Generic placeholder image

Recent Advances in Computer Science and Communications


ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Research Article

Research on Multirelational Entity Modeling based on Knowledge Graph Representation Learning

Author(s): Tongke Fan*

Volume 16, Issue 8, 2023

Published on: 24 July, 2023

Article ID: e120623217901 Pages: 7

DOI: 10.2174/2666255816666230612151713


Background: A research concern revolves around as to what can make the representation of entities and relationships fully integrate the structural information of the knowledge atlas to solve the entity modeling capability in complex relationships. World knowledge can be organized into a structured knowledge network by mining entity and relationship information in real texts. In order to apply the rich structured information in the knowledge map to downstream applications, it is particularly important to express and learn the knowledge map. In the knowledge atlas with expanding scale and more diversified knowledge sources, there are many types of relationships with complex types. The frequency of a single relationship in all triples is further reduced, which increases the difficulty of relational reasoning. Thus, this study aimed to improve the accuracy of relational reasoning and entity reasoning in complex relational models.

Methods: For the multi-relational knowledge map, CTransR based on the TransE model and TransR model adopts the idea of piecewise linear regression to cluster the potential relationships between head and tail entities, and establishes a vector representation for each cluster separately, so that the same relationship represented by different clusters still has a certain degree of similarity.

Results: The CTransR model carried out knowledge reasoning experiments in the open dataset, and achieved good performance.

Conclusion: The CTransR model is highly effective and progressive for complex relationships. In this experiment, we have evaluated the model, including link prediction, triad classification, and text relationship extraction. The results show that the CTransR model has achieved significant improvement.

Keywords: Knowledge map, representing learning, TransE, TransR, dataset, piecewise linear.

