In the past two decades, the use of ontologies has grown accompanied by a diversity in ontological representations and applications to more comprehensive domains. Knowledge engineers have found it expeditious to break down large (monolithic) ontologies to work with smaller fragments. Ontology modularization is the process of extracting a fragment, or "module", from an ontology, based on predefined requirements. Due to both the diversity in ontological representations and motivations for modularizing, the body of research on ontology modularization techniques has become extremely large and may be intimidating to the novice ontology researcher. The objective of the paper is to present a comprehensive, albeit high-level, review of ontology modularization techniques. A systematic literature review covering January 1st 2000 to July 31st 2020 was performed to find and classify papers on ontology modularization techniques. The techniques exhibiting certain properties with respect to several features were assessed, and the modularization techniques were classified with a multi-dimensional perspective. The classifications are intended to guide one to a suitable modularization process in accordance with the requirements. The limitations of ontology modularization techniques are highlighted in the conclusion, and characteristics of a desirable framework for an ontology representation that would be best-suited for modularization are presented.

This paper formalizes the graphical modularization technique, View Traversal, for an ontology-based system represented using the Domain Information System (DIS). Our work is motivated by the need for autonomous agents, within an ontology-based system, to automatically create their own views of the ontology to address the problems of ontology evolution and data integration found in an enterprise setting. Through DIS, we explore specific ontologies that give Cartesian perspectives of the domain, which allows modularization to be a means for agents to extract views of specific combinations of data. The theory of ideals from Boolean algebra is used to formalize a module. Then, with the use of homomorphisms, the quantity of knowledge within the module can be measured. More specifically, through the first isomorphism theorem, we establish that the loss of information is quantified by the kernel of the homomorphism. This constitutes a foundational step towards theories related to reasoning on partial domain knowledge, and is important for applications where an agent needs to quickly extract a view that contains a specific set of knowledge.