Clustering Concept Chains from Ordered Data without Path Descriptions

TL;DR

This paper introduces a generic, rule-based clustering method for forming concept chains from unordered, metadata-free data using simple counting, effective even with random and shallow data samples.

Contribution

It presents a novel, non-hierarchical clustering approach that relies on counting mechanisms to group concepts without path descriptions or metadata.

Findings

Effective clustering of concept chains from random data

Handles variability and randomness in data

Operates without hierarchical or metadata requirements

Abstract

This paper describes a process for clustering concepts into chains from data presented randomly to an evaluating system. There are a number of rules or guidelines that help the system to determine more accurately what concepts belong to a particular chain and what ones do not, but it should be possible to write these in a generic way. This mechanism also uses a flat structure without any hierarchical path information, where the link between two concepts is made at the level of the concept itself. It does not require related metadata, but instead, a simple counting mechanism is used. Key to this is a count for both the concept itself and also the group or chain that it belongs to. To test the possible success of the mechanism, concept chain parts taken randomly from a larger ontology were presented to the system, but only at a depth of 2 concepts each time. That is - root concept plus a concept that it is linked to. The results show that this can still lead to very variable structures being formed and can also accommodate some level of randomness.