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Mužské vlastní jméno Rafael pochází z hebrejského jména רפאל Refáél, což znamená „Bůh uzdravuje“. V starozákonní knize Tóbijáš se takto jmenuje anděl, který provází mladého Tóbijáše na cestě do Médie, podle církevní tradice jeden ze (tří) archandělů.
The analysis highlights Významné osoby se jménem Rafael, Statistické údaje and Jmeniny as prominent areas in the source structure around Rafael.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. 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.
The extracted context around Rafael shows recurring relationship patterns in the source. For example, Rafael → Bývá, Jeho, Nadal Parera, Objevuje, Rafael Kubelík, Santi, Tobiáše, Tóbijáš, Tóbita, Uzdravuje Another extracted example is Rafael → Anglicky, Rafael/RafelNěmecky, RafaelRusky, RafałPortugalsky, Raffaele, RaffaelloJaponsky, RaphaelArménsky, RaphaelPolsky, RaphaëlItalsky. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
jméno 29 září četnost mužské anděl archandělů příjmení jména uzdravuje tóbijáš provází původ údaje čr svátek pořadí hebrejského starozákonní tóbijáše
TTTA extracted 31 structured relationships around Rafael. Examples in this analysis include Rafael → Podle údajů z roku → 2016 and Rafael → Pořadí podle četnosti → 672.. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Rafael | Podle údajů z roku | 2016 | 1.00 | infobox |
| Rafael | Pořadí podle četnosti | 672. | 1.00 | infobox |
| Rafael | Původ | hebrejský | 1.00 | infobox |
| Rafael | Svátek | 29. září | 1.00 | infobox |
| Rafael | Četnost v Česku | 311 | 1.00 | infobox |
| Rafael | related to Externí odkazy | Obrázky | 0.60 | section |
| Rafael | related to Externí odkazy | Wikimedia Commons | 0.60 | section |
| Rafael | related to Jmeniny | Archandělů Michaela | 0.60 | section |
| Rafael | related to Jmeniny | Gabriela | 0.60 | section |
| Rafael | related to Jmeniny | Rafaela | 0.60 | section |
| Rafael | related to Příjmení | Ignác Václav Rafael | 0.60 | section |
| Rafael | related to Příjmení | František Rafael | 0.60 | section |
The concept neighborhoods around Rafael bring nearby vocabulary together. In this analysis, examples include Příjmení, Původ and Svátek. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Rafael, one of the stronger structural bridges in this analysis connects Rafael with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Rafael to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Významné osoby se jménem Rafael, Statistické údaje & Jmeniny, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Rafael · CS edition · Analysis: TopicsToTalkAbout