explora...
08/23/2019

Interactive Collaborative Exploration using Incomplete Contexts

A common representation of information about relations of objects and at...
02/05/2019

An Exploratory Study on Visual Exploration of Model Simulations by Multiple Types of Experts

Experts in different domains rely increasingly on simulation models of c...
09/07/2022

ErgoExplorer: Interactive Ergonomic Risk Assessment from Video Collections

Ergonomic risk assessment is now, due to an increased awareness, carried...
12/23/2017

Towards Collaborative Conceptual Exploration

In domains with high knowledge distribution a natural objective is to cr...
02/17/2017

Towards a Unified Taxonomy of Biclustering Methods

Being an unsupervised machine learning and data mining technique, biclus...
11/19/2015

Abstract Attribute Exploration with Partial Object Descriptions

Attribute exploration has been investigated in several studies, with par...