In the contemporary world, the Internet technology and its implementation mode are advancing at a swift
pace, leading to an exponential growth in the scale of Internet data. This data contains a significant amount of valuable
knowledge. The effective organization and articulation of knowledge, as well as the ability to conduct thorough
calculations and analyses, have garnered significant attention and developments within a particular environmental
context. The utilization of knowledge graphs for knowledge reasoning has emerged as a prominent area of focus within
the realm of knowledge graph research. It holds substantial significance in the realm of vertical search, intelligent
answering, and various other applications. This article will be centered on fundamental principles of reasoning. The
approach of knowledge reasoning oriented towards knowledge graphs is focused on the derivation of novel knowledge or
the detection of erroneous knowledge through the utilization of pre-existing knowledge. In contrast to conventional
knowledge reasoning approaches, the knowledge reasoning technique employed in knowledge graphs is characterized by
greater diversity, owing to the succinct, adaptable, and flexible representation of knowledge.
W. Nongrum and Jahanara, “An analysis of the knowledge of improved cultivation practices of tomato (Lycopersicon esculentum) of Khasi
tribes in east Khasi hills of Meghalaya, India,” Asian J. Agric. Ext. Econ. Sociol., pp. 299–306, 2022.
D. Calvanese, A. Gal, D. Lanti, M. Montali, A. Mosca, and R. Shraga, “Conceptually-grounded mapping patterns for Virtual Knowledge
Graphs,” Data Knowl. Eng., vol. 145, no. 102157, p. 102157, 2023.
F. Guo, Y. Liu, L. Zhang, and W. Zhang, “Evolution of railway engineering knowledge from the perspective of engineering philosophy,” J. OF ENG. STUD., vol. 14, no. 5, pp. 432–441, 2023.
W. He, Y. Feng, and D. Zhao, “Improving knowledge base completion by incorporating implicit information,” in Semantic Technology,
Cham: Springer International Publishing, 2016, pp. 141–153.
D. Găină, G. Badia, and T. Kowalski, “Omitting types theorem in hybrid dynamic first-order logic with rigid symbols,” Ann. Pure Appl.
Logic, vol. 174, no. 3, p. 103212, 2023.
H. Nassif, H. Al-Ali, S. Khuri, W. Keirouz, and D. Page, “An Inductive Logic Programming approach to validate hexose binding biochemical
knowledge,” Inductive Log. Program., vol. 5989, pp. 149–165, 2010.
A. Musalem, L. Aburto, and M. Bosch, “Market basket analysis insights to support category management,” Eur. J. Mark., vol. 52, no. 7/8, pp.
1550–1573, 2018.
C. T. Falk et al., “Data mining of RNA expression and DNA genotype data: presentation group 5 contributions to Genetic Analysis Workshop
15,” Genet. Epidemiol., vol. 31 Suppl 1, no. S1, pp. S43-50, 2007.
H. Camargo, G. Nusspaumer, D. Abia, V. Briceño, M. Remacha, and J. P. G. Ballesta, “The amino terminal end determines the stability and
assembling capacity of eukaryotic ribosomal stalk proteins P1 and P2,” Nucleic Acids Res., vol. 39, no. 9, pp. 3735–3743, 2011.
Mohebbanaaz, L. V. R. Kumari, and Y. P. Sai, “Classification of ECG beats using optimized decision tree and adaptive boosted optimized
decision tree,” Signal Image Video Process., vol. 16, no. 3, pp. 695–703, 2022.
M. H. Sundar and Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (A), Visakhapatnam-
530049, AP, India, “A review on applications of NLP with artificial neural networks,” Asia-Pac. J. Neural Netw. Appl., vol. 3, no. 1, pp. 1–8,
2019.
M. Nikku, K. Myöhänen, J. Ritvanen, and T. Hyppänen, “Computational fluid dynamics derived dataset for evaluation of mixing of a
secondary solid phase in a circulating fluidized bed riser,” Data Brief, vol. 48, no. 109039, p. 109039, 2023.
A. Cropper and S. Dumančić, “Inductive logic programming at 30: A new introduction,” J. Artif. Intell. Res., vol. 74, pp. 765–850, 2022.
K. G. Montero Quispe, D. M. S. Utyiama, E. M. Dos Santos, H. A. B. F. Oliveira, and E. J. P. Souto, “Applying self-supervised representation
learning for emotion recognition using physiological signals,” Sensors (Basel), vol. 22, no. 23, p. 9102, 2022.
T. Beelen and P. van Dooren, “A pencil approach for embedding a polynomial matrix into a unimodular matrix,” SIAM J. Matrix Anal. Appl.,
vol. 9, no. 1, pp. 77–89, 1988.
B. Jiang, Y. Chen, B. Wang, H. Xu, and B. Luo, “DropAGG: Robust graph Neural Networks via Drop Aggregation,” Neural Netw., vol. 163,
pp. 65–74, 2023.
X. Hu, H. Chen, S. Liu, H. Jiang, K. Wang, and Y. Wang, “Who are the evil backstage manipulators: Boosting graph attention networks
against deep fraudsters,” Comput. Netw., vol. 227, no. 109698, p. 109698, 2023.
L. Meng, A. Yazidi, M. Goodwin, and P. Engelstad, “Expert Q-learning: Deep reinforcement learning with coarse state values from offline
expert examples,” nldl, vol. 3, 2022.
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Ignacio Villegas Vergara
Ignacio Villegas Vergara
Faculty of Arts, National Major University of San Marcos, Lima 15081, Peru.
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Cite this article
Ignacio Villegas Vergara and Liza Chung Lee, “A Schematic Review of Knowledge Reasoning Approaches Based on the Knowledge Graph”, Journal of Enterprise and Business Intelligence, vol.3, no.3, pp. 179-189, July 2023. doi: 10.53759/5181/JEBI202303018.