다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the development of knowledge graphs. 

https://link.springer.com/content/pdf/10.1007/s10462-023-10465-9.pdf

Knowledge Graphs: Opportunities and Challenges

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions.

Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.

Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

Information fusion for edge intelligence: A survey

Multi-source knowledge fusion is one of the important research topics in the fields of artificial intelligence, natural language processing, and so on. The research results of multi-source knowledge fusion can help computer to better understand human intelligence, human language and human thinking, effectively promote the Big Search in Cyberspace, effectively promote the construction of domain knowledge graphs (KGs), and bring enormous social and economic benefits. Due to the uncertainty of knowledge acquisition, the reliability and confidence of KG based on entity recognition and relationship extraction technology need to be evaluated. On the one hand, the process of multi-source knowledge reasoning can detect conflicts and provide help for knowledge evaluation and verification; on the other hand, the new knowledge acquired by knowledge reasoning is also uncertain and needs to be evaluated and verified. Collaborative reasoning of multi-source knowledge includes not only inferring new knowledge from multi-source knowledge, but also conflict detection, i.e. identifying erroneous knowledge or conflicts between knowledges. Starting from several related concepts of multi-source knowledge fusion, this paper comprehensively introduces the latest research progress of open-source knowledge fusion, multi-knowledge graphs fusion, information fusion within KGs, multi-modal knowledge fusion and multi-source knowledge collaborative reasoning. On this basis, the challenges and future research directions of multi-source knowledge fusion in a large-scale knowledge base environment are discussed.

https://link.springer.com/content/pdf/10.1007/s11280-020-00811-0.pdf

Multi-source knowledge fusion: a survey

Multi-source knowledge fusion can effectively promote the research and development of knowledge graphs in the related fields such as Big Search in Cyberspace, natural language processing, and so on, effectively promote the construction of domain knowledge graphs, and bring enormous social and economic benefits. Starting from several related concepts of multi-source knowledge fusion, this paper comprehensively introduces the latest research progress of open source knowledge fusion, multi-knowledge graphs fusion, information fusion within knowledge graphs and multi-modal knowledge fusion. On this basis, the challenges and future research directions of multi-source knowledge fusion in a large-scale knowledge base environment are discussed.

Multi-source Knowledge Fusion: A Survey

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

The invention relates to a knowledge graph reasoning method and device for concerning neighbor entities. The knowledge graph reasoning method comprises the following steps: obtaining an initial embedded representation of an entity and a relationship in the knowledge graph; obtaining a to-be-inferred problem according to the to-be-inferred problem in the knowledge graph; constructing neighbor subgraphs, aiming at each neighbor node in the neighbor subgraph; and setting the attention probability, obtaining feature representation of each pair of triples in the neighbor subgraph, according to theattention probability and the feature represention, obtaining a head entity embedding representation fused with neighbor node information, splicing the head entity embedding representation and an initial embedding representation corresponding to a head entity to obtain an embedding representation of a final head entity, carrying out evaluation sorting on each formed triad according to the embedding representation, and carrying out reasoning according to an evaluation sorting result to obtain a tail entity in the triads. By adopting the knowledge graph reasoning method, invisible nodes can be generalized, and the knowledge graph completion efficiency is improved.

Knowledge graph reasoning method and device for concerning neighbor entities

With the rapid development of Internet technology and the widespread use of social networks in daily life, a large amount of information is propagated on the Web through various interactions among users. Researches on measuring users’ influence are becoming a hot spot, but the traditional methods are not suitable enough for identifying influential individuals in large-scale social networks. According to users’ time series behavior patterns of publishing information and their interested topics, we propose a TBRank model for mining individual influence of uses in different topics. Compared to other methods, our method can distinguish the difference of influence across different topics, and measure the influence of users more accurately. The experimental results on real dataset validate the effectiveness of our work.

Identify Influentials Based on User Behavior Across Different Topics

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