Knowledge Graphs and Big Data Processing
This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companie...
Uložené v:
| Hlavní autori: | , , , |
|---|---|
| Médium: | E-kniha Kniha |
| Jazyk: | English |
| Vydavateľské údaje: |
Cham
Springer Nature
2020
Springer Springer International Publishing AG |
| Vydanie: | 1 |
| Edícia: | Lecture Notes in Computer Science |
| Predmet: | |
| ISBN: | 3030531996, 9783030531997, 9783030531980, 3030531988 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
Obsah:
- Intro -- Preface -- Acknowledgments -- Acronyms and Definitions -- Contents -- I Foundations -- Chapter 1pg Ecosystem of Big Data -- 1 Introduction -- 2 Big Data Ecosystem -- 3 Components of the Big Data Ecosystem -- 4 Using Semantics in Big Data Processing -- 5 Big Data, Standards and Interoperability -- 6 Big Data Analytics -- 6.1 The Evolution of Analytics -- 6.2 Different Types of Data Analytics -- 7 Challenges for Exploiting the Potential of Big Data -- 7.1 Challenges -- 7.2 Example: Analysis of Challenges and Solutions for Traffic Management -- 8 Conclusions -- Chapter2 Knowledge Graphs: The Layered Perspective -- 1 Introduction -- 2 KGs as Knowledge Representation Tools -- 3 KGs as Knowledge Management Systems -- 4 KGs as Knowledge Application Services -- 5 KGs in Practice: Challenges and Opportunities -- 5.1 Integrated Ownership and Company Control -- 5.2 Large-Scale Scholarly Knowledge Graphs -- 6 Conclusion -- Chapter3 Big Data Outlook, Tools, and Architectures -- 1 Introduction -- 2 Big Data: Outlook -- 2.1 Key Technologies and Business Drivers -- 2.2 Characteristics of Big Data -- 2.3 Challenges of Big Data -- 2.4 Big Data Value Chain -- 3 Tools and Architectures -- 3.1 Big Data Architectures -- 3.2 Tools to Handle Big Data -- 4 Harnessing Big Data as Knowledge Graphs -- 4.1 Graph Stores -- 5 Conclusion -- I Architecture -- Chapter4 Creation of Knowledge Graphs -- 1 Introduction -- 2 R2RML -- 3 RML -- 3.1 Data Retrieval -- 3.2 Data Transformations: FnO -- 3.3 Other Representations: YARRRML -- 4 [R2]RML Extensions and Alternatives -- 4.1 XR2RML -- 4.2 KR2RML -- 4.3 FunUL -- 5 Conclusions -- Chapter 5 Federated Query Processing -- 1 Introduction -- 2 Data Integration Systems -- 2.1 Classification of Data Integration Systems -- 2.2 Data Integration in the Era of Big Data -- 3 Federated Query Processing -- 3.1 Data Source Description
- 3.2 Query Decomposition and Source Selection -- 3.3 Query Planning and Optimization -- 3.4 Query Execution -- 4 Grand Challenges and Future Work -- Chapter 6 Reasoning in Knowledge Graphs: An Embeddings Spotlight -- 1 Introduction -- 2 Reasoning for Knowledge Integration -- 2.1 Schema/Ontology Matching -- 2.2 Entity Resolution -- 2.3 Data Exchange and Integration -- 3 Reasoning for Knowledge Discovery -- 3.1 Link Prediction -- 4 Reasoning for Application Services -- 4.1 Recommendation Systems -- 4.2 Question Answering -- 5 Challenges and Opportunities -- I Methods and Solutions -- Chapter 7 Scalable Knowledge Graph Processing Using SANSA -- 1 Introduction -- 2 Semantic Layer Cake -- 3 Processing Big Knowledge Graphs with SANSA -- 3.1 Knowledge Representation and Distribution -- 3.2 Query -- 3.3 Inference -- 3.4 Machine Learning -- 3.5 Semantic Similarity Measures -- 3.6 Clustering -- 3.7 Anomaly Detection -- 3.8 Entity Linking -- 3.9 Graph Kernels for RDF -- 4 Grand Challenges and Conclusions -- Chapter 8 Context-Based Entity Matching for Big Data -- 1 Introduction -- 1.1 Motivating Example -- 1.2 Challenges and Problems -- 2 Applications of Entity Matching -- 2.1 Semantic Data Integration -- 2.2 Summarization of Knowledge Graph -- 3 Novel Entity Matching Approaches -- 3.1 Context in the Semantic Web -- 3.2 Entity Matching Approaches -- 4 COMET: A Context-Aware Matching Technique -- 4.1 Problem Definition -- 4.2 The COMET Architecture -- 4.3 Identifying Contextually Equivalent Entities -- 4.4 The 1-1 Perfect Matching Calculator -- 4.5 Integration Use Case: Applying Fusion Policies -- 5 Empirical Evaluation -- 5.1 Research Questions -- 5.2 Implementation -- 5.3 Baseline -- 5.4 Effectiveness Evaluation -- 5.5 Discussion of Observed Results -- 6 Grand Challenges and Conclusions -- I Applications -- Chapter 9 Survey on Big Data Applications
- 1 Introduction -- 2 Literature Review -- 3 Big Data Analytics in Industrial Sectors -- 4 Conclusions -- Chapter 10 Case Study from the Energy Domain -- 1 Introduction -- 2 Challenges Withing the Big Data Energy Domain -- 3 Energy Conservation Big Data Analytical Services -- 3.1 Non-Intrusive Load Monitoring -- 3.2 Energy Conservation Measures (ECM) -- 3.3 User Benchmark -- 4 Forecasters -- 4.1 Demand Forecaster -- 4.2 Production Forecaster -- 4.3 Pricing Prediction -- 5 Conclusion -- References