The human mind perceives the world around it in hierarchical structures. The input for the models built by humans are, however, flat. This creates dissonance. For instance, humans find the class-hierarchy tree for animals, intuitive. Nevertheless, they find presenting the relationships to a machine-learning algorithm a challenge. So, how to work with taxonomic data, without creating a dissonance?
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To understand data, it is important to recognize its syntax and semantics. This is why relational data modeling or eXtensible Markup Language (XML) modeling, the two models used in the last couple of decades for modeling data, have their limitations. Between the two, XML can capture the syntax. Additionally, semantics can be written in an XML format, if the semantics has already been captured by a modeling scheme based in formal logic. For these reasons, enterprises need a formal ontology.
Taxonomies are mapped to each other so that they can be employed in one combination or the other. Mapping was considered to be unidirectional, i.e. from terms in a tagged taxonomy to terms in a retrieval taxonomy. However, with taxonomy mapping being performed more frequently today, the view has become broader as reflected in the emergence of new standards and enhanced features in taxonomy management software.
Tagging is important in building automation systems (BAS) as it gives meaning to the data generated by them. In turn, tagging data facilitates analyzing and optimizing these systems, which include building equipment, automation, controls, and sensors. Additionally, it will provide a standard to ensure better interoperability between devices. Furthermore, tagging is seen to be one of the best options for organizing and making sense of the data generated by BAS.
Online retailers need to give their products the right names and place them in the correct categories to enable customers to find and buy them. This is the raison d’être for product taxonomy. They organize all available products in a way that customers can find what they want instantly.