3DRTE: 3D Rotation Embedding in Temporal Knowledge Graph

Wang, Jingbin; Zhang, Wang; Chen, Xinyuan; Lei, Jing; Xiaolian, Lai

doi:10.1109/access.2020.3036897

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…The main challenge lies in effectively integrating timestamps into the model to accurately capture and leverage the temporal dynamics of entities, relationships, and the underlying graph, thereby enhancing the ability to predict missing facts [8]. In this regard, researchers have explored various timestamp integration strategies, such as tensor decomposition based on timestamps [9], timestamp transformation-based methods [10], dynamic embeddings [11], and predictions based on historical contexts [12]. The goal is to incorporate timestamps into the fact-scoring function.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Temporal Knowledge Graph Completion via Learning High-Order Connectivity and Attribute Information

Wen,

Mei,

Wang

et al. 2023

Applied Sciences

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Temporal knowledge graph completion (TKGC) refers to the prediction and filling in of missing facts on time series, which is essential for many downstream applications. However, many existing TKGC methods suffer from two limitations: (1) they only consider direct relations between entities and fail to express high-order structural dependencies between entities; and (2) they only leverage relation quadruples of temporal knowledge graphs, ignoring attribute information that contains rich semantic information. This makes them vulnerable to sparsity and incompleteness problems. In response, we propose HCAE, a temporal knowledge graph completion model that includes high-order connectivity and attribute information. This consists mainly of a recursive embedding propagation layer and a multi-head attention aggregation layer. The former leverages a recursive mechanism to update entity embeddings and can learn high-order connectivity information between entities in linear complexity time. The latter leverages an attention mechanism to understand the importance of different attributes for entity representation automatically. Combining high-order connectivity and attribute information can lead to more diverse entity representations and help enhance the model’s ability to infer unknown entities. Comparative experiments on three real-world datasets show that the model’s inference accuracy significantly outperforms other benchmark methods, especially regarding knowledge graphs with many unknown entities or relations.

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Section: Introductionmentioning

confidence: 99%

Enhanced Temporal Knowledge Graph Completion via Learning High-Order Connectivity and Attribute Information

Wen,

Mei,

Wang

et al. 2023

Applied Sciences

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“…KGs are defined as a semantic network comprising entities (nodes) and relationships (edges) 8 . There are two main types of KGs adhering to the Resource Description Framework (RDF) data model or the property graphs model 9 .…”

Section: Introductionmentioning

confidence: 99%

Modelling temporal data in knowledge graphs: a systematic review protocol

et al. 2021

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Background: The benefits of having high-quality healthcare data are well established. However, high-dimensionality and irregularity of healthcare data pose challenges in their management. Knowledge graphs have gained increasing popularity in many domains, as a method for representing data to overcome such challenges. However, little is known about their suitability for use with healthcare data. One important factor in representing data is “time”.Data with time related attributes are considered, temporal data. Temporal data are frequently observed in healthcare and the management of rapidly changing patient data is an ongoing challenge. Traditionally, data models have focused on presenting static data and do not account for temporal data. Temporal data models ensure time consistency in data models and assist analysing the history of data and predicting the future trends in data. Knowledge graphs can include temporal data models and are therefore of interest to the field of healthcare data management. As such, the herein aim is to outline a protocol for an inter-disciplinary systematic review of approaches, applications and challenges in modelling temporal data in knowledge graphs so that we can inform the application of knowledge graphs to healthcare data. Method: The research questions is, what are the existing approaches in modelling temporal data in knowledge graphs. Two sub-questions on applications, and challenges will also be evaluated. ACM digital library, IEEEXplore and ScienceDirect will be searched for this review. The search will be limited to peer-reviewed literature referring to knowledge graphs based on Resource Description Framework (RDF). A narrative synthesis of the papers will be conducted. Conclusion: The findings of this systematic review will be useful for data engineers to better represent data and perform analytics through temporal data modelling. They can be applied in the context of healthcare data and the current challenges faced in managing rapidly changing patient data.

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