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.