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

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8. Case Search with Networks 8.2 Searching RDN 1) Use parallel searching for RDN connected with the case-structure-node 2) Find a union set of cases after parallel searching 3) Find the most similar case through full partial match with the found cases. attributes and values of a new prob. Flow_rate has_q_val fast Noise has_q_val low has_d_val CarType SUV intall has_d_val has_q_val Inside_car out_diameter has_n_val wide Country China 56 RDN #4 flow_rate noise CarType … sim_val(n) sim_val(n) sim_val(n) = 1 = 0.5 = 0 fast normal slow low mid MOP2 MOP3 MOP5 omitted omitted flow_rate= fast Case #3 Case #4 noise = low noise = mid noise sim_val(n) = 0.5 mid Case #5 low MOP4 sim_val(n) = 0.7 sim_val(n) = 1 flow_rate= fast noise = low standard CarType premium Case #1 Case #2 MOP1 flow_rate flow_rate fast slow sim_val(n) = 0.3 - Case#4 Case #5 fast normal Case #3 4 5 CSN Search RDN Search Memory organization packet (MOP) Attribute-concept Values (Qualitative_value_concept, Defined-value-concept) Case
9. Discussion Representation Limitations Text Attribute-value list Object-oriented Graph Is hard to calculate similarity. NLP is required can not represent relations in a data model can not represent a case beyond a class model Graph similarity calculation is required. Graph + Ontology Ontology construction is required. Why the Graph + Ontology representation is better than Graph representation? 1) The ontology can be used to infer taxonomy relations of new terminology. 2) The ontology can be used to infer new relations between concepts. 57
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Ontology-based Ontology based Case-
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Contents ■ Ontology-based Mapping
Page 5 and 6: 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: 8. Case Search with Networks 8.1 Se
Page 59 and 60: 9. Discussion Ontology-based CBR Ty
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Ontology-based Case-Based Reasoning (OntCBR) for Engineering ...

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