In the process of the gradual deepening of the electric power system informatization construction, a large amount of electric power data is accumulated in the electric power field (EPF). In order to effectively organize, manage and utilize the knowledge of large amounts of power data, knowledge graph (KG) technology is introduced into the field of electric power system. Firstly, the concept, classification and construction method of KG in EPF are introduced, and the key technology and difficult problems are elaborated in detail. Secondly, the typical application of KG in EPF is analyzed from three aspects: health management of electric power equipment, fault handling of power distribution network, and supervision and transaction of electric power market. Finally, the application prospect of KG in EPF is discussed, which provides the reference for further in-depth research on the application of KG technology in EPF. 

Construction and Application of Knowledge Graph in Electric Power Field

Representation Learning (RL) aims to convert data into low-dimensional and dense real-valued vectors, so as to realize reasoning in vector space. RL is one of the important research contents in the analysis of health data. This paper systematically reviews the latest research on Electronic Health Records (EHR) RL. We searched the Web of Science, Google Scholar, and Association for Computing Machinery Digital Library for papers involving EHR RL. On the basis of literature review, we propose a new taxonomy to categorize the state-of-the-art EHR RL methods into three categories: statistics learning-based RL methods, knowledge RL methods and graph RL methods. We analyze and summarize their characteristics according to the input data form and underlying learning mechanisms. In addition, we provide evaluation strategies to verify the quality of EHR representations from both intrinsic and extrinsic perspectives. Finally, we put forward three promising research directions to promote future research. Overall, this survey aims to provide a profound overview of state-of-the-art developments in the field of EHR RL and to help researchers find the most appropriate methods.

Recent Advances in Representation Learning for Electronic Health Records: A Systematic Review

Joint extraction of named entities and their relations has the advantage of avoiding cascading failures caused by falsely recognized named entities. Recent studies have focused on span classification modes to support end-to-end multiobjective learning. However, the enumeration of a large number of inaccurate entity spans creates a serious data imbalance and incurs high computational complexity. In this study, we propose a boundary regression model for joint entity and relation extraction, where a boundary regression mechanism is adopted to learn the offset of a possible named entity relevant to a true named entity. Instead of exhaustively enumerating all possible entity spans, this model receives only a small number of coarse entities with inaccurate boundaries as inputs. It can locate named entities and extract relations between them simultaneously. Experiments demonstrated that our boundary regression model outperforms state-of-the-art models in terms of the F1 score by ＋2.5%, ＋0.4%, ＋2.1%, and ＋1.3% on ADE, ACE05, ACE04, and CoNLL04 benchmark datasets respectively. Analytical experiments further confirmed the effectiveness of our model for refining entity boundaries and learning accurate span representations. 

Boundary regression model for joint entity and relation extraction

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer ``Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness ``6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0. 

The survey on multi-source data fusion in cyber-physical-social systems: Foundational infrastructure for industrial metaverses and industries 5.0

Ontology embedding methods have been popular in recent years, especially when it comes to representation learning algorithms for solving ontology-related tasks. Despite the impact of large language models on knowledge graphs’ related tasks, there has been less focus on adapting these models to construct ontology embeddings that are both semantically relevant and faithful to the ontological structure. In this paper, we present a novel ontology embedding method that encodes ontology classes into a pre-trained SBERT through random walks and then fine-tunes the embeddings using a distance-based regression loss. We benchmark our algorithm on four different datasets across two tasks and show the impact of transfer learning and our distance-based loss on the quality of the embeddings. Our results show that SORBET outperform state-of-the-art ontology embedding techniques for the performed tasks. 

SORBET: A Siamese Network for Ontology Embeddings Using a Distance-Based Regression Loss and BERT

Knowledge representation learning (KRL) is one of the important research topics in artificial intelligence and Natural language processing. It can efficiently calculate the semantics of entities and relations in a low-dimensional space, and effectively solve the problem of data sparsity, which significantly improve the performance of knowledge acquisition, fusion and reasoning and so on. Starting from the five perspectives of distance-based, semantic matching, bilinear-based, neural network model and additional information model, this paper first introduces the overall framework and specific model design, and then correspondingly introduces the experimental evaluation tasks, metrics and benchmark datasets of each model. On this basis, how to apply KRL to various downstream tasks is summarized.

