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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. Full article

Rapid market change is one of the reasons for accelerating a technology lifecycle. Enterprises have socialized, externalized, combined, and internalized knowledge for their survival. However, the current era requires ambidextrous innovation through the diffusion of knowledge from enterprises. Accordingly, enterprises have discovered sustainable resources and increased market value through collaborations with research institutions and universities. Such collaborative activities effectively improve enterprise innovation, economic growth, and national competence. However, as such collaborations are conducted continuously and iteratively, their effect has gradually weakened. Therefore, we focus on exploring potential R&D collaboration partners through patents co-owned by enterprises, research institutions, and universities. The business pattern of co-applicants is extracted through a patent graph, and potential R&D collaboration partners are unearthed. In this paper, we propose a method of converting a co-applicant-based graph into a vector using representation learning. Our purpose is to explore potential R&D collaboration partners from the similarity between vectors. Compared to other methods, the proposed method contributes to discovering potential R&D collaboration partners based on organizational features. The following questions are considered in order to discover potential R&D partners in collaborative activities: Can information about co-applicants of patents satisfactorily explain R&D collaboration? Conversely, can potential R&D collaboration partners be discovered from co-applicants? To answer these questions, we conducted experiments using autonomous-driving-related patents. We verified that our proposed method can explore potential R&D collaboration partners with high accuracy through experiments. Full article

Ontologies, and especially formal ones, have traditionally been investigated as a means to formalize an application domain so as to carry out automated reasoning on it. The union of the terminological part of an ontology and the corresponding assertional part is known as a Knowledge Graph. On the other hand, database technology has often focused on the optimal organization of data so as to boost efficiency in their storage, management and retrieval. Graph databases are a recent technology specifically focusing on element-driven data browsing rather than on batch processing. While the complementarity and connections between these technologies are patent and intuitive, little exists to bring them to full integration and cooperation. This paper aims at bridging this gap, by proposing an intermediate format that can be easily mapped onto the formal ontology on one hand, so as to allow complex reasoning, and onto the graph database on the other, so as to benefit from efficient data handling. Full article