TransO: a knowledge-driven representation learning method with ontology information constraints

Li, Zhao; Liu, Xin; Wang, Xin; Liu, Pengkai; Shen, Yuxin

doi:10.1007/s11280-022-01016-3

Cited by 30 publications

(7 citation statements)

References 23 publications

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“…The poor performance of the studied methods on YAGO3-10 leaves the future work to explore other embedding models in the literature on this dataset. Another perspective of this study is to plug SNS to a recently published approach, TransO, leveraging ontology information for learning better embeddings [30].…”

Section: Discussionmentioning

confidence: 99%

Negative sampling and rule mining for explainable link prediction in knowledge graphs

Islam

Aridhi

Smaïl-Tabbone

2022

Knowledge-Based Systems

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Section: Discussionmentioning

confidence: 99%

Negative sampling and rule mining for explainable link prediction in knowledge graphs

Islam

Aridhi

Smaïl-Tabbone

2022

Knowledge-Based Systems

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“…Graph neural networks have also emerged in newer variants after the GCN, with P Veličković et al [28] weighting the edges on top of the GCN, called GAT. Zhao et al [29] believe ontology information is the key for building knowledge-driven decisionmaking processes. Describing complex systems in nature is very important, Zhao et al [30] propose a novel deep attributed network representation learning model framework to preserve the highly nonlinear coupling and interactive network topological structure and attribute information.…”

Section: Related Workmentioning

confidence: 99%

UMP-MG: A Uni-directed Message-Passing Multi-label Generation Model for Hierarchical Text Classification

et al. 2023

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Hierarchical Text Classification (HTC) is a formidable task which involves classifying textual descriptions into a taxonomic hierarchy. Existing methods, however, have difficulty in adequately modeling the hierarchical label structures, because they tend to focus on employing graph embedding methods to encode the hierarchical structure while disregarding the fact that the HTC labels are rooted in a tree structure. This is significant because, unlike a graph, the tree structure inherently has a directive that ordains information flow from one node to another—a critical factor when applying graph embedding to the HTC task. But in the graph structure, message-passing is undirected, which will lead to the imbalance of message transmission between nodes when applied to HTC. To this end, we propose a unidirectional message-passing multi-label generation model for HTC, referred to as UMP-MG. Instead of viewing HTC as a classification problem as previous methods have done, this novel approach conceptualizes it as a sequence generation task, introducing prior hierarchical information during the decoding process. This further enables the blocking of information flow in one direction to ensure that the graph embedding method is better suited for the HTC task and thus resulted in the enhanced tree structure representation. Results obtained through experimentation on both the public WOS dataset and an E-commerce user intent classification dataset demonstrate that our proposed model can achieve superlative results.

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“…In this study, the ontology model is used to construct a knowledge graph of traffic safety events in universities [29][30][31]. This is accomplished by combining the distinct characteristics of the information about these events.…”

Section: Ontology Modeling Designmentioning

confidence: 99%

Construction of an Event Knowledge Graph Based on a Dynamic Resource Scheduling Optimization Algorithm and Semantic Graph Convolutional Neural Networks

Liu,

Zhang,

Zheng

et al. 2023

Electronics

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Presently, road and traffic control construction on most university campuses cannot keep up with the growth of the universities. Campus roads are not very wide, crossings do not have lights, and there are no full-time traffic management personnel. Teachers and students are prone to forming a peak flow of people when going to and from classes. This has led to a constant stream of traffic accidents. It is critical to conduct a comprehensive analysis of this issue by utilizing voluminous data pertaining to school traffic incidents in order to safeguard the lives of faculty and students. In the case of domestic universities, fewer studies have studied knowledge graph construction methods for traffic safety incidents. In event knowledge graph construction, the reasonable release and recycling of computational resources are inefficient, and existing entity–relationship joint extraction methods are unable to deal with ternary overlapping and entity boundary ambiguity problems in relationship extraction. In response to the above problems, this paper proposes a knowledge graph construction method for university on-campus traffic safety events with improved dynamic resource scheduling algorithms and multi-layer semantic graph convolutional neural networks. The experiment’s results show that the proposed dynamic computational resource scheduling method increases GPU and CPU use by 25% and 9%. On the public dataset, the proposed data extraction model’s F1 scores for event triples increase by 1.3% on the NYT dataset and by 0.4% on the WebNLG dataset. This method can help the relevant university personnel in dealing with unexpected traffic incidents and reduce the impact on public opinion.

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TransO: a knowledge-driven representation learning method with ontology information constraints

Cited by 30 publications

References 23 publications

Negative sampling and rule mining for explainable link prediction in knowledge graphs

Negative sampling and rule mining for explainable link prediction in knowledge graphs

UMP-MG: A Uni-directed Message-Passing Multi-label Generation Model for Hierarchical Text Classification

Construction of an Event Knowledge Graph Based on a Dynamic Resource Scheduling Optimization Algorithm and Semantic Graph Convolutional Neural Networks

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