人民幣定價：58 元
定 價：NT$ 348 元
優惠價：87 折 303 元
缺貨無法訂購
相關商品
商品簡介
作者簡介
目次
商品簡介
知識圖譜的自然語言查詢和關鍵字查詢是知識問答中較有前景的兩種知識圖譜查詢方式。知識圖譜是一種結構化的語義知識庫，以圖的方式展現“實體”、實體的“屬性”，以及實體與實體之間的“關係”。知識圖譜的自然語言查詢和關鍵字查詢，使搜尋引擎不僅能返回與查詢相關的網頁，而且能返回智能化的答案。本書介紹知識圖譜的自然語言查詢和關鍵字查詢，包括自然語言查詢中的語義關係識別、自然語言聚集查詢、SPARQL 和關鍵字相結合的自然語言查詢、關鍵字查詢等。本書可供高等院校計算機、人工智能、大資料等相關專業研究生和高年級本科生參考閱讀，也可供知識工程領域的技術人員參考閱讀。
作者簡介
胡新，博士，長江師範學院大資料與智能工程學院講師，長江師範學院高層次人才引進項目 知識圖譜問答中的自然語言查詢”負責人
目次
章 緒論·································．1
1．1 研究背景及意義··················．1
1．2 研究現狀···························．3
1．2．1 知識圖譜自然語言查詢的
研究現狀························3
1．2．2 知識圖譜關鍵字查詢的
研究現狀························4
1．3 存在的關鍵問題··················．5
1．4 研究內容及創新點···············．7
1．5 本書組織結構·····················10
第2 章 自然語言查詢和關鍵字查詢的
相關研究···························12
2．1 知識圖譜的自然語言查詢······12
2．1．1 語義關係識別················．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 知識圖譜的關鍵字查詢·········16
2．2．1 模式圖························．16
2．2．2 多樣化的關鍵字查詢········．17
2．3 兩種查詢共享的基礎技術······19
2．3．1 實體識別和實體連結········．19
2．3．2 解釋詞典·····················．19
2．4 眾包—輔助語義關係識別規則
挖掘·································20
2．5 知識圖譜的其他非結構化
查詢方式···························21
2．5．1 互動式查詢···················．21
2．5．2 實例查詢和樣例查詢········．22
第3 章 基於眾包的自然語言查詢中
語義關係識別規則挖掘·········23
3．1 問題描述及創新點···············23
3．2 眾包模型···························24
3．2．1 反覆運算模型和並行模型········．25
3．2．2 反覆運算式並行模型和
並行式反覆運算模型·············．25
3．2．3 帶回饋的並行式反覆運算模型···．26
3．3 生成語義關係資料集和
依賴結構資料集··················27
3．3．1 眾包模型標記語義關係·····．27
3．3．2 Stanford Parser 生成依賴
結構··························．27
3．4 挖掘語義關聯規則···············28
3．4．1 挖掘語義關聯規則的算法···．28
3．4．2 算法MSAR 的複雜度·······．30
3．5 實驗結果及分析—眾包模型··31
3．5．1 實驗資料及評估標準········．31
3．5．2 反覆運算模型和並行模型········．32
3．5．3 反覆運算式並行模型和並行式
反覆運算模型·····················．33
3．5．4 帶回饋的並行式反覆運算模型···．35
3．6 實驗結果及分析—語義關聯
規則·································36
3．7 語義關係識別·····················38
3．7．1 語義關係識別的算法········．38
3．7．2 算法SRR 的複雜度··········．39
3．7．3 實驗結果及分析—語義關係
識別··························．39
3．8 本章小結···························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 依賴結構分類················．46
4．3．3 從依賴結構中識別意圖解釋·．47
4．3．4 查詢理解的優化·············．49
4．3．5 算法AIII 的複雜度··········．49
4．4 構建基本圖模式··················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 語義關係映射················．53
4．4．6 算法SRM 的複雜度·········．55
4．4．7 構建基本圖模式BGP········．56
4．4．8 算法BBGP 的複雜度········．57
4．5 將基本圖模式翻譯為
SPARQL 語句·····················58
4．5．1 數值型謂詞···················．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 的複雜度···········．61
4．6 實驗結果及分析··················61
4．6．1 實驗資料集···················．61
4．6．2 各階段的優化能力···········．61
4．6．3 算法的有效性················．63
4．6．4 與現有算法對比·············．65
4．6．5 回答錯誤的原因·············．66
4．7 相關問題及解決方案············67
4．8 本章小結···························69
第5 章 知識圖譜的自然語言查詢—
SPARQL 和關鍵字··············70
