1 / 82

Chapter 6 Knowledge Acquisition 知識擷取

Chapter 6 Knowledge Acquisition 知識擷取. 6.1 簡介. 知識擷取 (Knowledge Acquisition) 的主要目的是抽取領域專家的專業知識. Expertise Transfer. 知識庫. Computerized Representation. 專家. 系統採用知識擷取技術的優點 ：. 不須依賴訓練範例 (training cases) 可以即時分析 可以即時地做一致性檢查 可以整合其他 KE 工具 知識庫可以自動產生. 先前研究回顧. Substantive( 獨立的、實在的 ) Knowledge ：

marlon
Télécharger la présentation

Chapter 6 Knowledge Acquisition 知識擷取

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 6Knowledge Acquisition知識擷取

  2. 6.1簡介 • 知識擷取(Knowledge Acquisition) 的主要目的是抽取領域專家的專業知識 Expertise Transfer 知識庫 Computerized Representation 專家 Expert Systems sstseng

  3. 系統採用知識擷取技術的優點： • 不須依賴訓練範例 (training cases) • 可以即時分析 • 可以即時地做一致性檢查 • 可以整合其他KE工具 • 知識庫可以自動產生 Expert Systems sstseng

  4. 先前研究回顧 Substantive(獨立的、實在的) Knowledge： 確認目前的狀態 “我目前是否處於被攻擊的危險中” 策略知識 (Strategic Knowledge)： 決定下一步做什麼 “攀爬到3000英尺處” Expert Systems sstseng

  5. 知識擷取系統 Substantive Knowledge Strategic Knowledge Classification Decision making Control Planning MORE SALT MOLE ASK Repertory Grid Approach Other Approach TEIRESIAS KRITON ETS NeoETS KSSO KITTEN AQUINAS KNACK RuleCon Expert Systems sstseng

  6. The Acquisition of Substantive Knowledge • Repertory Grid（知識表格）-Oriented Methods： 步驟一: 抽取出要被分類的元素 (elements) 步驟二: 由專家擷取出配對屬性組 (constructs) 每次取出三個元素, 專家必須決定一個配對屬性組, 區別出其中兩個元素與另一個元素的差別 步驟三: 填入表格中[元素, 屬性]的等級, 由1~5 步驟四: 從知識表格產生推論圖 (Implication graph) Expert Systems sstseng

  7. 步驟一: 抽取出要被分類的元素 步驟二: 由專家擷取出配對屬性組 Expert Systems sstseng

  8. 步驟三: 填入表格中[元素, 屬性]的等級 步驟四: 從知識表格產生推論圖 headache red purple high fever Expert Systems sstseng

  9. 由表格產生出來的規則: First column： IF high_fever and red and purple and (not headache) Then Disease = Measles CF = MIN (0.8,1.0,0.8,0.8) = 0.8 Second column： IF (not high_fever) and (not red) and (not purple) and (not headache) Then Disease = German Measles Expert Systems sstseng

  10. 使用知識表格的好處 容易分析擷取出來的知識： • 屬性配對組的相似性分析 • 元素的相似性分析 • 分析不同屬性配對組的關聯 • 偵測遺漏的元素 • 偵測邏輯上的錯誤 Expert Systems sstseng

  11. 6.2 ELICTATION(引出、誘出) OF SUBSTANTIVE KNOWLEDGE • 知識表示法 (Knowledge Representation) A dog has 4 legs being very sure Expert Systems sstseng

  12. An acquisition table is a repertory grid（知識表格） of multiple data types： Boolean ：true or false Single value：an integer, a real, or a symbol Set of value：a set of integers, real numbers or symbols. Range of values：a set of integers or real numbers. ‘X’：no relation. ‘U’：unknown or undecidable. • Ratings： 2：very likely to be. 1：maybe. Expert Systems sstseng

  13. 6.3 知識表格可能的問題 • 元素選擇的問題 Expert Systems sstseng

  14. Problem of Multi-Level Knowledge and Acquirability INPUT DATA INPUT DATA SUBGOAL SUBGOAL SUBGOAL INPUT DATA GOAL Expert Systems sstseng

