Metadata serves a variety of purposes. Some of the key purposes of metadata are for proving provenance, cataloging, discovery, and for describing standards. Among the key purposes, discovery is gaining prominence because, in the data science field, supply and management of metadata are crucial. Consequently, data discovery has become critical and has created a significant spike in the deployment of metadata as a tool for discovery (context).
To leverage, the full potential of metadata as a tool for data discovery, a shift from focusing on just storing data is required. In order to be successful in metadata’s use is to use discovery. In metadata, this is the knowledge that someone wants to discover, which goes back to Knowledge-Information-Data (KID) principles and having a database to allow processing. Having metadata built into linked data allows these kind of questions that would involve different joins in a relational database, for example, “What’s the population of Los Angeles County”. This is what discovery is, and in a world where the quantity of data is growing rapidly, context is the key to keeping metadata supply well managed.
There are diverse ways to approach metadata discovery. For instance, feature data can be registered in two or more semantics, such as schema.org ontology to support discovery. Schema.org is an ontological language pathology in linked data. It is a crowd-built, collaborative schema allowing users to add relationships or features based on their ontological relationship. Furthermore, users can decide on which vocabulary within schema.org would they want to recognize.
Linked data has a better probability of connecting data to the broader picture and the broader context, showing that most discoveries happen, when specializations in data are cross-pollinated. Imagine offering linked data, which has been peppered with more and more data that has all these different relationships in the context of the natural language. As more data in the context of this language and in the context of each other’s languages is added, it will improve artificial learning (AI) and machine learning deep in neural networks.
Artificial intelligence thrives on good training data and is based on confidence intervals and seeing patterns. Furthermore, as the confidence levels increases, it can rely on those patterns more and more. The more linked data is used, the more AI can get smarter in getting data discovered. Besides, the more linked data can be fed into AI, it will in turn feed into the KID concept by providing more data, and in turn providing more information, which grants more knowledge.
This is how metadata discovers the semantics of a data element in data sets and help the scientific community discover more accurate and insightful information.
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