The concept of data linking, a technology that would allow data to be linked to the way pages are in Wikipedia, has evolved into a critical component of the next internet revolution. Similar to the web of hypertext, the web of data is constructed with the documents on the web. Unlike the web of hypertext, where links are relationships anchored in hypertext documents written in HTML, for data the links are between arbitrary things. For example, people, places, ideas, events, activities, and just about anything.
Consequently, searching this data-linked semantic web for a term will return a web of entities conceptually linked to the term. Furthermore, drilling down from the term into the entities would transform the term from a static text into a concept rooted in a contextual web. This is in essence, data ontology in action.
In information technology, data ontology is “the working model of entities and interactions in some particular domain of knowledge or practices.” They deal with “[insert verb here]-a” relationships — “has-a,” “reports-to-a,” “is-also-known-as-a,” and so on. These are valid relationships between one entity and another, which makes ontologies considerably complex than other data structures.
Companies, as part of their data warehousing operations, have been leveraging ontological principles for decades. In fact, the more refined data warehouses have a semantic layer responsible for translating the “underlying database structures into business user-oriented terms and constructs” to reflect business definitions. In addition, the semantic layer is responsible for ensuring that disparate data are mapped to a common ontological scheme.
Even though it is a daunting task to create something like business logic for an entire company or for an industry, the upfront cost of creating a data ontology pays off in the end. An ontology has several advantages over the more common data structures. For instance, it is easy to make architectural changes such as applying structural changes to relational databases, as modifying the semantic concept underpinning the property is all that is required. Moreover, data ontology has the potential to accelerate machine-learning algorithms dramatically by introducing pre-defined concepts.
Another advantage of ontologies is that they can function as bridges between IT systems. Systems sharing a common ontological language can share data with ease. Hence, ontology can serve as a common reference point for diverse operating systems, database types, and applications, eliminating the need for a translation layer in between. Furthermore, a network of ontological associations could lead to powerful data insights by permitting people to travel the informational web in all directions, the way they surf the internet.
As data is generated in ever-growing volumes, data ontology will become fundamental to how data is organized and distributed. However, like all large-scale operations, ontology needs to be used by a critical mass of people for it to become self-sustaining.
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