Large scale services for connecting and integrating hundreds of linked datasets

Author:

Mountantonakis Michalis1

Affiliation:

1. University of Crete, Greece

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Challenges for Healthcare Data Analytics Over Knowledge Graphs;Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV;2023

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