Completely agree with the Completeness issue pointed by Michael. Still you could do some analysis with available data. I have done something similar to analyze hits from a genome scale RNAi screening.

Get all genes for a KO:Pathway in your list by parsing the ko.list

For example for the KO:Pathway path:ko0001 you can get all the components by parsing ko.list.

Now check how many ko components from your list are present.

You could assign a percentage to quantify this :

Completeness of pathway X = (No. of KO components in KO pathway X / No. of KO components in your list that belong to pathway X ) 100

This depends a bit on your definition of "see." ;-) For interactive exploration, I would recommend iPath which is based on KEGG data. There you can upload a list of genes and check the pathways by eye.

Computationally, you should be able to check if you have all the proteins for a pathway (e.g. if you have at least one hit for all the KO). But some pathways are more of a collection of independent modules, while in another pathway you might miss a crucial enzyme. So "completeness" is another difficult term.