Concept Generation from Relational Structure

Question Generation from Concept Maps

In this paper we present a question generation approach suitable for tutorial dialogues. The approach is based on previous psychological theories that hypothesize questions are generated from a knowledge representation modeled as a concept map. Our model semi-automatically extracts concept maps from a textbook and uses them to generate questions. The purpose of the study is to generate and eval...

Relational Data Exploration by Relational Concept Analysis

Relational Concept Analysis [4] is an extension to FCA considering several contexts with relations between them. Often used to extend the knowledge that can be learned with FCA, RCA also meets the issue of combinatorial explosion. The initial specification of RCA implies a monotonic growth of the number of concepts and an exhaustiveness of all the concepts that can be obtained when a fixed poin...

The Relational Species Concept

Querying Relational Concept Lattices

Navigating a Concept Lattice Family w.r.t a Query Introduction Relational Concept Analysis (RCA) constructs conceptual abstractions from a relational context family (RCF). An RCF is sets of objects described by both own properties and inter-object links. It generates several lattices that are connected via relational attributes. Navigating such interrelated lattice family in order to find conce...

Structure Learning of Probabilistic Relational Models from Incomplete Relational Data

Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn structures of probabilistic relational model (PRM) from incomplete relational data. The missing values are filled in randomly at first, and a maximum likelihood tree (MLT) is generated from the complete data sample. Then, Gibb...