Knowledge graphs and big data processing
Valentina Janev,Damien Graux,Hajira Jabeen,Emanuel Sallinger +3 more
- 01 Jan 2020
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TL;DR: This introductory chapter serves to characterize the relevant aspects of the Big Data Ecosystem with respect to big data characteristics, the components needed for implementing end-to-end big data processing and the need for using semantics for improving the data management, integration, processing, and analytical tasks.
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Abstract: The rapid development of digital technologies, IoT products and connectivity platforms, social networking applications, video, audio and geolocation services has created opportunities for collecting/accumulating a large amount of data. While in the past corporations used to deal with static, centrally stored data collected from various sources, with the birth of the web and cloud services, cloud computing is rapidly overtaking the traditional in-house system as a reliable, scalable and costeffective IT solution. The high volumes of structures and unstructured data, stored in a distributed manner, and the wide variety of data sources pose problems related to data/knowledge representation and integration, data querying, business analysis and knowledge discovery. This introductory chapter serves to characterize the relevant aspects of the Big Data Ecosystem with respect to big data characteristics, the components needed for implementing end-to-end big data processing and the need for using semantics for improving the data management, integration, processing, and analytical tasks.
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Citations
Domain-specific knowledge graphs: A survey
TL;DR: This survey is the first to provide an inclusive definition to the notion of domain KG, and a comprehensive review of the state-of-the-art approaches drawn from academic works relevant to seven dissimilar domains of knowledge is provided.
308
Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review
TL;DR: This paper provides a systematic literature review on knowledge graph creation from structured and semi-structured data sources using Semantic Web technologies and highlights the tools, methods, types of data sources, ontologies, and publication methods.
EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities
TL;DR: This study introduces a novel deployment algorithm, called the Evaluated Delaunay Triangulation-based Deployment for Smart Cities (EDTD-SC), which targets not only sensor distribution, but also sink placement and utilizesdelaunay triangulation and k-means clustering to find optimal locations to improve coverage while maintaining connectivity and robustness with obstacles existence in sensing area.
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Knowledge Graphs: A Practical Review of the Research Landscape
TL;DR: Knowledge graphs (KGs) have rapidly emerged as an important area in AI over the last ten years as discussed by the authors , fueled by increased publication of structured datasets on the Web, and well-publicized successes of large-scale projects such as the Google Knowledge Graph and the Amazon Product Graph.
Liberating host-virus knowledge from biological dark data.
Nathan S. Upham,Jorrit H. Poelen,Deborah Paul,Quentin Groom,Nancy B. Simmons,Maarten P.M. Vanhove,Sandro Bertolino,DeeAnn M. Reeder,Cristiane Bastos-Silveira,Atriya Sen,Beckett Sterner,Nico M. Franz,Marcus Guidoti,Lyubomir Penev,Donat Agosti +14 more
TL;DR: In this paper, the authors outline two viable solutions: first, in the short term, to interconnect published data about host organisms, viruses, and other pathogens; and second, to shift the publishing framework beyond unstructured text (the so-called PDF prison) to labelled networks of digital knowledge.
21
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