漫談·知識圖譜

1、關係抽取

1.1 專家系統

• 以兩家公司有 投資 關係爲例

key_words = ["收購","競拍","轉讓","擴張","併購","注資","整合","併入","競購","競買","支付","收購價","收購價格","承購","購得","購進",
             "購入","買進","買入","贖買","購銷","議購","函購","函售","拋售","售賣","銷售","轉售"]

1.2 句法依存

• 根據人的說話習慣來抽取
• 主謂賓

if 'SBV' in child_dict and 'VOB' in child_dict:
	r = words[index]
	e1 = complete_e(words, postags, child_dict_list, child_dict['SBV'][0])
	e2 = complete_e(words, postags, child_dict_list, child_dict['VOB'][0])
	svos.append([e1, r, e2])

1.3 遠程監督

2、知識圖譜的常見表示（Neo4j）

2.1 數據

字段	描述
Person_a	實體a（人名，如：賈寶玉）
Person_b	實體b（人名，如：薛寶釵）
Relation	實體間關係（丈夫）
Family_a	實體a的家族（賈家榮國府）
Family_b	實體b的家族（薛家）

2.2 導入

with open("./raw_data/relation.txt") as f:
    for line in f.readlines():
        rela_array=line.strip("\n").split(",")
        print(rela_array)
        graph.run("MERGE(p: Person{cate:'%s',Name: '%s'})"%(rela_array[3],rela_array[0]))
        graph.run("MERGE(p: Person{cate:'%s',Name: '%s'})" % (rela_array[4], rela_array[1]))
        graph.run(
            "MATCH(e: Person), (cc: Person) \
            WHERE e.Name='%s' AND cc.Name='%s'\
            CREATE(e)-[r:%s{relation: '%s'}]->(cc)\
            RETURN r" % (rela_array[0], rela_array[1], rela_array[2], rela_array[2])
        )

2.3 查詢

match(p )-[r]->(n:Person{Name:'%s'}) return  p.Name,r.relation,n.Name,p.cate,n.cate Union all match(p:Person {Name:'%s'}) -[r]->(n) return p.Name, r.relation, n.Name, p.cate, n.cate

2.4 效果

• 賈寶玉 的關係圖譜
  
• 李紈 的關係圖譜
  
• 林黛玉 的關係圖譜
  

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