How do you cope with ontology evolution and dynamics in AI environments?

1 Track and manage changes

Track and manage the changes that occur in the ontology. This involves identifying the sources and types of changes, such as additions, deletions, modifications, or merges of concepts, relations, or axioms. It also involves documenting the rationale, context, and effects of the changes, such as the motivation, scope, and impact of the changes on the ontology and its users. Tracking and managing changes can help maintain the consistency, quality, and usability of the ontology over time.

2 Align and integrate ontologies

Align and integrate ontologies that share some commonality or overlap in the domain of knowledge. This can help avoid duplication, inconsistency, or incompleteness of the ontological information, and leverage the existing knowledge and resources from different sources. Aligning and integrating ontologies involves finding the correspondences, similarities, or differences between the concepts, relations, or constraints of the ontologies, and establishing the mappings, transformations, or rules to relate them. Aligning and integrating ontologies can help you achieve interoperability, compatibility, and synergy among the ontologies and their applications.

4 Evaluate and improve ontologies

Evaluate and improve the ontologies based on the feedback, results, or outcomes of the changes and applications. This can help assess the quality, usefulness, and relevance of the ontologies, and identify the strengths, weaknesses, or gaps of the ontological information. Evaluating and improving ontologies involves applying various criteria, metrics, or methods to measure and compare the ontologies, such as the syntactic, semantic, pragmatic, or social aspects of the ontologies. Evaluating and improving ontologies can enhance the value, impact, and sustainability of the ontologies.

5 Learn and discover ontologies

Learn and discover new ontologies or ontological information from the data, text, or other sources of knowledge. This can help enrich, update, or extend the ontologies with new concepts, relations, or constraints that may not be explicitly or adequately represented in the existing ontologies. Learning and discovering ontologies involves applying various techniques, tools, or algorithms to extract, analyze, or infer the ontological information from the data, text, or other sources, such as the natural language processing, machine learning, or data mining techniques. Learning and discovering ontologies can expand, refine, or innovate the ontologies.

6 Collaborate and communicate ontologies

Collaborate and communicate with other ontology developers, users, or stakeholders in the domain of knowledge. This can help share, exchange, or integrate the ontological information, and coordinate, harmonize, or reconcile the changes or variations in the ontologies. Collaborating and communicating ontologies involves using various platforms, formats, or standards to represent, store, or access the ontologies, such as the web-based, XML-based, or RDF-based platforms, formats, or standards. Collaborating and communicating ontologies can help foster the community, consensus, or trust among the ontology developers, users, or stakeholders.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?