Smart machine technologies will be a top priority for gaining competitive advantage over the next 10 years according to Gartner’s 2016 Hype Cycle for Emerging Technologies report. To help leverage this disruptive class of technology, Gartner added 16 new technologies to its Hype Cycle. One notable inclusion to this list was enterprise taxonomy and ontology management.
What are the implications of this? First of all, it’s an important validation of this technology. While it was once considered a topic for librarians, enterprise taxonomy and ontology management technologies are being recognized as critical for organizations in effectively managing the increasing amount of data for enterprise applications.
Defining taxonomy and ontology
In a general context, taxonomy and ontology are often considered synonymous. However, they are different concepts that require different tools to manage them. A taxonomy and an ontology are technically two ways of organizing information.
A taxonomy is a knowledge tree, a logical structure that uses hierarchies to classify documents, concepts, etc.
An ontology is a representation of knowledge that defines the meaning of concepts and the relationships among different concepts (it can include several taxonomies).
With the growing amount of information created every day, effective information management is a key business requirement. Included as part of a well implemented strategy, both taxonomy and ontology can help companies access and organize information more effectively.
The value of cognitive computing for enterprise taxonomy and ontology management
What does “well implemented” mean?
First of all, it means that the ontology and taxonomy should be relevant for the organization or the industry. Taxonomies and ontologies must be able to effectively classify content. To do so, they must include industry vocabularies and any specific thesauri used in the relevant content.
Second, it means that you need a software that enables the automatic tagging of the enterprise content you’re managing, at scale.
Finally, it means that you have tools that enable the customization of this software to map the classification and tagging to the relevant taxonomies and ontologies.
Cogito Studio is the environment which, through a unique combination of semantic rules and machine learning and with the ability to augment the knowledge graph embedded in Cogito, provides a fast and efficient way for supporting the automatic application of taxonomies and ontologies to improve search and enhance knowledge management activities.