Ontology-based Case-Based Reasoning (OntCBR) for Engineering ...

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9. Discussion Why CSN is effective to search a similar concept structure? c2 If we only compare concepts in concept structures, we can miss the meanings of relations in the concept structure. Concept structure: cs1 Concept structure: cs2 c1 c1 r4 r5 r1 r2 r1 r3 Axiom ∀x,y r4(x,y) → r5(x,y) c4 c3 c2 c3 c4 58 Concept vector similarity (FEM paper) CBS(cs1, cs2) = 100% CBS(v1, v2) = (v1 * v2) / ||v1|| * ||v2|| v1=[1, 1, 1, 1] v2=[1, 1, 1, 1] CSN similarity (Our approach) NetSim(cs1, cs2) = 56.6% NetSim(cs1, cs2) = ( rel_sim(r1, r1) * con_sim(c2, c2) + rel_sim(r2, r3) * con_sim(c3, c3) + rel_sim(r4, r5) * con_sim(c4, c4)) / 3 = ( 1*1 + 0*1 + 0.7*1)/3 = 1.7/3 = 0.566
9. Discussion Ontology-based CBR Typical Approach a1 r4 a2 a4 a5 r5 r6 r7 r6 Query:Q1 C4 C3 a4 a5 v 3 Axioms … Definitions … Ontology c1 c2 r7 v 4 r1 r2 r3 r2 c3 c4 c5 r8 r8 r9 r8 a3 a7 a6 a3 v 7 Wringgers and et. al., 2007, “Intelligent support of engineering analysis using ontology and case-based reasoning.” Engineering Applications of Artificial Intelligence, 20, pp. 709-720. 59 Cases Case1 CBS(Q1, Case1) = 0.75 S C2 m = 0.75 r6 SBS(Q1, C1) = 0.3 S(Q1, C1) = 0.6 a4 Smax = 0.6 Case2 r2 C3 v1 r8 CBS(Q1, Case2) = 0.5 C1 Sm = 0.5 r4 (no calculation, a1 Sm < Smax ) r1 r5 a2 C3 r8 Case3 CBS(Q1, Case3) = 0.5 S C2 m = 0.5 r6 (no calculation, S a4 m < Smax ) CBS(Q1, Case4) = 0.8 S Case4 m = 0.8 SBS(Q1, C4) = 0.6 C4 r6 r7 S(Q1, C4) = 0.7 Smax = 0.7 a4 a5 v 3 v 5 v 4 v 6 r2 r2 v 3 r3 C3 C5 r8 r9 a3 v 2 a7 v 6 a3 v 7 r8 C3 a3 v 6 a6 v 9 Search sequentially. (because it is a flat model)
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Ontology-based Ontology based Case-
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Contents ■ Ontology-based Mapping
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Ontology - Definition a formal and
Page 7 and 8: Ontology - Mapping/Similarity Match
Page 9 and 10: Taxonomy Kim, Park Lee, Suh Employe
Page 11 and 12: Contents � Ontology � Case-Base
Page 13 and 14: 1. Introduction Case-Based Reasonin
Page 15 and 16: 2. Previous Research Prev. Research
Page 17 and 18: 3. Proposed Approach Problem Descri
Page 19 and 20: 3. Proposed Approach Logic represen
Page 21 and 22: 3. Proposed Approach Ontology: Term
Page 23 and 24: 3. Proposed Approach Ontology: Term
Page 25 and 26: 3. Proposed Approach Ontology: Onto
Page 27 and 28: 3. Proposed Approach Ontology: RDN
Page 29 and 30: 3. Proposed Approach OntCBR New Pro
Page 31 and 32: 4. Ontology Construction 4.1. Commo
Page 33 and 34: 4. Ontology Construction 4.1. Commo
Page 35 and 36: 5. Ontology Reasoning 5.1 Taxonomy
Page 37 and 38: 5. Ontology Reasoning Tableau Algor
Page 39 and 40: 5. Ontology Reasoning 5.2 Axiom Rea
Page 41 and 42: has_req 5. Ontology Reasoning 3) Ax
Page 43 and 44: 6. Ontology-based Case-Base Network
Page 49 and 50: 7. Similarity Evaluation 7.1 Term
Page 51 and 52: 7. Similarity Evaluation 7.1 Term
Page 53 and 54: 7. Similarity Evaluation 7.2 Graph
Page 55 and 56: 8. Case Search with Networks 8.1 Se
Page 57: 9. Discussion Representation Limita
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Ontology-based Case-Based Reasoning (OntCBR) for Engineering ...

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