scholarly journals Large-scale linked data integration using probabilistic reasoning and crowdsourcing

2013 ◽  
Vol 22 (5) ◽  
pp. 665-687 ◽  
Author(s):  
Gianluca Demartini ◽  
Djellel Eddine Difallah ◽  
Philippe Cudré-Mauroux
Keyword(s):  
Data Integration ◽  
Linked Data ◽  
Large Scale ◽  
Probabilistic Reasoning

Large scale services for connecting and integrating hundreds of linked datasets

2021 ◽  
pp. 1-4
Author(s):  
Michalis Mountantonakis
Keyword(s):  
Data Integration ◽  
Computer Science ◽  
Linked Data ◽  
Large Scale ◽  
Associate Professor ◽  
Science Department ◽  
Research Papers ◽  
Computer Science Department ◽  
Semantic Data ◽  
Available Information

Michalis Mountantonakis is a Postdoctoral Researcher of the Information Systems Laboratory at FORTH-ICS (Greece) and a Visiting Lecturer in the Computer Science Department at University of Crete (CSD-UoC), Greece. He obtained his PhD degree from the CSD-UoC in 2020. His research interests fall in the areas of large-scale semantic data integration, linked data and semantic data management. The results of his research have been published in more than 20 research papers. For his dissertation, he awarded a) the prestigious SWSA Distinguished Dissertation Award 2020, which is given to the PhD dissertation from the previous year with the highest originality, significance, and impact in the area of semantic web, and b) the Maria Michael Manasaki Legacy's fellowship, which is awarded to the best graduate student of CSD-UoC, once a year. In his dissertation, supervised by Associate Professor Yannis Tzitzikas (Computer Science Department at University of Crete), Michalis Mountantonakis dealt with the problem of Linked Data Integration at large scale, which is a very big challenging problem. He factorized the integration process according to various dimensions, for better understanding the overall problem and for identifying the open challenges, and proposed novel indexes and algorithms for providing core services, which can be exploited for several tasks related to Data Integration, such as: for finding all the URIs and all the available information for an entity, for producing connectivity analytics, for discovering the most relevant datasets for a given task, for dataset enrichment, and many others.

scholarly journals Learnings from Multi-Source Enduring Linked Data Assets (MELDAS)

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Geoff Neideck
Keyword(s):  
Data Integration ◽  
Linked Data ◽  
Large Scale ◽  
Complex Data ◽  
Real World Data ◽  
Future Design ◽  
Efficient Management ◽  
Integration Project ◽  
Technical Solutions ◽  
Innovative Approaches

IntroductionDemand continues to grow for accessible and large scale linked data assets to answer complex cross-sector, and cross-jurisdiction research questions. To meet this demand, a number of Multi-source, Enduring Linked Data Assets (MELDAs) have emerged including the National Integrated Health Service Infrastructure (NIHSI), National Disability Data Asset (NDDA) and Multi-Agency Data Integration Project (MADIP). Using these MELDAs has proven much more efficient than project-specific linkages, and provides consistent national data assets for multiple uses. However, the development of these assets raises new challenges, including complex data models, governance, and access arrangements, and new approaches to analysis. Objectives and ApproachThrough developing the NIHSI Analytical Asset in collaboration with state/territory and Federal Government partners, the AIHW has identified challenges in traditional linkage approaches, which require innovative approaches to ensure high quality linkage. As AIHW commences scoping on new MELDAs, we are taking lessons from building the NIHSI and applying them to future design. ResultsAIHW’s development of MELDAs across jurisdictions and portfolios provides new learnings on how to address advanced real world data integration issues. This review will focus on lessons learnt at the Australian Institute of Health and Welfare (AIHW) working with new data sharing arrangements, applications of technologies and innovative approaches to streamline MELDA processes. Conclusion / ImplicationsThe learnings from the AIHW development of MELDAs will assist others developing enduring assets to establish effective sharing arrangements, governance and technical solutions to ensure efficient management. These learnings will save time and resources, and prompt further discussion on a gold standard for building MELDAs moving forwards.

Large-scale Data Integration for Facilities Analytics: Challenges and Opportunities

2020 ◽  
Author(s):  
Balaje T. Thumati ◽  
Halasya Siva Subramania ◽  
Rajeev Shastri ◽  
Karthik Kalyana Kumar ◽  
Nicole Hessner ◽  
...  
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Large Scale Data ◽  
Challenges And Opportunities ◽  
Scale Data

scholarly journals Image-Based Literal Node Matching for Linked Data Integration

2014 ◽  
Vol 5 (4) ◽  
pp. 101-112 ◽  
Author(s):  
Takahiro Kawamura ◽  
Akihiko Ohsuga
Keyword(s):  
Data Integration ◽  
Linked Data

Multi-Target Search on Semantic Associations in Linked Data

2018 ◽  
Vol 14 (1) ◽  
pp. 71-97 ◽  
Author(s):  
Xiang Zhang ◽  
Erjing Lin ◽  
Yulian Lv
Keyword(s):  
Linked Data ◽  
Large Scale ◽  
Search Process ◽  
Search Model ◽  
Linguistic Features ◽  
Target Search ◽  
Search Results ◽  
Semantic Associations ◽  
Effectiveness And Efficiency ◽  
Virtual Document

In this article, the authors propose a novel search model: Multi-Target Search (MT search in brief). MT search is a keyword-based search model on Semantic Associations in Linked Data. Each search contains multiple sub-queries, in which each sub-query represents a certain user need for a certain object in a group relationship. They first formularize the problem of association search, and then introduce their approach to discover Semantic Associations in large-scale Linked Data. Next, they elaborate their novel search model, the notion of Virtual Document they use to extract linguistic features, and the details of search process. The authors then discuss the way search results are organized and summarized. Quantitative experiments are conducted on DBpedia to validate the effectiveness and efficiency of their approach.

scholarly journals Apply Linked Data to Large-Scale Humanities Database

2015 ◽  
Vol 25 (4) ◽  
pp. 291-298 ◽  
Author(s):  
Makoto GOTO
Keyword(s):  
Linked Data ◽  
Large Scale
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