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Amur: Historie, Průběh toku & Využití
Amur (rusky Амур, čínsky znaky 黑龍江, mandžusky Sahaliyan Ula, mongolsky Хара-Мурэн) je řeka ve Východní Asii, která tvoří přirozenou hranici mezi Ruskem (Zabajkalský kraj, Amurská oblast, Židovská autonomní oblast) a ČLR (Vnitřní Mongolsko a Chej-lung-ťiang) a na dolním toku protéká jen Ruskem (Chabarovský kraj). V mongolštině a mandžuštině její jméno…
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Amur topic overview
The analysis highlights Historie, Průběh toku and Využití as prominent areas in the source structure around Amur.
Related topics
121
Source areas
6
Connected nodes
131
Extracted relationships
103
Related term clusters
21
Bridge connections
131
What this topic covers Research coverage
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.
Historie · 33 topics
Průběh toku · 33 topics
Overview · 26 topics
Využití · 14 topics
Vodní stav · 12 topics
Amur v pojmenováních · 3 topics
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.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Délka toku
2824 km
OpenStreetMap
OSM, WMF
Plocha povodí
1 855 000 km²
Průměrný průtok
10800 m³/s
Světadíl
Asie
Tematické okruhy
Prozkoumejte skupiny témat propojených ve zdrojovém textu. Vyberte si libovolné téma; okruhy nemají určené pořadí.
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
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.
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Advanced semantic analysis
How Amur connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Amur shows recurring relationship patterns in the source. For example, Amur → Amguň, Amu-er-che, Amurem, Amursko-zejskou, Amurský, Amuru, Arguň, Bezumnyj, Blagověščensku, Boloň, Bureja, Chabarovsku, Dolnoamurskou, Dolní, Horní, Ilchuri Alin, Jezera, Kizi, Korsakovské, Koryto Another extracted example is Amur → Akademie, Aldan, Amurská, Amuru, Džürčeni, Jakutska, Lenu, Mandžuové, Na Amuru, Nanajci, Nivchové, Něvelskij, Oblast, První Rusové, Přiamuří Jerofej Pavlovič Chabarov, Původními, Sachalin, SSSR, Ulčové, Vasilije Daniloviče Pojarkova. Use these groups to spot repeated connection types before inspecting the individual relationships.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 103 structured relationships around Amur. Examples in this analysis include Amur → Délka toku → 2824 km and Amur → OpenStreetMap → OSM, WMF. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Amur
Délka toku
2824 km
1.00
infobox
Amur
OpenStreetMap
OSM, WMF
1.00
infobox
Amur
Plocha povodí
1 855 000 km²
1.00
infobox
Amur
Průměrný průtok
10800 m³/s
1.00
infobox
Amur
Světadíl
Asie
1.00
infobox
Amur
related to Amur v pojmenováních
Amurská
0.60
section
Amur
related to Amur v pojmenováních
Ruské
0.60
section
Amur
related to Amur v pojmenováních
Amur Chabarovsk
0.60
section
Amur
related to Amur v pojmenováních
Stejně
0.60
section
Amur
related to Amur v pojmenováních
Jako
0.60
section
Amur
related to Historie
Původními
0.60
section
Amur
related to Historie
Amuru
0.60
section
No triples extracted yet.
Related concept clusters Related term clusters
Related term clusters group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Amur bring nearby vocabulary together. In this analysis, examples include Amurská, Oblast and Chej-lung-ťiang. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Amur
Amurská
Oblast
Chej-lung-ťiang
Commons
Mongolsko
Povodí
Toku
Řeku
Šilka
Člr
Řek
Arguň
amur
Amurská
Oblast
Chej-lung-ťiang
Commons
Mongolsko
Povodí
Toku
Řeku
Šilka
Člr
Řek
Arguň
nikolajevskem na amuru
Člr
Povodí
Km
Rusko
Letech
Řek
Chabarovsku
Oblast
Ústí
Chej-lung-ťiang
Commons
Mongolsko
komsomolsk na amuru
Člr
Povodí
Km
Rusko
Letech
Řek
Chabarovsku
Oblast
Ústí
Chej-lung-ťiang
Commons
Mongolsko
amur chabarovsk
Amurská
Oblast
Rusko
Chej-lung-ťiang
Commons
Mongolsko
Povodí
Toku
Řeku
Šilka
Člr
Řek
amur v pojmenováních
Amurská
Oblast
Chej-lung-ťiang
Commons
Mongolsko
Povodí
Toku
Řeku
Šilka
Člr
Řek
Arguň
amurská oblast
Chej-lung-ťiang
Mongolsko
Oblast
Člr
Commons
Rusko
Chabarovsk
Protéká
Toku
Řeku
Šilka
Část
židovská autonomní oblast
Chej-lung-ťiang
Mongolsko
Člr
Commons
Rusko
Toku
Chabarovsk
Protéká
Část
Řeku
Šilka
Řek
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Amur, one of the stronger structural bridges in this analysis connects Amur with Průběh toku. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Amur
Nodes132
Edges131
Triples103
Avg. degree1.98
Density0.015152
Components1
Source & methodology
TTTA analyzes the structure around Amur to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Historie, Průběh toku & Využití, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Amur · CS edition · Analysis: TopicsToTalkAbout
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