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Large scale services for connecting and integrating hundreds of linked datasets

Published: 03 December 2021 Publication History

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.

References

[1]
Mountantonakis, M. 2021. Services for Connecting and Integrating Big Numbers of Linked Datasets. Studies on the Semantic Web, vol. 50. IOS Press.
[2]
Mountantonakis, M. and Tzitzikas, Y. 2016. On measuring the lattice of commonalities among several linked datasets. Proceedings of the VLDB Endowment 9, 12, 1101--1112.
[3]
Mountantonakis, M. and Tzitzikas, Y. 2017. How linked data can aid machine learning-based tasks. In International Conference on Theory and Practice of Digital Libraries. Springer, 155--168.
[4]
Mountantonakis, M. and Tzitzikas, Y. 2018a. High performance methods for linked open data connectivity analytics. Information 9, 6, 134.
[5]
Mountantonakis, M. and Tzitzikas, Y. 2018b. Lodsyndesis: global scale knowledge services. Heritage 1, 2, 335--348.
[6]
Mountantonakis, M. and Tzitzikas, Y. 2018c. Scalable methods for measuring the connectivity and quality of large numbers of linked datasets. Journal of Data and Information Quality (JDIQ) 9, 3, 1--49.
[7]
Mountantonakis, M. and Tzitzikas, Y. 2019a. Knowledge graph embeddings over hundreds of linked datasets. In Research Conference on Metadata and Semantics Research. Springer, 150--162.
[8]
Mountantonakis, M. and Tzitzikas, Y. 2019b. Large-scale semantic integration of linked data: A survey. ACM Computing Surveys (CSUR) 52, 5, 1--40.
[9]
Mountantonakis, M. and Tzitzikas, Y. 2020. Content-based union and complement metrics for dataset search over rdf knowledge graphs. Journal of Data and Information Quality (JDIQ) 12, 2, 1--31.

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  • (2023)Challenges for Healthcare Data Analytics Over Knowledge GraphsTransactions on Large-Scale Data- and Knowledge-Centered Systems LIV10.1007/978-3-662-68014-8_4(89-118)Online publication date: 22-Sep-2023

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cover image ACM SIGWEB Newsletter
ACM SIGWEB Newsletter  Volume 2021, Issue Autumn
Autumn 2021
41 pages
ISSN:1931-1745
EISSN:1931-1435
DOI:10.1145/3494825
Issue’s Table of Contents
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 December 2021
Published in SIGWEB Volume 2021, Issue Autumn

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  • (2023)Challenges for Healthcare Data Analytics Over Knowledge GraphsTransactions on Large-Scale Data- and Knowledge-Centered Systems LIV10.1007/978-3-662-68014-8_4(89-118)Online publication date: 22-Sep-2023

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