5．1 問題描述及創新點···············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 執行SPARQL 階段··········．74
5．4．4 查詢分解算法················．75
5．4．5 算法DQ 的複雜度···········．76
5．5 構建關鍵字索引··················77
5．5．1 算法QUKI ···················．77
5．5．2 算法QUKI 的複雜度········．78
5．6 聚合SPARQL 結果子圖和
關鍵字查詢························78
5．6．1 算法CSK ····················．78
5．6．2 算法CSK 的複雜度·········．80
5．7 實驗結果及分析··················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 查詢為主導的
優勢··························．85
5．7．5 關鍵字索引的效率···········．85
5．8 本章小結···························86
第6 章 知識圖譜的關鍵字聚集查詢···88
6．1 問題描述及創新點···············88
6．2 查詢流程···························90
6．3 構建類型-謂詞圖·················90
6．3．1 關係提取·····················．90
6．3．2 關係標準化··················．91
6．3．3 類型-謂詞圖··················．92
6．4 查詢理解···························92
6．5 基於類型-謂詞圖構建
查詢圖······························94
6．5．1 查詢圖························．94
6．5．2 構建查詢圖··················．94
6．5．3 算法BQG 的複雜度·········．99
6．6 將查詢圖翻譯為SPARQL
語句·································99
6．6．1 數值型謂詞···················．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 的複雜度········102
6．7 實驗結果及分析···············．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 資料集的可擴展性···········106
6．7．4 組件的有效性················106
6．8 本章小結························．108
第7 章 總結與展望·····················．109
7．1 總結······························．109
7．2 展望······························．111
參考文獻····································．112
1．1 研究背景及意義··················．1
1．2 研究現狀···························．3
1．2．1 知識圖譜自然語言查詢的
研究現狀························3
1．2．2 知識圖譜關鍵字查詢的
研究現狀························4
1．3 存在的關鍵問題··················．5
1．4 研究內容及創新點···············．7
1．5 本書組織結構·····················10
第2 章 自然語言查詢和關鍵字查詢的
相關研究···························12
2．1 知識圖譜的自然語言查詢······12
2．1．1 語義關係識別················．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 知識圖譜的關鍵字查詢·········16
2．2．1 模式圖························．16
2．2．2 多樣化的關鍵字查詢········．17
2．3 兩種查詢共享的基礎技術······19
2．3．1 實體識別和實體連結········．19
2．3．2 解釋詞典·····················．19
2．4 眾包—輔助語義關係識別規則
挖掘·································20
2．5 知識圖譜的其他非結構化
查詢方式···························21
2．5．1 互動式查詢···················．21
2．5．2 實例查詢和樣例查詢········．22
第3 章 基於眾包的自然語言查詢中
語義關係識別規則挖掘·········23
3．1 問題描述及創新點···············23
3．2 眾包模型···························24
3．2．1 反覆運算模型和並行模型········．25
3．2．2 反覆運算式並行模型和
並行式反覆運算模型·············．25
3．2．3 帶回饋的並行式反覆運算模型···．26
3．3 生成語義關係資料集和
依賴結構資料集··················27
3．3．1 眾包模型標記語義關係·····．27
3．3．2 Stanford Parser 生成依賴
結構··························．27
3．4 挖掘語義關聯規則···············28
3．4．1 挖掘語義關聯規則的算法···．28
3．4．2 算法MSAR 的複雜度·······．30
3．5 實驗結果及分析—眾包模型··31
3．5．1 實驗資料及評估標準········．31
3．5．2 反覆運算模型和並行模型········．32
3．5．3 反覆運算式並行模型和並行式
反覆運算模型·····················．33
3．5．4 帶回饋的並行式反覆運算模型···．35
3．6 實驗結果及分析—語義關聯
