知識圖譜的自然語言查詢和關(guān)鍵詞查詢

章 緒論·································．1
1．1 研究背景及意義··················．1
1．2 研究現(xiàn)狀···························．3
1．2．1 知識圖譜自然語言查詢的
研究現(xiàn)狀························3
1．2．2 知識圖譜關(guān)鍵詞查詢的
研究現(xiàn)狀························4
1．3 存在的關(guān)鍵問題··················．5
1．4 研究內(nèi)容及創(chuàng)新點···············．7
1．5 本書組織結(jié)構(gòu)·····················10
第2 章 自然語言查詢和關(guān)鍵詞查詢的
相關(guān)研究···························12
2．1 知識圖譜的自然語言查詢······12
2．1．1 語義關(guān)系識別················．12
2．1．2 自然語言聚集查詢···········．13
2．1．3 查詢映射·····················．14
2．1．4 多樣化的自然語言查詢······．15
2．2 知識圖譜的關(guān)鍵詞查詢·········16
2．2．1 模式圖························．16
2．2．2 多樣化的關(guān)鍵詞查詢········．17
2．3 兩種查詢共用的基礎(chǔ)技術(shù)······19
2．3．1 實體識別和實體鏈接········．19
2．3．2 解釋詞典·····················．19
2．4 眾包—輔助語義關(guān)系識別規(guī)則
挖掘·································20
2．5 知識圖譜的其他非結(jié)構(gòu)化
查詢方式···························21
2．5．1 交互式查詢···················．21
2．5．2 實例查詢和樣例查詢········．22
第3 章 基于眾包的自然語言查詢中
語義關(guān)系識別規(guī)則挖掘·········23
3．1 問題描述及創(chuàng)新點···············23
3．2 眾包模型···························24
3．2．1 迭代模型和并行模型········．25
3．2．2 迭代式并行模型和
并行式迭代模型·············．25
3．2．3 帶反饋的并行式迭代模型···．26
3．3 生成語義關(guān)系數(shù)據(jù)集和
依賴結(jié)構(gòu)數(shù)據(jù)集··················27
3．3．1 眾包模型標(biāo)記語義關(guān)系·····．27
3．3．2 Stanford Parser 生成依賴
結(jié)構(gòu)··························．27
3．4 挖掘語義關(guān)聯(lián)規(guī)則···············28
3．4．1 挖掘語義關(guān)聯(lián)規(guī)則的算法···．28
3．4．2 算法MSAR 的復(fù)雜度·······．30
3．5 實驗結(jié)果及分析—眾包模型··31
3．5．1 實驗數(shù)據(jù)及評估標(biāo)準(zhǔn)········．31
3．5．2 迭代模型和并行模型········．32
3．5．3 迭代式并行模型和并行式
迭代模型·····················．33
3．5．4 帶反饋的并行式迭代模型···．35
3．6 實驗結(jié)果及分析—語義關(guān)聯(lián)
規(guī)則·································36
3．7 語義關(guān)系識別·····················38
3．7．1 語義關(guān)系識別的算法········．38
3．7．2 算法SRR 的復(fù)雜度··········．39
3．7．3 實驗結(jié)果及分析—語義關(guān)系
識別··························．39
3．8 本章小結(jié)···························40
第4 章 知識圖譜的自然語言聚集
查詢·································42
4．1 問題描述及創(chuàng)新點···············42
4．2 查詢流程···························45
4．3 查詢理解···························45
4．3．1 意圖解釋·····················．45
4．3．2 依賴結(jié)構(gòu)分類················．46
4．3．3 從依賴結(jié)構(gòu)中識別意圖解釋·．47
4．3．4 查詢理解的優(yōu)化·············．49
4．3．5 算法AIII 的復(fù)雜度··········．49
4．4 構(gòu)建基本圖模式··················50
4．4．1 擴展的解釋詞典ED ·········．50
4．4．2 短語映射·····················．51
4．4．3 謂詞-類型鄰近集PT ·········．51
4．4．4 謂詞-謂詞鄰近集PP ·········．53
4．4．5 語義關(guān)系映射················．53
4．4．6 算法SRM 的復(fù)雜度·········．55
4．4．7 構(gòu)建基本圖模式BGP········．56
4．4．8 算法BBGP 的復(fù)雜度········．57
4．5 將基本圖模式翻譯為
SPARQL 語句·····················58
4．5．1 數(shù)值型謂詞···················．58
4．5．2 翻譯基本圖模式·············．59
4．5．3 翻譯聚集·····················．59
4．5．4 算法TA 的復(fù)雜度···········．61
4．6 實驗結(jié)果及分析··················61
4．6．1 實驗數(shù)據(jù)集···················．61
4．6．2 各階段的優(yōu)化能力···········．61
4．6．3 算法的有效性················．63
4．6．4 與現(xiàn)有算法對比·············．65
4．6．5 回答錯誤的原因·············．66
4．7 相關(guān)問題及解決方案············67
4．8 本章小結(jié)···························69
第5 章 知識圖譜的自然語言查詢—
SPARQL 和關(guān)鍵詞··············70
5．1 問題描述及創(chuàng)新點···············70
5．2 查詢流程···························71
5．3 SPARQL 語句的生成過程······72
5．4 查詢分解···························73
5．4．1 查詢理解階段················．73
5．4．2 查詢映射階段················．74
5．4．3 執(zhí)行SPARQL 階段··········．74
5．4．4 查詢分解算法················．75
5．4．5 算法DQ 的復(fù)雜度···········．76
5．5 構(gòu)建關(guān)鍵詞索引··················77
5．5．1 算法QUKI ···················．77
5．5．2 算法QUKI 的復(fù)雜度········．78
5．6 聚合SPARQL 結(jié)果子圖和
關(guān)鍵詞查詢························78
5．6．1 算法CSK ····················．78
5．6．2 算法CSK 的復(fù)雜度·········．80
5．7 實驗結(jié)果及分析··················81
5．7．1 算法的有效性················．81
5．7．2 回答正確的原因·············．83
5．7．3 回答錯誤的原因·············．84
5．7．4 以SPARQL 查詢?yōu)橹鲗?dǎo)的
優(yōu)勢··························．85
5．7．5 關(guān)鍵詞索引的效率···········．85
5．8 本章小結(jié)···························86
第6 章 知識圖譜的關(guān)鍵詞聚集查詢···88
6．1 問題描述及創(chuàng)新點···············88
6．2 查詢流程···························90
6．3 構(gòu)建類型-謂詞圖·················90
6．3．1 關(guān)系提取·····················．90
6．3．2 關(guān)系標(biāo)準(zhǔn)化··················．91
6．3．3 類型-謂詞圖··················．92
6．4 查詢理解···························92
6．5 基于類型-謂詞圖構(gòu)建
查詢圖······························94
6．5．1 查詢圖························．94
6．5．2 構(gòu)建查詢圖··················．94
6．5．3 算法BQG 的復(fù)雜度·········．99
6．6 將查詢圖翻譯為SPARQL
語句·································99
6．6．1 數(shù)值型謂詞···················．99
6．6．2 翻譯一般路徑················．99
6．6．3 翻譯聚集·····················100
6．6．4 算法TQGS 的復(fù)雜度········102
6．7 實驗結(jié)果及分析···············．102
6．7．1 算法的有效性················102
6．7．2 輸入的可擴展性·············104
6．7．3 數(shù)據(jù)集的可擴展性···········106
6．7．4 組件的有效性················106
6．8 本章小結(jié)························．108
第7 章 總結(jié)與展望·····················．109
7．1 總結(jié)······························．109
7．2 展望······························．111
參考文獻····································．112