Instance-Based Lossless Summarization of Knowledge Graph With Optimized Triples and Corrections (IBA-OTC)

Javed, Hafiz Tayyeb and Khan, Kifayat Ullah and Cheema, Muhammad Faisal and Algarni, Asaad and Park, Jeongmin (2023) Instance-Based Lossless Summarization of Knowledge Graph With Optimized Triples and Corrections (IBA-OTC). IEEE Access, 12. pp. 5584-5604. ISSN 2196-3536

[img]
Preview
Text
Kifayatpaper23.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (2MB)

Abstract

Knowledge graph (KG) summarization facilitates efficient information retrieval for exploring complex structural data. For fast information retrieval, it requires processing on redundant data. However, it necessitates the completion of information in a summary graph. It also saves computational time during data retrieval, storage space, in-memory visualization, and preserving structure after summarization. State-of-the-art approaches summarize a given KG by preserving its structure at the cost of information loss. Additionally, the approaches not preserving the underlying structure, compromise the summarization ratio by focusing only on the compression of specific regions. In this way, these approaches either miss preserving the original facts or the wrong prediction of inferred information. To solve these problems, we present a novel framework for generating a lossless summary by preserving the structure through super signatures and their corresponding corrections. The proposed approach summarizes only the naturally overlapped instances while maintaining its information and preserving the underlying Resource Description Framework RDF graph. The resultant summary is composed of triples with positive, negative, and star corrections that are optimized by the smart calling of two novel functions namely merge and disperse . To evaluate the effectiveness of our proposed approach, we perform experiments on nine publicly available real-world knowledge graphs and obtain a better summarization ratio than state-of-the-art approaches by a margin of 10% to 30% with achieving its completeness, correctness, and compactness. In this way, the retrieval of common events and groups by queries is accelerated in the resultant graph.

Item Type: Article
Identification Number: https://doi.org/10.1109/ACCESS.2023.3340984
Dates:
DateEvent
4 December 2023Accepted
7 December 2023Published Online
Uncontrolled Keywords: Knowledge graph, semantic web, instance-based aggregation, super signature, optimized triples, optimized corrections
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-03 - information systems
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
CAH11 - computing > CAH11-01 - computing > CAH11-01-07 - business computing
Divisions: Faculty of Business, Law and Social Sciences > College of Business, Digital Transformation & Entrepreneurship
Depositing User: Kifayat Khan
Date Deposited: 16 Jan 2024 11:53
Last Modified: 16 Jan 2024 11:53
URI: https://www.open-access.bcu.ac.uk/id/eprint/15120

Actions (login required)

View Item View Item

Research

In this section...