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Clustering Concept Chains from Ordered Data without Path Descriptions

by   Kieran Greer, et al.

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.


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