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研究生:Nurul Fajrin Ariyani
研究生(外文):Nurul Fajrin Ariyani
論文名稱:Building A Domain Ontology for Disaster and Emergency Information Management
論文名稱(外文):Building A Domain Ontology for Disaster and Emergency Information Management
指導教授:Hahn-Ming LeeTyng-Ruey Chuang
指導教授(外文):Hahn-Ming LeeTyng-Ruey Chuang
口試委員:Hahn-Ming LeeTyng-Ruey Chuang
口試日期:2012-07-10
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:資訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:78
中文關鍵詞:ontologySUMOsemantic web
外文關鍵詞:ontologySUMOsemantic web
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In the early response phase after the disaster happens, most responders need to find integrated and relevant information to make decision. In this case, finding suitable information in the open crowdsourcing environments is a complex task, since it involves many actors and a large amount of unstructured and heterogeneous spatial data. While quite significant progress on providing system and standardizing syntax heterogeneity of data has been made, semantic issues are still insufficiently addressed. Using ontological approach to represent information in disaster and emergency management can resolve this semantic heterogeneity problem. However, to the best of our knowledge, there is no formal vocabulary or ontology in existence that specifically allow victims to describe incident information in their nature language.
In this thesis, we build a domain ontology model for disaster and emergency information management which is able to capture descriptive spatial information about incidents and reasons them to get more valuable information that mostly needed by disaster responders. On the other hand, we also consider the possibility of integrating our ontology model with the other systems. In order to aim for these two objectives, we propose a methodology to implement hybrid ontological architecture by engaging SUMO (Suggested Upper Merged Ontology); we later complement it with knowledge-based representation that is capable of processing information depending on its content. Building on the result of our proposed ontology implementation, we create several experimental scenarios for information retrieval using our system.
In the early response phase after the disaster happens, most responders need to find integrated and relevant information to make decision. In this case, finding suitable information in the open crowdsourcing environments is a complex task, since it involves many actors and a large amount of unstructured and heterogeneous spatial data. While quite significant progress on providing system and standardizing syntax heterogeneity of data has been made, semantic issues are still insufficiently addressed. Using ontological approach to represent information in disaster and emergency management can resolve this semantic heterogeneity problem. However, to the best of our knowledge, there is no formal vocabulary or ontology in existence that specifically allow victims to describe incident information in their nature language.
In this thesis, we build a domain ontology model for disaster and emergency information management which is able to capture descriptive spatial information about incidents and reasons them to get more valuable information that mostly needed by disaster responders. On the other hand, we also consider the possibility of integrating our ontology model with the other systems. In order to aim for these two objectives, we propose a methodology to implement hybrid ontological architecture by engaging SUMO (Suggested Upper Merged Ontology); we later complement it with knowledge-based representation that is capable of processing information depending on its content. Building on the result of our proposed ontology implementation, we create several experimental scenarios for information retrieval using our system.
ABSTRACT. i
ACKNOWLEDGMENTS ii
CONTENT…………. iii
LIST OF FIGURES vi
LIST OF TABLES viii
CHAPTER 1. INTRODUCTION 1
1.1 Motivation 1
1.2 Thesis Organization 4
CHAPTER 2. PRELIMINARIES 5
2.1 Ontology 5
2.1.1 Elements of ontology 6
2.1.2 Ontologies Approaches 8
2.2 XML, OWL 2, and Reasoner 9
2.3 SUMO 11
2.4 Related Work 14
2.4.1 Ushahidi Project 14
2.4.2 Disaster Ontology 15
2.5 Summary 20
CHAPTER 3. METHODOLOGY 22
3.1 Case Study 22
3.2 Ontology Building 23
3.2.1 Ontology Capture 23
3.2.2 Ontology Coding 37
3.3 Encoding of Information 39
3.4 Concepts and Relationship Refinement 43
3.4.1 Event – Time Concepts Refinement 44
3.4.2 Event – Object Concepts Refinement 46
3.4.3 Event – Location Concepts Refinement 48
3.4.4 Spatial Relationships 52
3.4.5 Temporal Relationships 53
3.4.6 Causal Relationships 54
3.5 System Architecture 55
3.5.1 Geo-reference Finding 58
CHAPTER 4. EVALUATION 61
4.1 Data Preparation 61
4.2 Scenario 1: Insert a new report about incident 63
4.3 Scenario 2: List events based on its type and inferred location 64
4.4 Scenario 3: Find available hospital nearby 65
4.5 Integrate DEIM Ontology with Wang’s work 66
4.6 Operating Environment 67
CHAPTER 5. CONCLUSION AND FUTURE WORK 68
5.1 Conclusion 68
5.2 Future Work 68
REFERENCES 69
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