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Moradbeiky, Afrooz, Yaghmaee, Farzin. (1402). A Multi-View Model for Knowledge Graph Embedding in Link Prediction using GRU-RNN as Constraint Satisfaction Problem. , 12(1), 137-147. doi: 10.22044/jadm.2024.13827.2498
Afrooz Moradbeiky; Farzin Yaghmaee. "A Multi-View Model for Knowledge Graph Embedding in Link Prediction using GRU-RNN as Constraint Satisfaction Problem". , 12, 1, 1402, 137-147. doi: 10.22044/jadm.2024.13827.2498
Moradbeiky, Afrooz, Yaghmaee, Farzin. (1402). 'A Multi-View Model for Knowledge Graph Embedding in Link Prediction using GRU-RNN as Constraint Satisfaction Problem', , 12(1), pp. 137-147. doi: 10.22044/jadm.2024.13827.2498
Moradbeiky, Afrooz, Yaghmaee, Farzin. A Multi-View Model for Knowledge Graph Embedding in Link Prediction using GRU-RNN as Constraint Satisfaction Problem. , 1402; 12(1): 137-147. doi: 10.22044/jadm.2024.13827.2498

A Multi-View Model for Knowledge Graph Embedding in Link Prediction using GRU-RNN as Constraint Satisfaction Problem

Journal of AI and Data Mining
دوره 12، شماره 1، فروردین 2024، صفحه 137-147    اصل مقاله (1.09 M)
نوع مقاله: Original/Review Paper
شناسه دیجیتال (DOI): 10.22044/jadm.2024.13827.2498
نویسندگان
Afrooz Moradbeiky؛ Farzin Yaghmaee*
Electrical and Computer Engineering, Semnan University,Semnan, Iran.
چکیده
Knowledge graphs are widely used tools in the field of reasoning, where reasoning is facilitated through link prediction within the knowledge graph. However, traditional methods have limitations, such as high complexity or an inability to effectively capture the structural features of the graph. The main challenge lies in simultaneously handling both the structural and similarity features of the graph. In this study, we employ a constraint satisfaction approach, where each proposed link must satisfy both structural and similarity constraints. For this purpose, each constraint is considered from a specific perspective, referred to as a view. Each view computes a probability score using a GRU-RNN, which satisfies its own predefined constraint. In the first constraint, the proposed node must have a probability of over 0.5 with frontier nodes. The second constraint computes the Bayesian graph, and the proposed node must have a link in the Bayesian graph. The last constraint requires that a proposed node must fall within an acceptable fault. This allows for N-N relationships to be accurately determined, while also addressing the limitations of embedding. The results of the experiments showed that the proposed method improved performance on two standard datasets.
کلیدواژه‌ها
multi-view؛ knowledge graph embedding؛ constraint satisfaction problem؛ GRU-RNN
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