  15. The Concept of Acquirability： The value of a terminalattribute of a decision tree must either be a constant or be acquirable from users. For example： IF (leaf-shape = scale(鱗狀)) and (class = Gymnosperm(裸子植物)) THEN family = Cypress(柏樹). Class is not an acquirable attribute. Expert Systems sstseng

  16. ？ ？ ？ Leaf Shape Class Family Expert Systems sstseng

  17. Domain basis and classification knowledge： Diseases Domain basis (劇烈的疹病) Other diseases Acute Exanthemas Classification knowledge Measles, German measles, Dangue fever,… Expert Systems sstseng

  18. 隱含知識的問題 • 當一個診斷者在描述感冒有下列特徵 “頭痛, 疲勞, 咳嗽, 打噴嚏,…,” 他的真正意思是 “當一個人真正感冒時, 他可能會有上述幾種症狀” • 一般我們常用以下的規則來表示: (Headache = yes) and (Feel_tired = yes) and (cough = yes) and …, --> Disease = Catch_cold Expert Systems sstseng

  19. 診斷者的隱含知識 “假如沒有一個或數個感冒的症狀, 這個病人仍然有可能感冒” 這樣的隱含知識被忽略了 Expert Systems sstseng

  20. 6.4 EMCUD：一個新的隱含知識擷取技術 • 知識表示法 (Knowledge Representation)： 知識擷取 (Conventional Repertory grid) 或 Acquisition Table + 屬性序列表格(Attribute Ordering Table - AOT) Expert Systems sstseng

  21. 根據屬性序列表格擷取隱含知識 • 每一個AOT的值可能是： ‘D’：該屬性對目標有主導權 ‘X’：該屬性與目標無關 整數：屬性相對於目標的重要程度順序 (越小的數值越不重要) Expert Systems sstseng

  22. 知識表格的範例 根據第一個欄位產生出來的規則： RULE1： (A1{9,10,12}) (A2 = YES) > GOAL=obj1 Where F(confidence) = 1.0 if confidence = 2 = 0.8 if confidence = 1 and Certainty Factor CF = MIN(F(2),F(1)) = 0.8 Expert Systems sstseng

  23. 產生AOT的範例 EMCUD：If A1 {9,10,12}, is it possible that GOAL =Obj1 ? EXPERT：No. /*This implies that A1 dominates Obj1 and AOT<Obj1,A1> = ‘D’ */ EMCUD：If A2  YES,is it possible that GOAL = Obj1? EXPERT：Yes. /*A2 does not dominate Obj1 */ EMCUD：If A1 > 16 or A1  13, is it possible that GOAL = Obj3? EXPERT：Yes. /* A1 does not dominate Obj3 */ EMCUD：If A2  YES, is it possible that GOAL = Obj3 ? EXPERT：Yes. /* A2 does not dominate Obj3 */ EMCUD：If A3  4.3 , is it possible that GOAL = Obj3 ? EXPERT：No. /* A3 does dominate Obj3 */ Expert Systems sstseng

  24. EMCUD：Please rank A1 and A2 in the order of importance to Obj3 by choosing one of the following expressions： 1)A1 is more important that A2 2)A1 is less important that A2 3)A1 is as important as A2 EXPERT：1 /* A1 is more important to Obj3 than A2, hence AOT < Obj3,A1> = 2 and AOT <Obj3,A2> = 1 */ Expert Systems sstseng

  25. 擷取隱含知識 From RULE3, the following embedded rules（隱含規則） will Be generated by negating the predicates of A1 and A2： RULE3,1：NOT(13<A116)(A2=YES)  (A3=A3) 　　　　 → GOAL = Obj3 RULE3,2： (13<A116)NOT(A2=YES)  (A3=A3) 　　　　 → GOAL = Obj3 RULE3,3：NOT(13<A116)NOT(A2=YES)  (A3=A3) 　　　　 → GOAL = Obj3 Expert Systems sstseng

