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" ... Metadata Managers are a variant of data catalog that usually come into play with organizations that are dealing with differing but conceptually overlapping ontologies. This is typically a problem when you have a data catalog type environment, but because of acquisitions you still end up with multiple ontologies that overlap and need to be translated. In this case, there's usually the goal of creating a single canonical ontology, but because the source ontologies are still in use, an intermediate stage is needed to manage the translation until they can be phased out. These differ from semantic data catalogs because they are managing mappings from one ontology to another, and they are actually a pretty crucial step towards a universal data conversion engine. ... "
" ... The current, human-to-machine web fix involves building APIs to unite those ontologies. Because this costs tens of thousands of dollars per API, only the biggest of big tech (Amazon, Facebook, etc.) can afford to connect data ontologies on a massive scale. The resultant data ecosystems enable tech giants to innovate on Web3 while the average business can't afford the expense. ... "
" ... Traditional ML-driven search engines rely on matching keywords in their algorithms to answer queries. A semantic search engine, on the other hand, enables users to locate information by concepts instead. This is possible because it relies on a more effective information network called knowledge graphs or ontologies. Ontology-driven search engines enhance knowledge sharing and establish common understanding across departments because they are transparent and explainable, and they allow domain experts and administrators to correct, reuse or update data within their models. ... "