Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Haya El Ghalayini

dblp:344/7762 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning
modularity
0.712023
A Review on Ontology Modularization Techniques - A Multi-Dimensional Perspective · IEEE Trans. Knowl. Data Eng. 2023
Knowledge, reasoning and agents › Knowledge representation and reasoning
ontology
0.712023
A Review on Ontology Modularization Techniques - A Multi-Dimensional Perspective · IEEE Trans. Knowl. Data Eng. 2023
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology
ontology modularity
0.712023
A Review on Ontology Modularization Techniques - A Multi-Dimensional Perspective · IEEE Trans. Knowl. Data Eng. 2023

Methods — techniques the papers use, named apart from their topics

systematic literature review · 0.7
YearPublicationVenuePosition
2023 A Review on Ontology Modularization Techniques - A Multi-Dimensional Perspective
abstract
In the past two decades, the use of ontologies has grown accompanied by a diversity in ontological representations and applications to more comprehensive domains. Knowledge engineers have found it expeditious to break down large (monolithic) ontologies to work with smaller fragments. Ontology modularization is the process of extracting a fragment, or "module", from an ontology, based on predefined requirements. Due to both the diversity in ontological representations and motivations for modularizing, the body of research on ontology modularization techniques has become extremely large and may be intimidating to the novice ontology researcher. The objective of the paper is to present a comprehensive, albeit high-level, review of ontology modularization techniques. A systematic literature review covering January 1st 2000 to July 31st 2020 was performed to find and classify papers on ontology modularization techniques. The techniques exhibiting certain properties with respect to several features were assessed, and the modularization techniques were classified with a multi-dimensional perspective. The classifications are intended to guide one to a suitable modularization process in accordance with the requirements. The limitations of ontology modularization techniques are highlighted in the conclusion, and characteristics of a desirable framework for an ontology representation that would be best-suited for modularization are presented.
Andrew LeClair, Alicia Marinache, Haya El Ghalayini, Wendy MacCaull, Ridha Khédri
IEEE Trans. Knowl. Data Eng.3