Semantic Metadata Clustering

Semantic Metadata Clustering

Semantic metadata is defined as the metadata that enables give or define the value of data and the names of things that can be articulated to represent such values [1]. Semantic metadata clustering methods are the ideal tool to convey information through social media. As technicians and specialists in the field of technology try to outline information in a well-defined meaning and enable people and computers to work in cooperation, they deploy different techniques of metadata clustering. For instance, in social media, hashtags are now used for a variety of purposes – for telling jokes, collecting consumer feedback, launching campaigns, advertising, following topics, and much more. In the metadata clustering of hashtags, two major approaches are put into consideration. The most significant of these approaches examine the lexical semantics of external resources, commonly known as metadata independent of the tweet messages themselves. For proper and effective performance of the metadata-based approach, hashtag quality and metadata quality factors are dependent on. Once these factors are deployed, it enables to have a direct impact on the performance.

Accuracy of the currently available semantic hashtag clustering method is enhanced through the extraction of words from Wikipedia and Wordnet. Although, there are a series of studies done to correctly familiarize users of social media on using Wikipedia and Wordnet as the primary clusters. In one method, carried out by Vicent and Moreno [5], they used similar Wordnet and Wikipedia as a metadata source for identifying the lexical semantics of a hashtag. Clustering decisions are taken at the word level in their method, and into their conclusion, it often leads to wrong clusters.