Said Fathalla – Smart Data Analytics

PhD Student
Computer Science Institute
University of Bonn

Room 1.052
Endenicher Allee 19a, 53115 Bonn
University of Bonn, Computer Science

fathalla@cs.uni-bonn.de

Short CV

Said Fathalla is a Ph.D. Student at the University of Bonn. Said received an M.S. degree in Computer Science from Alexandria University, Alexandria, Egypt, in 2013. In 2007-2016 he was working as Assistant Lecturer at the Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Alexandria, Egypt. He is currently pursuing the Ph.D. degree in Computer Science at Smart Data Analytics (SDA), University of Bonn, Bonn, Germany. His research focuses on Semantic Web technologies, scholarly communication, knowledge graph construction, semantic reasoning, and natural language processing. He has several conference and journal publications in the semantic web, text classification, Bioinformatics, scholarly communication, and knowledge engineering. He participated in the program committee of several conferences, such as ACM SAC, and he is a reviewer in several journals, such as IJSWIS, MDPI Publications, KIS, and TKDE.

Research Interests

Scholarly Communication
Semantic Data Management
Knowledge Management
Ontology Engineering
Data Analysis and Visualization

Projects

Openresearch.org

Publications

2021

Fathalla, Said

Towards Facilitating Scholarly Communication using Semantic Technologies PhD Thesis

Rheinische Friedrich-Wilhelms-Universität Bonn, 2021.

Abstract | Links | BibTeX

@phdthesis{said_thesis,

title = {Towards Facilitating Scholarly Communication using Semantic Technologies},

author = {Said Fathalla},

url = {https://hdl.handle.net/20.500.11811/9089},

year  = {2021},

date = {2021-05-20},

school = {Rheinische Friedrich-Wilhelms-Universität Bonn},

abstract = {Web technologies have substantially stimulated the submission of manuscripts, publishing scientific articles, as well as the organization of scholarly events, especially virtual events, when a global crisis occurs, which consequently restricts travels across the globe. Publication in scholarly events, such as conferences, workshops, and symposiums, is essential and pervasive in computer science, engineering, and natural sciences. The past years have witnessed significant growth in scholarly data published on the Web, mostly in unstructured formats, which immolate the embedded semantics and relationships between various entities. These formats restrict the reusability of the data, i.e., data analysis, retrieval, and mining. Therefore, managing, retrieving, and analyzing such data have become quite challenging. Consequently, there is a pressing need to represent this data in a semantic format, i.e., Linked Data, which significantly improves scholarly communication by supporting researchers concerning analyzing, retrieving, and exploring scholarly data. Notwithstanding the considerable advances in technology, publishing and exchanging scholarly data have not substantially changed (i.e., still follows the document-based scheme), thus restricting both developments of research applications in various industries as well as data preservation and exploration. This thesis tackles the problem of facilitating scholarly communication using semantic technologies. The ultimate aim is improving scholarly communication by facilitating the transformation from a document-based to knowledge-based scholarly communication, which helps researchers to examine science itself with a new perspective. Key steps towards the goal have been taken by proposing methodologies as well as a metrics suite for publishing and assessing the quality of scholarly events concerning several criteria, in particular, Computer Science as well as Physics, Mathematics, and Engineering. Within the framework of these criteria, steps towards assessing the quality of scholarly events and recommendations to various stakeholders have been taken. Furthermore, we engineered the Scientific Events Ontology in order to enable the enriched semantic representation of scholarly event metadata. Currently, this ontology is in use on thousands of OpenResearch.org events wiki pages. These steps will have far-reaching implications for the various stakeholders involved in the scholarly communication domain, including authors, sponsors, reviewers, publishers, and libraries. Most of the scholarly data publishers, such as Springer Nature, have taken serious steps towards publishing research data in a semantic form by publishing collated information from across the research landscape, such as research articles, scholarly events, persons, and grants, as knowledge graphs. Interlinking this data will significantly enable the provision of better and more intelligent services for the discovery of scientific work, which opens new opportunities for both scholarly data exploration and analysis. In the direction to this goal, we proposed the Science Knowledge Graph Ontologies suite, which comprises four OWL ontologies for representing the scientific knowledge in various fields of science, including Computer Science, Physics, and Pharmaceutical science. Besides, we developed an upper ontology on top of them for modeling modern science branches and related concepts, such as scientific discovery, instruments, and phenomena.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Web technologies have substantially stimulated the submission of manuscripts, publishing scientific articles, as well as the organization of scholarly events, especially virtual events, when a global crisis occurs, which consequently restricts travels across the globe. Publication in scholarly events, such as conferences, workshops, and symposiums, is essential and pervasive in computer science, engineering, and natural sciences. The past years have witnessed significant growth in scholarly data published on the Web, mostly in unstructured formats, which immolate the embedded semantics and relationships between various entities. These formats restrict the reusability of the data, i.e., data analysis, retrieval, and mining. Therefore, managing, retrieving, and analyzing such data have become quite challenging. Consequently, there is a pressing need to represent this data in a semantic format, i.e., Linked Data, which significantly improves scholarly communication by supporting researchers concerning analyzing, retrieving, and exploring scholarly data. Notwithstanding the considerable advances in technology, publishing and exchanging scholarly data have not substantially changed (i.e., still follows the document-based scheme), thus restricting both developments of research applications in various industries as well as data preservation and exploration. This thesis tackles the problem of facilitating scholarly communication using semantic technologies. The ultimate aim is improving scholarly communication by facilitating the transformation from a document-based to knowledge-based scholarly communication, which helps researchers to examine science itself with a new perspective. Key steps towards the goal have been taken by proposing methodologies as well as a metrics suite for publishing and assessing the quality of scholarly events concerning several criteria, in particular, Computer Science as well as Physics, Mathematics, and Engineering. Within the framework of these criteria, steps towards assessing the quality of scholarly events and recommendations to various stakeholders have been taken. Furthermore, we engineered the Scientific Events Ontology in order to enable the enriched semantic representation of scholarly event metadata. Currently, this ontology is in use on thousands of OpenResearch.org events wiki pages. These steps will have far-reaching implications for the various stakeholders involved in the scholarly communication domain, including authors, sponsors, reviewers, publishers, and libraries. Most of the scholarly data publishers, such as Springer Nature, have taken serious steps towards publishing research data in a semantic form by publishing collated information from across the research landscape, such as research articles, scholarly events, persons, and grants, as knowledge graphs. Interlinking this data will significantly enable the provision of better and more intelligent services for the discovery of scientific work, which opens new opportunities for both scholarly data exploration and analysis. In the direction to this goal, we proposed the Science Knowledge Graph Ontologies suite, which comprises four OWL ontologies for representing the scientific knowledge in various fields of science, including Computer Science, Physics, and Pharmaceutical science. Besides, we developed an upper ontology on top of them for modeling modern science branches and related concepts, such as scientific discovery, instruments, and phenomena.

Ali, Mohamed Hassan Mohamed; Fathalla, Said; Kholief, Mohamed; Hassan, Yasser Fouad

Learning Non-Taxonomic Relations of Ontologies: A Systematic Review Journal Article

In: Int. J. Semantic Web Inf. Syst., 17 (1), pp. 97–122, 2021.

Short CV

Research Interests

Projects

Publications

2021

2020

2019

2018

2017

2016

2012