A Survey on Approaches and Applications of Knowledge Representation Learning

Joint extraction of entities and relations is an important task in the field of natural language processing and the basis of many NLP high-level tasks. However, most existing joint models cannot solve the problem of overlapping triples well. We propose an efficient end-to-end model for joint extraction of entities and overlapping relations. Firstly, the BERT pre-training model is introduced to model the text more finely. Next, We decompose triples extraction into two subtasks: head entity extraction and tail entity extraction, which solves the problem of single entity overlap in the triples. Then, We divide the tail entity extraction into three parallel extraction sub-processes to solve entity pair overlap problem of triples, that is the relation overlap problem. Finally, We transform each extraction sub-process into sequence tag task. We evaluate our model on the New York Times (NYT) dataset and achieve overwhelming results compared with most of the current models, Precise =0.870, Recall = 0.851, and F1 = 0.860. The experimental results show that our model is effective in dealing with triples overlap problem.

An Advanced BERT-Based Decomposition Method for Joint Extraction of Entities and Relations

In the last few years, the availability of temporal knowledge graphs has stimulated extensive research in temporal knowledge graph completion (TKGC) and temporal knowledge graph embedding (TKGE), where temporal information is added to static knowledge graphs that have been widely applied previously. However, most existing methods, such as current state-of-the-art DE-SimplE and TeRo, learn embeddings of temporal-evolving attributes, overlooking the inherent attributes inside entities, where some essential and inherent features are included. In this paper, we introduce a novel method utilizing Inherent Attributes with a Graph Attention network (IAGAT) for TKGC. Our IAGAT extracts inherent attributes from sufficient features corresponding to various facts at different time stamps, to obtain the inherent embeddings. And we take advantages of previous rotation based methods to obtain the temporal-evolving embed-dings. Through extensive experiments and sufficient comparisons, we demonstrate our model outperforms the current state-of-the-art models on link prediction task. Furthermore, we evaluate and prove the necessity of the inherent attributes in performance improvement, and study how our model functions in extracting inherent features.

Focus on Inherent Attributes for Temporal Knowledge Graph Completion

Knowledge representation learning (KRL) is one of the most important research topics in artificial intelligence, especial in natural language processing (NLP). After extracting entities and relations, some kinds of knowledge, KRL can efficiently calculate the semantics of the entities and the relations in a low-dimensional space, which effectively solve the problem of data sparsity, and can significantly improve the performance of knowledge acquisition, fusion and reasoning. Starting from the three common perspectives of KRL, scoring function, model coding type and additional information, this chapter introduces the overall framework of KRL and the specific model design. In addition, we introduce the corresponding experimental evaluation tasks, including the evaluation metrics and benchmark datasets of each model. Afterwards, we summarize how to apply KRL in various downstream NLP tasks.

Applications of Knowledge Representation Learning

As the basis of many knowledge graph completion tasks, the embedding representation of entities and relations in knowledge graph (KG) is an important task in the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI). While most of the existing knowledge graph embedding (KGE) models based on convolutional neural network (CNN) can obtain abundant feature embedding, they may ignore an important fact that the triples in the KG come from the text, as they simply learn about the feature embedding of entities and relations without considering contextual information. Therefore, in this paper, we propose an effective KGE model based on neural network. First of all, we convert the triple (h,r,t) of the KG into a sentence [h r t]. Then, the LSTM neural network is used to learn the long-term dependence of sentences from the input feature vectors. Then, on this basis, the two-layer convolutional neural network with several different filters is used to extract different local features. Finally, the obtained feature vectors are connected together, and the inner product is carried out with the weight vectors to obtain the score of the triple, so as to judge the validity of the given triple. We evaluate our model on two benchmark datasets FB15k-237 and WN18RR, the experimental results show that the model can effectively improve the accuracy of link prediction, achieving better results compared with other baseline models.

A Knowledge Graph Embedding Method Based on Neural Network

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