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Jiří je nejčastější české mužské jméno. K roku 2016 bylo jeho nositeli 296 090 Čechů. Za svou četnost vděčí zejména své velké popularitě od 40. do 80. let 20. století. Jedním z nejsilnějších se stal rok 1952, ve kterém si jméno Jiří odneslo z porodnice celkem 6 553 novorozenců, kteří jsou dosud naživu (stav k 31. prosinci 2013). Dnes již mezi novorozenci…
The analysis highlights Překlady a varianty, Známí nositelé tohoto jména and Statistické údaje as prominent areas in the source structure around Jiří.
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 Jiří shows recurring relationship patterns in the source. For example, Jiří → Adamíra, Aventina, Bartoška, Brabec, Brdečka, Bělohlávek, Chalupa, Císler, Georg Cantor, Grossmann, Gruša, Grygar, Jiří Adamec, Just, Kajínek, Kodet, Koníček, Krampol, KSČMJiří Svoboda, Kulhánek Another extracted example is Jiří → Jirka, Jiřík, Jiříček, Jura, Juráš, Jurášek. 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.
dubna jméno král 23 britský četnost původ roku svátek mužské george 24 první 2016 zemích den jména řecký juraj jurij
TTTA extracted 88 structured relationships around Jiří. Examples in this analysis include Jiří → Podle údajů z roku → 2016 and Jiří → Pořadí podle četnosti → 1.. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Jiří | Podle údajů z roku | 2016 | 1.00 | infobox |
| Jiří | Pořadí podle četnosti | 1. | 1.00 | infobox |
| Jiří | Původ | řecký | 1.00 | infobox |
| Jiří | Svátek | 24. dubna | 1.00 | infobox |
| Jiří | Četnost v Česku | 296 090 | 1.00 | infobox |
| Jiří | related to Domácky | Jirka | 0.60 | section |
| Jiří | related to Domácky | Jiřík | 0.60 | section |
| Jiří | related to Domácky | Jiříček | 0.60 | section |
| Jiří | related to Domácky | Jura | 0.60 | section |
| Jiří | related to Domácky | Juráš | 0.60 | section |
| Jiří | related to Domácky | Jurášek | 0.60 | section |
| Jiří | related to Externí odkazy | Obrázky | 0.60 | section |
The concept neighborhoods around Jiří bring nearby vocabulary together. In this analysis, examples include Jméno, Král and George. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Jiří, one of the stronger structural bridges in this analysis connects Jiří with Známí nositelé tohoto jména. 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 Jiří to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Překlady a varianty, Známí nositelé tohoto jména & Statistické údaje, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Jiří · CS edition · Analysis: TopicsToTalkAbout