143 lines
4.2 KiB
Plaintext
143 lines
4.2 KiB
Plaintext
// 圐圙文化网络 - Neo4j Cypher 查询集合
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// ===== 基础查询 =====
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// 1. 查看所有词汇节点
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MATCH (w:Word)
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RETURN w.name, w.category, w.meaning, w.region, w.dynasty
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ORDER BY w.category, w.name;
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// 2. 查看所有关系类型
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MATCH ()-[r]-()
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RETURN DISTINCT type(r) as relationship_types;
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// ===== 圐圙词根网络分析 =====
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// 3. 以"昆仑"为中心的词汇网络
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MATCH (center:Word {name: '昆仑'})-[r]-(connected:Word)
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RETURN center, r, connected;
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// 4. 查找所有音转关系
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MATCH (source:Word)-[r:SOUND_SHIFT]->(target:Word)
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RETURN source.name as 源词, target.name as 目标词, r.type as 关系类型;
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// 5. 查找音转路径（最多3步）
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MATCH path = (start:Word {name: '昆仑'})-[:SOUND_SHIFT*1..3]-(end:Word)
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RETURN
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start.name as 起点,
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[node in nodes(path) | node.name] as 路径,
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end.name as 终点,
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length(path) as 步数
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ORDER BY 步数;
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// ===== 语义分析 =====
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// 6. 按类别分组的词汇分布
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MATCH (w:Word)
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RETURN w.category as 类别, collect(w.name) as 词汇列表, count(w) as 数量
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ORDER BY 数量 DESC;
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// 7. 按朝代分组的词汇演化
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MATCH (w:Word)
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RETURN w.dynasty as 朝代, collect(w.name) as 词汇, count(w) as 数量
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ORDER BY
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CASE w.dynasty
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WHEN '先秦' THEN 1
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WHEN '汉' THEN 2
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WHEN '唐' THEN 3
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WHEN '宋' THEN 4
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WHEN '元' THEN 5
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WHEN '明' THEN 6
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WHEN '清' THEN 7
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ELSE 8
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END;
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// 8. 按地区分布的词汇地理分析
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MATCH (w:Word)
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WHERE w.region IS NOT NULL
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RETURN w.region as 地区, collect(w.name) as 词汇, count(w) as 数量
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ORDER BY 数量 DESC;
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// ===== 高级分析查询 =====
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// 9. 查找"圐圙"的完整关联网络
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MATCH (kulue:Word {name: '圐圙'})-[r*1..2]-(related:Word)
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RETURN kulue, r, related;
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// 10. 查找同时具有音转和语义关系的词汇对
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MATCH (w1:Word)-[:SOUND_SHIFT]-(w2:Word)
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MATCH (w1)-[:SEMANTIC]-(w2)
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RETURN w1.name, w2.name, '既有音转又有语义关系' as 关系特征;
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// 11. 查找每个类别中的核心词汇（关联最多的）
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MATCH (w:Word)-[r]-(connected:Word)
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WITH w, count(r) as connections
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WHERE connections > 1
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RETURN w.category as 类别, w.name as 核心词汇, w.meaning as 含义, connections as 关联数
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ORDER BY 类别, connections DESC;
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// 12. 查找跨类别的关联模式
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MATCH (w1:Word)-[r]-(w2:Word)
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WHERE w1.category <> w2.category
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RETURN w1.category as 类别1, w2.category as 类别2, count(r) as 关联数
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ORDER BY 关联数 DESC;
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// ===== 历史演化分析 =====
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// 13. 三个帝国的词汇分布
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MATCH (w:Word)
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WITH w,
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CASE
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WHEN w.dynasty IN ['先秦', '秦汉', '汉', '三国', '南北朝'] THEN '长城帝国'
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WHEN w.dynasty IN ['隋唐', '隋', '唐', '五代', '辽', '金', '夏', '宋'] THEN '运河帝国'
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WHEN w.dynasty IN ['元', '明', '清', '民国'] THEN '圐圙帝国'
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ELSE '其他'
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END as empire
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RETURN empire as 帝国, collect(w.name) as 词汇, count(w) as 数量;
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// 14. 查找词汇的时空分布模式
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MATCH (w:Word)
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RETURN w.dynasty as 朝代, w.region as 地区, collect(w.name) as 词汇
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ORDER BY w.dynasty, w.region;
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// ===== 可视化查询 =====
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// 15. 生成完整的圐圙网络图（用于可视化）
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MATCH (w:Word)-[r]-(connected:Word)
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RETURN w, r, connected;
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// 16. 生成核心概念的星形图
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MATCH (core:Word {category: '核心'})-[r]-(related:Word)
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RETURN core, r, related;
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// 17. 生成音转关系的有向图
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MATCH (source:Word)-[r:SOUND_SHIFT]->(target:Word)
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RETURN source, r, target;
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// ===== 数据统计 =====
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// 18. 网络统计信息
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MATCH (w:Word)
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WITH count(w) as total_words
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MATCH ()-[r]-()
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WITH total_words, count(r)/2 as total_relationships
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MATCH (w:Word)-[r]-(connected:Word)
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WITH total_words, total_relationships, w, count(r) as degree
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RETURN
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total_words as 总词汇数,
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total_relationships as 总关系数,
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avg(degree) as 平均度数,
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max(degree) as 最大度数,
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min(degree) as 最小度数;
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// 19. 查找孤立节点（没有关系的词汇）
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MATCH (w:Word)
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WHERE NOT (w)-[]-()
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RETURN w.name as 孤立词汇, w.category as 类别;
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// 20. 查找关联度最高的词汇（网络中心）
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MATCH (w:Word)-[r]-(connected:Word)
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WITH w, count(r) as degree
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ORDER BY degree DESC
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LIMIT 5
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RETURN w.name as 中心词汇, w.meaning as 含义, degree as 关联度; |