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Svilajnac: Historie, Obyvatelstvo & Turismus
Svilajnac je město a centrum stejnojmenné opštiny v centrálním Srbsku, asi 100 km jihovýchodně od Bělehradu. Město je součástí okruhu Pomoravlje, nachází se na řekách Velká Morava a Resava. V městě Svilajnac podle sčítání lidu, domů a bytů z roku 2011 žilo 9 131 obyvatel. Svilajnac je považován za centrum Resavského regionu. Součástí opštiny je kromě…
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Svilajnac topic overview
The analysis highlights Historie, Obyvatelstvo and Turismus as prominent areas in the source structure around Svilajnac.
Related topics
18
Source areas
4
Connected nodes
22
Extracted relationships
58
Related term clusters
6
Bridge connections
22
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 · 11 topics
Overview · 5 topics
Obyvatelstvo · 1 topics
Turismus · 1 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.
Etnické složení
Srbové
Hustota zalidnění
326,1 obyv./km²
Nadmořská výška
103 m n. m.
Náboženské složení
Pravoslaví
Oficiální web
www.svilajnac.rs
Okruh
Pomoravský
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.
You can skip this section if you’re here for content ideas and keyword inspiration.
Advanced semantic analysis
How Svilajnac 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 Svilajnac shows recurring relationship patterns in the source. For example, Svilajnac → Díky, Jižní Slované, Julius Draškoci, Miloš Obrenović, Miloše, Moesie, Město, Nejvýznamnějším, Největší, Nemocnice, Nikoly, Oblast Resavy, První, Předpokladem, Resavy, Srbska, Stefana Lazareviće, Stevan Sindjelić, Svilajnci, Thrákové Another extracted example is Svilajnac → Baćica, Bělehradu, Celým, Hum, Hustota, Nížiny, Oblast, Oblast Svilajnce, Průměrná, Průměrný, Resava, Resavy, Srbska, Svilajnce, Svilajncem, Velké Moravy. Use these groups to spot repeated connection types before inspecting the individual relationships.
Svilajnac
Top relations
related to Historie· 23
Svilajnac → Díky, Jižní Slované, Julius Draškoci, Miloš Obrenović, Miloše, Moesie, Město, Nejvýznamnějším, Největší, Nemocnice, Nikoly, Oblast Resavy, První, Předpokladem, Resavy, Srbska, Stefana Lazareviće, Stevan Sindjelić, Svilajnci, Thrákové
related to Geografie· 16
Svilajnac → Baćica, Bělehradu, Celým, Hum, Hustota, Nížiny, Oblast, Oblast Svilajnce, Průměrná, Průměrný, Resava, Resavy, Srbska, Svilajnce, Svilajncem, Velké Moravy
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 58 structured relationships around Svilajnac. Examples in this analysis include Svilajnac → Etnické složení → Srbové and Svilajnac → Hustota zalidnění → 326,1 obyv./km². The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Svilajnac
Etnické složení
Srbové
1.00
infobox
Svilajnac
Hustota zalidnění
326,1 obyv./km²
1.00
infobox
Svilajnac
Nadmořská výška
103 m n. m.
1.00
infobox
Svilajnac
Náboženské složení
Pravoslaví
1.00
infobox
Svilajnac
Oficiální web
www.svilajnac.rs
1.00
infobox
Svilajnac
Okruh
Pomoravský
1.00
infobox
Svilajnac
Označení vozidel
SV
1.00
infobox
Svilajnac
Počet obyvatel
9 131
1.00
infobox
Svilajnac
PSČ
35 210
1.00
infobox
Svilajnac
Rozloha
28 km²
1.00
infobox
Svilajnac
Souřadnice
44°14′0″ s. š., 21°11′41″ v. d.
1.00
infobox
Svilajnac
Stát
Srbsko Srbsko
1.00
infobox
Svilajnac
Telefonní předvolba
(+381) 035
1.00
infobox
Svilajnac
Časové pásmo
UTC+1
1.00
infobox
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 Svilajnac bring nearby vocabulary together. In this analysis, examples include Roce, Znak and Kolem. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Svilajnac
Roce
Znak
Kolem
Rozvoj
Století
Svilajnci
svilajnac
Roce
Znak
Kolem
Rozvoj
Století
Svilajnci
opštiny
Bělehradu
Součástí
Km
Svilajnac
Města
bělehradu
Km
Centrum
Opštiny
Město
Svilajnac
obyvatelstvo
Znak
Kolem
Roce
Svilajnci
resava
Součástí
Rozvoj
Města
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 Svilajnac, one of the stronger structural bridges in this analysis connects Svilajnac with Historie. 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.
Svilajnac
Nodes23
Edges22
Triples58
Avg. degree1.91
Density0.086957
Components1
Source & methodology
TTTA analyzes the structure around Svilajnac to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Historie, Obyvatelstvo & Turismus, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
