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{"id":65810,"date":"2022-01-26T15:26:44","date_gmt":"2022-01-26T15:26:44","guid":{"rendered":"https:\/\/aijourn.com\/?p=65810"},"modified":"2024-04-02T15:45:45","modified_gmt":"2024-04-02T15:45:45","slug":"knowledge-as-a-service-kaas-in-2022","status":"publish","type":"post","link":"https:\/\/aijourn.com\/knowledge-as-a-service-kaas-in-2022\/","title":{"rendered":"Knowledge-As-A-Service (KaaS) In 2022"},"content":{"rendered":"\n

Knowledge graphs hit the peak<\/a> of their Gartner hype cycle in 2020. And much as KG teams celebrated the rise to prominence, \u201chype\u201d carries an implicit risk. What if the touted promise never emerges? After all, network-based databases (including graph DBs) have been in use since the 1960s, just largely confined to academic settings. What\u2019s working for graph adoption today?

For starters, what do we mean by the \u201cknowledge\u201d in knowledge-as-a-service?

In common parlance, knowledge has come to be known as information<\/strong> and context<\/strong>. This is one of the roots of the term \u201cknowledge graph.\u201d While a graph is simply a database structure composed of nodes (entities) and edges (relationships), knowledge graphs are populated to provide information and context.\u00a0<\/p>\n\n\n\n

To jump to the present day, knowledge-as-a-service has grown an ecosystem of players providing services at a range of layers.\u00a0<\/p>\n\n\n\n

Build your own knowledge graph: <\/p>\n\n\n\n