規則·································36
3．7 語義關係識別·····················38
3．7．1 語義關係識別的算法········．38
3．7．2 算法SRR 的複雜度··········．39
3．7．3 實驗結果及分析—語義關係
識別··························．39
3．8 本章小結···························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 依賴結構分類················．46
4．3．3 從依賴結構中識別意圖解釋·．47
4．3．4 查詢理解的優化·············．49
4．3．5 算法AIII 的複雜度··········．49
4．4 構建基本圖模式··················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 語義關係映射················．53
4．4．6 算法SRM 的複雜度·········．55
4．4．7 構建基本圖模式BGP········．56
4．4．8 算法BBGP 的複雜度········．57
4．5 將基本圖模式翻譯為
SPARQL 語句·····················58
4．5．1 數值型謂詞···················．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 的複雜度···········．61
4．6 實驗結果及分析··················61
4．6．1 實驗資料集···················．61
4．6．2 各階段的優化能力···········．61
4．6．3 算法的有效性················．63
4．6．4 與現有算法對比·············．65
4．6．5 回答錯誤的原因·············．66
4．7 相關問題及解決方案············67
4．8 本章小結···························69
第5 章 知識圖譜的自然語言查詢—
SPARQL 和關鍵字··············70
5．1 問題描述及創新點···············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 執行SPARQL 階段··········．74
5．4．4 查詢分解算法················．75
5．4．5 算法DQ 的複雜度···········．76
5．5 構建關鍵字索引··················77
5．5．1 算法QUKI ···················．77
5．5．2 算法QUKI 的複雜度········．78
5．6 聚合SPARQL 結果子圖和
關鍵字查詢························78
5．6．1 算法CSK ····················．78
5．6．2 算法CSK 的複雜度·········．80
5．7 實驗結果及分析··················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 查詢為主導的
優勢··························．85
5．7．5 關鍵字索引的效率···········．85
5．8 本章小結···························86
第6 章 知識圖譜的關鍵字聚集查詢···88
6．1 問題描述及創新點···············88
6．2 查詢流程···························90
6．3 構建類型-謂詞圖·················90
6．3．1 關係提取·····················．90
6．3．2 關係標準化··················．91
6．3．3 類型-謂詞圖··················．92
6．4 查詢理解···························92
6．5 基於類型-謂詞圖構建
查詢圖······························94
6．5．1 查詢圖························．94
6．5．2 構建查詢圖··················．94
6．5．3 算法BQG 的複雜度·········．99
6．6 將查詢圖翻譯為SPARQL
語句·································99
6．6．1 數值型謂詞···················．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 的複雜度········102
6．7 實驗結果及分析···············．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 資料集的可擴展性···········106
6．7．4 組件的有效性················106
6．8 本章小結························．108
第7 章 總結與展望·····················．109
7．1 總結······························．109
7．2 展望······························．111
參考文獻····································．112
您曾經瀏覽過的商品
購物須知
大陸出版品因裝訂品質及貨運條件與台灣出版品落差甚大，除封面破損、內頁脫落等較嚴重的狀態，其餘商品將正常出貨。
特別提醒：部分書籍附贈之內容(如音頻mp3或影片dvd等)已無實體光碟提供，需以QR CODE 連結至當地網站註冊“並通過驗證程序”，方可下載使用。
無現貨庫存之簡體書，將向海外調貨：
海外有庫存之書籍，等候約45個工作天;
海外無庫存之書籍，平均作業時間約60個工作天，然不保證確定可調到貨，尚請見諒。
為了保護您的權益，「三民網路書店」提供會員七日商品鑑賞期(收到商品為起始日)。
若要辦理退貨，請在商品鑑賞期內寄回，且商品必須是全新狀態與完整包裝(商品、附件、發票、隨貨贈品等)否則恕不接受退貨。