  26. Certainty Sequence(CS)： Represents the drgree of certainty degradation. CS(RULESij) = SUM(AOT<Obji,Ak>) for each ak in the negated predicates of ruleij For example： CS(RULE3,3) = AOT < Obj3,A1 + AOT<Obj3,A2> = 2 + 1 = 3 The embedded rules（隱含規則）generated from RULE3： RULE3,1：NOT(13<A116)(A2=YES)  (A3=A3) 　　　　 → GOAL = Obj3 CS = 2 RULE3,2： (13<A116)NOT(A2=YES)  (A3=A3) 　　　　 → GOAL = Obj3CS = 1 RULE3,3：NOT(13<A116)NOT(A2=YES)  (A3=A3) 　　　　 → GOAL = Obj3CS = 3 Expert Systems sstseng

  27. Construct Constraint List • Sort the embedded rules according to the CS values： RULES3,2CS = 1 RULES3,1CS = 2 RULES3,3CS = 3 • A prune-and-search algorithm： EMCUD：Do you think RULE3,1 is acceptable? Expert：Yes. /* then RULE3,2 is also accepted*/ EMCUD：Do you think RULE3,3 is acceptable? Expert：No. /* then CS=3 is recorded in the constraint list */ Expert Systems sstseng

  28. 計算確定因子 (Certainty Factors) Confirm：1.0 Strongly support：0.8 Support：0.6 May support：0.4 CFij= Upper-Boundi- (Csij/MAX(Csi))  (Upper-Boundi – Lower-Boundi) MAX(Csi)：maximum CS value of the embedded rules generated from RULEi. Upper-Boundi：certainty factor of embedded Lower-Boundi：certainty factor of embedded rule with MAX(Csi) /* The rule with least confidence*/ Expert Systems sstseng

  29. 一個計算確定因子的例子 針對由RULE3得來的隱含規則： 1. Upper – Bound = CF(RULES3) = 0.8 2. 因為RULES3 沒有被接受, 所以擁有最大確定因子 (MAX(CS)) 的是 RULE3,1： EMCUD：If RULE3 strongly supports GOAL = Obj3 , what about RULE3,1 ? Expert：1. /*The Lower-Bound = 0.6*/ CF3,1 = 0.8 – (2/2) * (0.8 – 0.6) = 0.6 CF3,2 = 0.8 – (1/2) * (0.8 – 0.6) = 0.7 Expert Systems sstseng

  30. 擷取隱含知識的流程： repertory grid original rules Attribute-Ordering Table eliciting embedded rules possible embedded rules Constraint List thresholding accepted embedded rules mapping mapping function certainty factors of the embedded rules Expert Systems sstseng

  31. ACQUISITION TABLE AOT Expert Systems sstseng

  32. 傳統的知識表格： IF (咳嗽=YES)&(疲倦=YES)&(頭痛=YES) THEN DISEASE=肺炎 EMCUD： IF (咳嗽=YES)&(疲倦<>YES)&(頭痛=YES) THEN DISEASE=肺炎　　　　　CF=0.67 IF (咳嗽=YES)&(疲倦=YES)&(頭痛<>YES) THEN DISEASE=肺炎　　　　　CF=0.73 IF (咳嗽=YES)&(疲倦<>YES)&(頭痛<>YES) THEN DISEASE=肺炎　　　　　CF=0.6 Expert Systems sstseng

  33. OBJECT CHAIN：A METHOD FOR questions selection： • For the grid with 50 elements (or objects), there are 19600 possible choices of questions to elicit constructs (or attributes). • Initial repertory grid（知識表格） and the object chains： OBJECT CHAIN Obj1 --> 2,3,4,5 Obj2 --> 1,3,4,5 Obj3 --> 1,2,4,5 Obj4 --> 1,2,3,5 Obj5 --> 1,2,3,4 Expert Systems sstseng

  34. The expert gives attribute P1 to distinguish Obj1 andObj2 fromObj3 OBJECT CHAIN Obj1 -- > 2,5 Obj2 -- > 1,5 Obj3 -- > 4 Obj4 -- > 3 Obj5 -- > 1,2 Expert Systems sstseng

