scholarly journals Semantic Data Integration on Biomedical Data Using Semantic Web Technologies

10.5772/21086 ◽  
2011 ◽  
Author(s):  
Roland Kienast ◽  
Christian Baumgartner
Keyword(s):  
Semantic Web ◽  
Data Integration ◽  
Biomedical Data ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Semantic Data ◽  
Semantic Data Integration

scholarly journals The 3rd DBCLS BioHackathon: improving life science data integration with Semantic Web technologies

2013 ◽  
Vol 4 (1) ◽  
pp. 6 ◽  
Author(s):  
Toshiaki Katayama ◽  
Mark D Wilkinson ◽  
Gos Micklem ◽  
Shuichi Kawashima ◽  
Atsuko Yamaguchi ◽  
...  
Keyword(s):  
Semantic Web ◽  
Data Integration ◽  
Life Science ◽  
Science Data ◽  
Semantic Web Technologies ◽  
Web Technologies

Semantic Web Technologies for Business Intelligence

2011 ◽  
pp. 310-339 ◽  
Author(s):  
Rafael Berlanga ◽  
Oscar Romero ◽  
Alkis Simitsis ◽  
Victoria Nebot ◽  
Torben Bach Pedersen ◽  
...  
Keyword(s):  
Semantic Web ◽  
Business Intelligence ◽  
Full Potential ◽  
Semantic Web Technologies ◽  
Data Annotation ◽  
Web Technologies ◽  
Critical Knowledge ◽  
Semantic Data ◽  
External Data ◽  
The Relationship

This chapter describes the convergence of two of the most influential technologies in the last decade, namely business intelligence (BI) and the Semantic Web (SW). Business intelligence is used by almost any enterprise to derive important business-critical knowledge from both internal and (increasingly) external data. When using external data, most often found on the Web, the most important issue is knowing the precise semantics of the data. Without this, the results cannot be trusted. Here, Semantic Web technologies come to the rescue, as they allow semantics ranging from very simple to very complex to be specified for any web-available resource. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The chapter first presents a motivating running example, followed by an introduction to the relevant SW foundation concepts. The chapter then goes on to survey the use of SW technologies for data integration, including semantic data annotation and semantics-aware extract, transform, and load processes (ETL). Next, the chapter describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms, and the use of advanced SW reasoning functionality on MD models. Finally, the chapter describes in detail a number of directions for future research, including SW support for intelligent BI querying, using SW technologies for providing context to data warehouses, and scalability issues. The overall conclusion is that SW technologies are very relevant for the future of BI, but that several new developments are needed to reach the full potential.

scholarly journals A case study of data integration for aquatic resources using semantic web technologies

2015 ◽  
Author(s):  
Janice M. Gordon ◽  
Nina Chkhenkeli ◽  
David L. Govoni ◽  
Frances L. Lightsom ◽  
Andrea C. Ostroff ◽  
...  
Keyword(s):  
Semantic Web ◽  
Data Integration ◽  
Semantic Web Technologies ◽  
Aquatic Resources ◽  
Web Technologies

scholarly journals Linked Data Driven Information Systems as an Enabler for Integrating Financial Data

2011 ◽  
pp. 239-270 ◽  
Author(s):  
Seán O’Riain ◽  
Andreas Harth ◽  
Edward Curry
Keyword(s):  
Semantic Web ◽  
Data Integration ◽  
Information Integration ◽  
Financial Services ◽  
Linked Data ◽  
Financial Data ◽  
Data Sources ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Multiple Data

With increased dependence on efficient use and inclusion of diverse corporate and Web based data sources for business information analysis, financial information providers will increasingly need agile information integration capabilities. Linked Data is a set of technologies and best practices that provide such a level of agility for information integration, access, and use. Current approaches struggle to cope with multiple data sources inclusion in near real-time, and have looked to Semantic Web technologies for assistance with infrastructure access, and dealing with multiple data formats and their vocabularies. This chapter discusses the challenges of financial data integration, provides the component architecture of Web enabled financial data integration and outlines the emergence of a financial ecosystem, based upon existing Web standards usage. Introductions to Semantic Web technologies are given, and the chapter supports this with insight and discussion gathered from multiple financial services use case implementations. Finally, best practice for integrating Web data based on the Linked Data principles and emergent areas are described.

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