The goal of OntoPop is to make ontologies easier to understand and more accessible to a broader range of users beyond information and data architects.
OntoPop can be deployed in any context where ontologies are used, enabling your users to visualise, search, query, explore and manage those ontologies. Exciting example use-cases of ontologies include:
- Semantic Web - providing a common and consistent "language" by which different users and IT systems can understand a given domain, and each other. OntoPop enables users to visualise, search, explore and manage information in that domain, and to understand how information is linked together.
- Common Data Models - building ontology-based conceptual data models that act as a reference for all of your subsequent logical and physical data models and physical system interfaces. OntoPop enables you to make your ontology-based conceptual data model accessible to non-technical users, thus significantly improving data visibility and accessibility by providing an interactive map of all your linked data assets across your entire organisation.
- Classification Systems - combining machine learning with ontologies enables organisations to build highly accurate predictive classification systems that can be applied to analytical use cases such as classifying huge volumes of text and other structured and unstructured data. OntoPop enables both human analysts and automated machine learning systems to iteratively refine and improve the ontology (and hence the quality of classification predictions) via OntoPop's APIs.