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To address the limitations in capturing complex semantic features between entities and the incomplete acquisition of entity and relationship information by existing patent knowledge graph reasoning algorithms, we propose a reasoning method that integrates semantic and structural features for patent knowledge graphs, denoted as SS-DSA. Initially, to facilitate the model representation of patent information, a directed graph representation model based on the patent knowledge graph is designed. Subsequently, structural information within the knowledge graph is mined using inductive learning, which is combined with the learning of graph structural features. Finally, an attention mechanism is employed to integrate the scoring results, enhancing the accuracy of reasoning outcomes such as patent classification, latent inter-entity relationships, and new knowledge inference. Experimental results demonstrate that the improved algorithm achieves an up to approximately 30% increase in the MRR index compared to the ComplEx model in the public Dataset 1; in Dataset 2, the MRR and Hits@n indexes, respectively, saw maximal improvements of nearly 30% and 112% when compared with MLMLM and ComplEx models; in Dataset 3, the MRR and Hits@n indexes realized maximal enhancements of nearly 200% and 40% in comparison with the MLMLM model. This effectively proves the efficacy of the refined model in the reasoning process. Compared to recently widely applied reasoning algorithms, it offers a superior capability in addressing complex structures within the datasets and accomplishing the completion of existing patent knowledge graphs.
To address the limitations in capturing complex semantic features between entities and the incomplete acquisition of entity and relationship information by existing patent knowledge graph reasoning algorithms, we propose a reasoning method that integrates semantic and structural features for patent knowledge graphs, denoted as SS-DSA. Initially, to facilitate the model representation of patent information, a directed graph representation model based on the patent knowledge graph is designed. Subsequently, structural information within the knowledge graph is mined using inductive learning, which is combined with the learning of graph structural features. Finally, an attention mechanism is employed to integrate the scoring results, enhancing the accuracy of reasoning outcomes such as patent classification, latent inter-entity relationships, and new knowledge inference. Experimental results demonstrate that the improved algorithm achieves an up to approximately 30% increase in the MRR index compared to the ComplEx model in the public Dataset 1; in Dataset 2, the MRR and Hits@n indexes, respectively, saw maximal improvements of nearly 30% and 112% when compared with MLMLM and ComplEx models; in Dataset 3, the MRR and Hits@n indexes realized maximal enhancements of nearly 200% and 40% in comparison with the MLMLM model. This effectively proves the efficacy of the refined model in the reasoning process. Compared to recently widely applied reasoning algorithms, it offers a superior capability in addressing complex structures within the datasets and accomplishing the completion of existing patent knowledge graphs.
Aimed at mitigating the limitations of the existing document entity relation extraction methods, especially the complex information interaction between different entities in the document and the poor effect of entity relation classification, according to the semi-structured characteristics of patent document data, a patent document ontology model construction method based on hierarchical clustering and association rules was proposed to describe the entities and their relations in the patent document, dubbed as MPreA. Combined with statistical learning and deep learning algorithms, the pre-trained model of the attention mechanism was fused to realize the effective extraction of entity relations. The results of the numerical simulation show that, compared with the traditional methods, our proposed method has achieved significant improvement in solving the problem of insufficient contextual information, and provides a more effective solution for patent document entity relation extraction.
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