  35. The expert gives attribute P2 to distinguish Obj2 andObj5 fromObj1 OBJECT CHAIN Obj1 -- > NULL Obj2 -- > 5 Obj3 -- > NULL Obj4 -- > NULL Obj5 -- > 2 Expert Systems sstseng

  36. The expert gives attribute P3 to distinguish Obj2 fromObj5 OBJECT CHAIN Obj1 -- > NULL Obj2 -- > NULL Obj3 -- > NULL Obj4 -- > NULL Obj5 -- > NULL Expert Systems sstseng

  37. Advantages： • Fewer questions are asked(log2n to n-1 questions). • All of the objects are classified. • Every question matches the current requirement of classifying objects. • Disadvantages： • It may force the expert to think a specific direction. • Some important attributes may be ignored. Expert Systems sstseng

  38. Eliciting hierarchy of grids： • For the expert system（專家系統） of classifying families of plants Goal is FAMILY Expert Systems sstseng

  39. Since class is not acquirable, it becomes the goal of a new grid. Goal is CLASS Expert Systems sstseng

  40. Since class is not acquirable, it becomes the goal of a new grid. Goal is TYPE Expert Systems sstseng

  41. Decision tree of the hierarchy of grids： FAMILY OF PLANT LEAF SHAPE NIDDLE PATTERN CLASS TYPE FLATE STEAM POSITION ONE TRUNK Expert Systems sstseng

  42. 6.5 EMCUD 的應用和效能評估 • 應用領域： 急性疹病的診斷 • 硬體： 個人電腦 • 軟體： Personal Consultant Easy Expert Systems sstseng

  43. The codes of diseases and their translations: 1-Measles8 - Meningococcemia 2-German measles9 - Rocky Mt. Spotted fever 3-Chickenpox10 - Typhus fevers 4-Smallpox11 – Infectious mononucleosis 5-Scarlet12 – Enterovirus infections 6-Exanthem subitum13 – Drug eruptions 7-Fifth disease14 – Eczema herpeticum Table 6.3：Testing results of the old and new prototypes. Expert Systems sstseng

  44. 6.6 多專家知識整合 • 為了建立一個可靠的專家系統, 通常我們需要多個專家通力合作 • 困難點： • Synonyms of elements (possible solutions) • Synonyms of traits (attributes to classify the solutions) • Conflicts of ratings Expert Systems sstseng

  45. Each expert has his own way to do some works. Habitual domain of Expert 1 Habitual domain of Expert 2 Integrated Knowledge Use more attributes to make choices from more possible decisions Expert Systems sstseng

  46. Expert 1 Expert 2 Expert N Busy Busy Busy Far away Far away Knowledge Engineer It is difficult to have all of the experts work together Expert Systems sstseng

  47. … Expert 1 Expert 2 Expert N Phase 1 interview Repertory Grid 1 Repertory Grid 2 Repertory Grid N The unions of element sets and construct sets Common Repertory Grid Phase 2 interview … Expert 1 Expert 2 Expert N Eliminate some redundant vocabularies Common Repertory Grid Expert Systems sstseng

  48. Phase 3 interview … Expert 1 Expert 2 Expert N Rated Common Repertory Grid 1 Rated Common Repertory Grid 2 Rated Common Repertory Grid N Knowledge Integration Integrated Repertory Grid Rule Generation Expert Systems sstseng

  49. Repertory Grid 1 Repertory Grid 2 Repertory Grid N The unions of element sets and construct sets Common Repertory Grid Phase 2 interview … Expert 1 Expert 2 Expert N Eliminate some redundant vocabularies Common Repertory Grid Phase 3 interview Expert 1 Expert 2 Expert N Expert Systems sstseng

  50. Rated Common Repertory Grid 1 Rated Common Repertory Grid 2 Rated Common Repertory Grid N Knowledge Integration Integrated Repertory Grid Flat Repertory Grid Generate AOT AOT … Filled AOT 1 Filled AOT 2 Filled AOT N Integration or AOT’s Integrated AOT Rule Generation Expert Systems sstseng

More Related