Y.F. Dong, C. Liu, and L.Q. Wang, "Relationship reasoning method combining multi-hop relationship path information", Comput. Appl., vol. 41, no. 10, pp. 2799-2805, 2021.
H.N. Song, G. Zhao, and X.F. Wang, "Knowledge reasoning method com bining knowledge representation with deep reinforcement learning", Comput. Eng. App., vol. 57, no. 19, pp. 189-197, 2021.
X.D. Yang, Inference completion algorithms based on knowledge graph construction., University of Electronic Science and Technology of China, 2020.
Y. Ning, Z. Gang, J. Lu, D. Yang, and Z. Tian, "A representation learning method of knowledge graph integrating relation path and entity description information", J. Comp. Res. Develop., vol. 59, no. 9, pp. 1966-1979, 2022.
P. Yuan, and R.J. Wang, "Multi-relational knowledge graph reasoning algorithm based on representation learning", Microelectronics Comp., vol. 39, no. 4, pp. 75-82, 2022.
A. Onan, "Mining opinions from instructor evaluation reviews: A deep learning approach", Comput. Appl. Eng. Educ., vol. 28, no. 1, pp. 117-138, 2020.
A. Onan, "Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach", Comput. Appl. Eng. Educ., vol. 29, no. 3, pp. 572-589, 2021.
X. Chen, S. Jia, and Y. Xiang, "A review: Knowledge reasoning over knowledge graph", Expert Syst. Appl., vol. 141, p. 112948, 2020.
D. Paulius, and Y. Sun, "A survey of knowledge representation in service robotics", Robot. Auton. Syst., vol. 118, no. 8, pp. 13-30, 2019.
D. Santra, S.K. Basu, J.K. Mandal, and S. Goswami, "Rough set based lattice structure for knowledge representation in medical expert systems: Low back pain management case study", Expert Syst. Appl., vol. 145, no. 5, p. 113084, 2020.
K. Kumarasinghe, N. Kasabov, and D. Taylor, "Deep leaming and deep knowledge representation in spiking neural networks for brain Computer interfaces", Neural Netw., vol. 121, pp. 169-185, 2020.
[] [PMID: 31568895]
R. Gayathri, and V. Uma, "Ontology based knowledge represen tation technique, domain modeling lan guages and planners for robotic path planning: A survey", ICT Express, vol. 4, no. 2, pp. 69-74, 2018.
A. Viloria, and O.B. Pineda Lezama, "An intelligent approach for the design and development of a personalized system of knowledge representation", Procedia Comput. Sci., vol. 151, pp. 1225-1230, 2019.
Jinda Qi, Ding Lan, and S. Lim, "Ontology-based knowledge representation of urban heat island miti gation strategies", Sustain Cities Soc., vol. 52, pp. 101-875, 2020.
P. Warren, P. Mulholland, T. Collins, and E. Motta, "Improving comprehension of knowledge representation languages: A case study with Description Logics", Int. J. Hum. Comput. Stud., vol. 122, no. 2, pp. 145-167, 2019.
H. Xiao, C. Zhang, and C. Guo, "Distributed representation of knowledge graphs with subgraphaware proximity", Theor. Comput. Sci., vol. 803, no. 1, pp. 48-56, 2020.
W. Fan, L. Heng, and D. Chao, "Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis", Reliab. Eng. Syst. Saf., vol. 191, no. 11, p. 106529, 2019.
D. Gürdür, F.A. Vulgarakis, J. El-khoury, M.S. Kumar, R. Badrinath, M.A. Pradeep, and E. Fersman, "Know ledge representation of cyber-physical systems for monitoring purpose", Procedia CIRP, vol. 72, pp. 468-473, 2018.
A.E. Suleimankadieva, V.I. Pilipenko, and J. Sagi, "Knowledge company: Approaches to assessing new know ledge and representation it to society", Procedia Comput. Sci., vol. 150, pp. 730-736, 2019.
"K. atalnikovaS, L. Novickis, and N. Prokofyeva, “Intelligent collaborative educational systems and knowledge representation”", Procedia Comput. Sci., vol. 104, pp. 166-173, 2017.
C. Sanin, Z. Haoxi, I. Shafiq, M.M. Waris, C. Silva de Oliveira, and E. Szczerbicki, "Experience based knowledge representation for Internet of Things and Cyber Physical Systems with case studies", Future Gener. Comput. Syst., vol. 92, no. 3, pp. 604-616, 2019.
D.H. Pham, and A.C. Le, "Learning multiple layers of knowledge representation for aspect based sentiment analysis", Data Knowl. Eng., vol. 114, no. 3, pp. 26-39, 2018.
W. Li, and F. Leite, "Formalized knowledge representation for spatial conflict coordination of Mechanical, Electrical and Plumbing (MEP) systems in new building projects", Autom. Construct., vol. 64, no. 4, pp. 20-26, 2016.
C. Benavides, I. García, H. Alaiz, and L. Quesada, "An ontology-based approach to knowledge representation for computer-aided control system design", Data Knowl. Eng., vol. 118, no. 11, pp. 107-125, 2018.
Y. Ding, R. Wu, and X. Zhang, "Ontology-based knowledge representation for malware individuals and families", Comput. Secur., vol. 87, no. 11, p. 101574, 2019.
N. Angelopoulos, and J. Cussens, "Distributional logic programming for Bayesian knowledge representation", Int. J. Approx. Reason., vol. 80, no. 1, pp. 52-66, 2017.
B. Robson, and S. Boray, "Data-mining to build a knowledge representation store for clinical decision support. Studies on curation and validation based on machine performance in multiple choice medical licensing examinations", Comput. Biol. Med., vol. 73, no. 6, pp. 71-93, 2016.
[] [PMID: 27089305]
F. Miao, Z. Qiang, and T.F. Zheng, "Distributed represen tation leaming for knowledge graphs with entity descriptions", Pattern Recognit. Lett., vol. 93, no. 7, pp. 31-37, 2017.
A. Gellert, A. Florea, U. Fiore, P. Zanetti, and L. Vintan, "Performance and energy optimisation in CPUs through fuzzy knowledge representation", Inf. Sci., vol. 476, no. 2, pp. 375-391, 2019.

© 2023 Bentham Science Publishers | Privacy Policy