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
|
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: |
|
||||||
| 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 Accountancy, Finance and Economics 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: | 20 Jun 2024 12:04 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/15120 |
Actions (login required)
 |
View Item |
Tools
Tools