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KMS, an abbreviation of Knowledge Management System, was a commercial second generation hypermedia system, originally created as a successor for the early hypermedia system ZOG. KMS was developed by Don McCracken and Rob Akscyn of Knowledge Systems, a 1981 spinoff from the Computer Science Department of Carnegie Mellon University.
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Explore the main themes, entities and connections around KMS (hypertext). Start with the topic map, then use the sections below for research and deeper semantic analysis.
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the full topic structure. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
See the strongest relationship patterns around the current topic before diving into the raw triples.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
kms frames frame programs system links users text one items hypermedia always another also functionality current annotation knowledge akscyn originally
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| presentations | instance of | KMS was intended to represent all forms of explicit 'knowledge artifacts' | 0.80 | text |
| documents | instance of | KMS was intended to represent all forms of explicit 'knowledge artifacts' | 0.80 | text |
| databases | instance of | KMS was intended to represent all forms of explicit 'knowledge artifacts' | 0.80 | text |
| and software programs | instance of | KMS was intended to represent all forms of explicit 'knowledge artifacts' | 0.80 | text |
| as well as common forms of electronic communication | instance of | KMS was intended to represent all forms of explicit 'knowledge artifacts' | 0.80 | text |
| documents | instance of | opting instead for larger aggregates | 0.80 | text |
| programs to be structured as hierarchies | instance of | opting instead for larger aggregates | 0.80 | text |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.