A Survey on Ontology Operations Techniques

Ontologies are increasingly seen as key factors in enabling interoperability between heterogeneous systems and semantic web applications and are emerging as representative techniques for overlapping complementary context domains. A single ontology is no longer sufficient to support the tasks predicted by a distributed environment such as the Semantic Web; several ontologies for many applications are necessary. Different ontological tools have different representations of data and concept operations with respect to their input. Its functions and informational structures also differ depending on its tools and processes. Currently very few investigation documents provide an in-depth discussion of these technologies and their applications. In this article, we discuss various sophisticated ontological tools with their various internal processes and algorithms. Mapping, aligning, or merging ontologies creates an identification layer that allows different applications to access the resulting ontology and then share its information, of course, while preserving the semantics it contains. For integrating large ontologies, automatic matches become an essential solution. However, the large ontology matching process presents high spatial and temporal complexities. Therefore, for a tool to efficiently and accurately match this large ontology within limited computing resources, it must have techniques that can significantly reduce the high spatio-temporal complexities associated with the existential matching process. These processes provide an important basis for many other processes such as translation, reconciliation, coordination and negotiation between ontologies.