臺灣博碩士論文加值系統

大數據具有海量的資料規模、快速的資料流轉以及多樣的資料類型等特徵，在數據獲取、存儲、管理与分析等面向，超出傳統資料庫軟體工具的功能。因此，傳統的數據融合方法，無法有效解決大數據的數據融合問題。知識圖譜作為一種知識表示方法与數據管理模式，適合描述世界中的各種實體和概念，以及這些實體和概念之間的關聯，並且具有融合異質數據的能力。目前在語義搜索、問題回答、資訊檢索和資料採擷等領域有著廣泛的應用，可以作為異質大數據融合的有效方法。
本研究提出一種基於知識圖譜的數據融合模型，能夠融合結構化、半結構化或非結構化數據。首先為每個領域的數據構建獨立的領域本體庫，作為領域數據源之數據模式的獨立語義描述。然後將各個數據源之間重合或相似的概念抽取出來，構建全局本體庫，為後續知識圖譜融合提供一致性檢測和冗餘去除。在本體的基礎上，通過從不同數據源中提取實體和關係，為每個領域數據源構建領域知識圖譜。最后，通過相似性檢測、實體對齊和衝突解決等過程，將眾多領域知識圖譜融合成一個通用知識圖譜。
知識圖譜的學習和推理能夠進一步完善知識圖譜，並解決問題回答和資訊檢索等領域問題。目前大多數基於嵌入轉換的知識圖譜推理模型在構造corrupted triplets時，未考慮實體間的關係語義類型約束，導致可能不符合實際語義資訊，影響預測準確率。針對此問題，本研究提出一種基於約束嵌入轉換的模型－TransC,該模型在構造corrupted triplets時增加對每個關係的語義類型約束，將不符合語義關係的三元組排除在外，減少訓練模型的錯誤率。實驗結果證明，TransC模型具有參數簡單和預測準確率高的優點。
為驗證本研究所提模型的實用性,對醫療事件、氣象和環境數據進行融合，成功構建一個具備醫療事件、氣象和環境汙染數據的通用知識圖譜，用於分析關聯樣態，改善回溯型分析研究在大數據儲存與動態關聯運算上的困難。最后，為便於知識圖譜數據的使用和管理，本研究開發一個知識圖譜管理平臺，為用戶提供高級數據蒐索、統計、分析和可視化等服務。

Traditional data fusion methods are inadequate in big data analytics. Knowledge graph aiming to describe the various entities, concepts and their linkages. It can capture and present the semantic relations between concepts in the domain and overcome the semantic heterogeneity of big data. Numerous large-scale universal knowledge graphs had already been applied in many big data scenarios.
Thus, this dissertation proposed a knowledge graph-based approach for data fusion in big data analytics. First, a construction of domain ontologies for each specific is given, and then, a construction of global ontology on the basis of domain ontology to provide the consistency checking and redundancy removal for knowledge graph fusion is presented. After that, a construction of the domain knowledge graph, which focused on solving the problem of entity and relation extraction based on ontology is developed. Eventually, a fusion of various domain knowledge graphs into a general knowledge graph on the processes of similarity detection, entity alignment, conflict resolution, relation redirection and data migration concludes this approach.
This dissertation proposed a constraint based embedding model TransC to complete the knowledge graph. It added the semantic-type constraints for each relation while constructing corrupted triplets to excluded the triplets that didn't conform to semantic constraints. The experimental results showed that TransC not only maintained the simple parameters and fast training speed of TransE, but also improved the prediction accuracy.
To verify the practicality and effectiveness of the proposed framework, this dissertation applied data fusion framework to construct a general knowledge graph from the medical, environmental and meteorological data, and established the interrelation for the graph effectively. In addition, this research developed a knowledge graph management platform to provide a consistent access interface and a unified view for the heterogeneous data sources, user could make the operations of advanced search, statistic, analytic, and visualization applications.

List of Tables xi
List of Figures xii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Why Knowledge Graph? 3
1.3 Scope of the Work 5
1.4 Dissertation Organization 7
Chapter 2 Knowledge Graph as a Data Fusion Framework 8
2.1 Background 8
2.2 Traditional Data Fusion Methods 10
2.2.1 Relational Operators and Techniques for Data Fusion 10
2.2.2 Bayesian Networks for Data Fusion 12
2.2.3 Kernel-based Data Fusion 14
2.2.4 Data Fusion in Information Retrieval 16
2.3 Knowledge Graph-based Data Fusion Model 17
Chapter 3 Knowledge Graph Construction and Fusion 22
3.1 Ontology Construction 22
3.1.1 The Structure of the Ontology Library 24
3.1.2 Domain Ontology Construction 24
3.1.3 Global Ontology Construction 43
3.2 Knowledge Graph Construction 51
3.2.1 Entity and Relation Extraction from Structured Data 53
3.2.2 Entity and Relation Extraction from Semi-structured and Unstructured Data 57
3.3 Knowledge Graph Fusion 61
3.3.1 Similarity Detection 63
3.3.2 Entity Alignment 64
3.3.3 Conflict Resolution 66
3.3.4 Relation Redirection 71
3.3.5 Data Migration 72
3.4 Summary 72
Chapter 4 Learning and Reasoning 73
4.1 Background 73
4.2 Typical Learning and Reasoning Methods 75
4.2.1 Learning and Reasoning based on Tensor Decomposition 75
4.2.2 Learning and Reasoning based on Translation 77
4.2.3 Learning and Reasoning based on Path 81
4.2.4 Other Learning and Reasoning Methods 82
4.3 Knowledge Graph Embedding Translation with Constraint 83
4.3.1 Related Works 85
4.3.2 TransC Model 87
4.3.3 TransC Algorithm 89
4.3.4 Comparisons on Parameter Complexity of Algorithms 91
4.3.5 Experiments 92
4.4 Summary 98
Chapter 5 Application in Data Fusion for Medical, Environment, and Meteorological Data 99
5.1 Background 99
5.2 Data Source 100
5.2.1 Industry Internal Data 101
5.2.2 External Related Data 103
5.3 Ontology Construction 107
5.3.1 Domain Ontology Construction 108
5.3.2 Global Ontology Construction 121
5.4 Knowledge Graph Construction 122
5.4.1 Medical Knowledge Graph 122
5.4.2 Meteorological Knowledge Graph 123
5.4.3 Environmental Knowledge Graph 123
5.5 Knowledge Graph Fusion 124
5.6 Knowledge Graph Analysis Platform 125
5.7 Other Applications 132
Chapter 6 Conclusions 134
6.1 Contributions 134
6.2 Future Work 135
Bibliography 138
Biography 153

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