This is an important point and one that often gets lost. What’s more, you can customize the output from the knowledge graph so that it reflects the desired ontology (data model) by a client rather than the ontology that is stored internally. Knowledge Graphs Are Not Necessarily Linked Data Your applications do not need to query multiple data sources every time a request is made – the knowledge graph engine queries it once, then periodically checks to see if such information has changed. In essence, the knowledge graph is a global index for persisting information. This does create some changes in thinking about how data is managed. Still, creating an internally consistent representation of your business pays off. This approach takes more work initially – because you do have to do some modeling a priori. However, unlike other enterprise ERPs, the primary distinction within a knowledge graph is that from within, everything connects to everything else, rather than simply being stored enterprise warehouse-like. Then, once contained, it can be transformed to other formats as needed. It can take information from multiple data sources (as well as content originating in the knowledge graph itself) and map to a central information space (called an ontology). They can better coordinate between different data sources, but ultimately service managers only extend the time that a company can take to find a better solution.Ī knowledge graph, on the other hand, is a true integration platform. Services management tools are at best stopgaps. The services era has left many companies with dozens or even hundreds of different data services, each describing the core objects in use within one segment of the business, often times overlapping the same offerings from others. In most organizations, integration is one of the biggest headaches any IT manager will have to face. To that end, in planning for a knowledge graph solution, there are several key points to consider: A Knowledge Graph is an Integration Platform As such, going in, understand that the knowledge graph represents an investment that will produce great rewards, but will take time to grow. A knowledge graph is, in many ways, a garden, something that you plant and carefully tend, with the dividends coming out over years rather than necessarily all at once. Knowledge graphs can make a big difference, but you need to understand these going in and be willing to commit to the project for the long haul. All too often, this can result in knowledge graph solutions sitting largely under-utilized because no one can figure out what it’s for. However, just as with any data solution, there are times when, after the initial acquisition of a knowledge graph solution, companies and IT managers, particularly, wonder what exactly it is they have acquired. While Knowledge Graph hype is nowhere near as loud as AI hype, there is no question that more and more organizations are turning to knowledge graphs to solve real-world problems.
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