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Kleve (úředním názvem do 20. července 1935 Cleve, latinsky Clivia, v místním dialektu Kleef, nizozemsky: Kleef, francouzsky Clèves, anglicky Cleves) je okresní město v dolním Porýní, ve vládním obvodu Düsseldorf, ve spolkové zemi Severní Porýní-Vestfálsko, ležící na německo-nizozemské hranici. Je sídlem zemského okresu Kleve a členem euroregionu…
The analysis highlights Historie, Památky and Osobnosti as prominent areas in the source structure around Kleve.
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 Kleve shows recurring relationship patterns in the source. For example, Kleve → Adolf II, Byl, Byli, Dolní, Hrad, Jan, Jana, Johannisturm, Jülichu, Kleve-Marku, Klevských, KlevskýJan II, KlevskýJan III, KlevskýMarie Habsburská, Kostel Nanebevzetí Panny Marie, Kurhaus, Marie Eleonora Klevská, Markéta, Nejstarší, Neposkvrněného Another extracted example is Kleve → Adolf II, Berg, Berlínem, Braniborskému, Cleve, Dietrich VI, Došlo, Düsseldorfu, Hagschenviertel, Heideberg, Johanna Wilhelma, Jádrem, Jülich, Kliff, Klippe, Koncem, Královcem, Marienstift, Na, Název Kleve. 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.
století město roce města 15 hrad jeho cleve vévodů commons düsseldorf německo severní porýní-vestfálsko něm ii roku kostel zde jan
TTTA extracted 81 structured relationships around Kleve. Examples in this analysis include Kleve → Hustota zalidnění → 546,8 obyv./km² and Kleve → Nadmořská výška → 12 m n. m.. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Kleve | Hustota zalidnění | 546,8 obyv./km² | 1.00 | infobox |
| Kleve | Nadmořská výška | 12 m n. m. | 1.00 | infobox |
| Kleve | Oficiální web | www.kleve.de | 1.00 | infobox |
| Kleve | Označení vozidel | KLE, GEL | 1.00 | infobox |
| Kleve | Počet obyvatel | 53 458 (2023) | 1.00 | infobox |
| Kleve | PSČ | 47533 | 1.00 | infobox |
| Kleve | Rozloha | 97,76 km² | 1.00 | infobox |
| Kleve | Souřadnice | 51°47′15″ s. š., 6°8′7″ v. d. | 1.00 | infobox |
| Kleve | Spolková země | Severní Porýní-Vestfálsko | 1.00 | infobox |
| Kleve | Starosta | Markus Dahmen (nezávislý) | 1.00 | infobox |
| Kleve | Stát | Německo Německo | 1.00 | infobox |
| Kleve | Telefonní předvolba | 02 821 | 1.00 | infobox |
| Kleve | Vládní obvod | Düsseldorf | 1.00 | infobox |
| Kleve | Vznik | 1092 | 1.00 | infobox |
| Kleve | Zemský okres | Kleve | 1.00 | infobox |
The concept neighborhoods around Kleve bring nearby vocabulary together. In this analysis, examples include Jeho, Města and Město. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Kleve, one of the stronger structural bridges in this analysis connects Kleve with Památky. 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 Kleve to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Historie, Památky & Osobnosti, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Kleve · CS edition · Analysis: TopicsToTalkAbout