  Metadata presents a medium for connection, elaboration, examination, and
comprehension of relativity between two datasets. Metadata can be enriched to
calculate the existence of a connection between different disintegrated
datasets. In order to do so, the very first task is to attain a generic
metadata representation for domains. This representation narrows down the
metadata search space. The metadata search space consists of attributes, tags,
semantic content, annotations etc. to perform classification. The existing
technologies limit the metadata bandwidth i.e. the operation set for matching
purposes is restricted or limited. This research focuses on generating a mapper
function called cognate that can find mathematical relevance based on pairs of
attributes between disintegrated datasets. Each pair is designed from one of
the datasets under consideration using the existing metadata and available
meta-tags. After pairs have been generated, samples are constructed using a
different combination of pairs. The similarity and relevance between two or
more pairs are attained by using a data clustering technique to generate large
groups from smaller groups based on similarity index. The search space is
divided using a domain divider function and smaller search spaces are created
using relativity and tagging as the main concept. For this research, the
initial datasets have been limited to textual information. Once all disjoint
meta-collection have been generated the approximation algorithm calculates the
centers of each meta-set. These centers serve the purpose of meta-pointers i.e.
a collection of meta-domain representations. Each pointer can then join a
cluster based on the content i.e. meta-content. It also facilitates the process
of possible synonyms across cross-functional domains. This can be examined
using meta-pointers and graph pools.
