Temporal Heterogeneous Information Network Embedding via Semantic Evolution

Zhou, Wei; Huang, Hong; Shi, Ruize; Song, Xiran; Xue, Lin; Wang, Xiao; Jin, Hai

doi:10.1109/tkde.2023.3287260

Cited by 2 publications

(2 citation statements)

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“…The other feasible and commonly used approach is to utilize knowledge graph embedding (KGE) techniques to train a low-dimensional vector for each entity and relation, while maintaining the inherent structure of the IKG. The resulting vectors obtained through KGE can then be utilized to enhance the representations of entities in the IKG [13][14][15][16][17]. However, it is noted that although KGE is specifically tailored for tasks related to knowledge graphs, it is not optimized for applications in industrial settings.…”

Section: Knowledge Graph Embeddingmentioning

confidence: 99%

“…Sampling nodes and capturing meaningful changes in the graph structure presents a significant challenge. Recent research has focused on addressing this challenge by exploring dynamic heterogeneous graphs, which integrate previous node embedding techniques for heterogeneous graphs and dynamic graphs to accommodate the evolving nature of heterogeneous graphs [13][14][15][16].…”

Section: Vectorization Of An Ikgmentioning

confidence: 99%

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Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Huang,

Yang

2024

Electronics

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Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.

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