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Jaroslav je mužské jméno. Podle českého občanského kalendáře má svátek 27. dubna.
The analysis highlights Známí Jaroslavové, Statistické údaje and Známé literární a filmové postavy as prominent areas in the source structure around Jaroslav.
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 Jaroslav shows recurring relationship patterns in the source. For example, Jaroslav → Bašta, Břeský, Dietl, Dudek, Durych, Dušek, Emil Frída, Filip, Flegr, Foglar, Foldyna, Hašek, Heyrovský, Himr, Holík, Hrbáček, Ježek, Jára Kohout, Kaczyński, Krejčí Another extracted example is Jaroslav → Bělorusky, Hieroslaus, Iruslb, JarekRusky, Jarislaus, Jarosius, Jaroslao, Jaroslaus, JaroslausPolsky, JaroslavasMaďarsky, JaroslavUkrajinsky, Jarossius, JaroszlávNěmecky, Jarosław, Jaryslovas, JerezlausLitevsky, Jeroslai, Jeroslao, Jeroslaus, Jerusschio. 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 27 dubna mužské četnost svátek jména jára český herec zpěvák ze jaroslaus jeroslaus původ údaje čr 1999 pořadí údajů
TTTA extracted 81 structured relationships around Jaroslav. Examples in this analysis include Jaroslav → Podle údajů z roku → 2016 and Jaroslav → Pořadí podle četnosti → 8.. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Jaroslav | Podle údajů z roku | 2016 | 1.00 | infobox |
| Jaroslav | Pořadí podle četnosti | 8. | 1.00 | infobox |
| Jaroslav | Původ | slovanský | 1.00 | infobox |
| Jaroslav | Svátek | 27. dubna | 1.00 | infobox |
| Jaroslav | Četnost v Česku | 172 785 | 1.00 | infobox |
| Jaroslav | related to Další využití jména | N-S | 0.60 | section |
| Jaroslav | related to Další využití jména | Krycí | 0.60 | section |
| Jaroslav | related to Další využití jména | Sedloňova | 0.60 | section |
| Jaroslav | related to Externí odkazy | Obrázky | 0.60 | section |
| Jaroslav | related to Externí odkazy | Wikimedia Commons Slovníkové | 0.60 | section |
| Jaroslav | related to Externí odkazy | Wikislovníku | 0.60 | section |
| Jaroslav | related to Jaroslav v jiných jazycích | Bělorusky | 0.60 | section |
The concept neighborhoods around Jaroslav bring nearby vocabulary together. In this analysis, examples include Jméno, Herec and Jaroslaus. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Jaroslav, one of the stronger structural bridges in this analysis connects Jaroslav with Známí Jaroslavové. 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 Jaroslav to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Známí Jaroslavové, Statistické údaje & Známé literární a filmové postavy, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Jaroslav · CS edition · Analysis: TopicsToTalkAbout