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  • Open Access

    ARTICLE

    DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

    Leilei Li1, Yansheng Fu2, Dongjie Zhu2,*, Xiaofang Li3, Yundong Sun2, Jianrui Ding2, Mingrui Wu2, Ning Cao4,*, Russell Higgs5

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3295-3307, 2023, DOI:10.32604/iasc.2023.035257 - 15 March 2023

    Abstract The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms. Image and text descriptions added to the knowledge graph enrich the node information, which accounts for the advantage of the multi-modal knowledge graph. In the field of cross-modal retrieval platforms, multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational information provided by knowledge graphs. The representation learning method is significant to the application of multi-modal knowledge graphs. This paper proposes a distributed collaborative vector retrieval platform (DCRL-KG) using… More >

  • Open Access

    ARTICLE

    Topic Controlled Steganography via Graph-to-Text Generation

    Bowen Sun1, Yamin Li1,2,3,*, Jun Zhang1, Honghong Xu1, Xiaoqiang Ma4, Ping Xia2,3,5

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 157-176, 2023, DOI:10.32604/cmes.2023.025082 - 05 January 2023

    Abstract Generation-based linguistic steganography is a popular research area of information hiding. The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to. However, in the course of our experiment, we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text, which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases, and that the topic of generated texts is uncontrollable, so there is still room for improvement… More >

  • Open Access

    ARTICLE

    Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

    Zefeng Gu, Hua Chen*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2497-2514, 2023, DOI:10.32604/cmes.2023.024332 - 23 November 2022

    Abstract Link prediction, also known as Knowledge Graph Completion (KGC), is the common task in Knowledge Graphs (KGs) to predict missing connections between entities. Most existing methods focus on designing shallow, scalable models, which have less expressive than deep, multi-layer models. Furthermore, most operations like addition, matrix multiplications or factorization are handcrafted based on a few known relation patterns in several well-known datasets, such as FB15k, WN18, etc. However, due to the diversity and complex nature of real-world data distribution, it is inherently difficult to preset all latent patterns. To address this issue, we propose KGE-ANS, More >

  • Open Access

    ARTICLE

    ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

    Byeongmin Choi1, YongHyun Lee1, Yeunwoong Kyung2, Eunchan Kim3,*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 71-82, 2023, DOI:10.32604/iasc.2023.032783 - 29 September 2022

    Abstract Recently, pre-trained language representation models such as bidirectional encoder representations from transformers (BERT) have been performing well in commonsense question answering (CSQA). However, there is a problem that the models do not directly use explicit information of knowledge sources existing outside. To augment this, additional methods such as knowledge-aware graph network (KagNet) and multi-hop graph relation network (MHGRN) have been proposed. In this study, we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers (ALBERT) with knowledge graph information extraction technique. We also propose to applying the novel method, More >

  • Open Access

    ARTICLE

    Future Event Prediction Based on Temporal Knowledge Graph Embedding

    Zhipeng Li1,2, Shanshan Feng3,*, Jun Shi2, Yang Zhou2, Yong Liao1,2, Yangzhao Yang2, Yangyang Li4, Nenghai Yu1, Xun Shao5

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2411-2423, 2023, DOI:10.32604/csse.2023.026823 - 01 August 2022

    Abstract Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes. Knowledge graph is a general language for describing and modeling complex systems. Different types of events continually occur, which are often related to historical and concurrent events. In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem. Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process. As a result, they cannot effectively… More >

  • Open Access

    ARTICLE

    Design and Implementation of Police Equipment Knowledge Query System

    Chenxi Yu, Xin Li*

    Journal of Quantum Computing, Vol.4, No.2, pp. 63-74, 2022, DOI:10.32604/jqc.2022.027715 - 16 May 2023

    Abstract In the field of public security, the standardized use of police equipment can better assist the public security police in performing their duties. With the advancement of science and technology of the times, police equipment is also constantly developing, and more and more new types of police equipment have appeared. Nowadays, there are a large number and variety of police equipment, and public security police are facing the challenge of mastering and updating equipment knowledge. This article builds a knowledge base of police equipment based on the knowledge of opening source data on the Internet,… More >

  • Open Access

    ARTICLE

    A Top-down Method of Extraction Entity Relationship Triples and Obtaining Annotated Data

    Zhiqiang Hu1, Zheng Ma1, Jun Shi1, Zhipeng Li1, Xun Shao1,2, Yangzhao Yang1,*, Yong Liao1, Zhenyuan Gao1, Jie Zhang1

    Journal of Quantum Computing, Vol.4, No.1, pp. 13-22, 2022, DOI:10.32604/jqc.2022.026785 - 12 August 2022

    Abstract The extraction of entity relationship triples is very important to build a knowledge graph (KG), meanwhile, various entity relationship extraction algorithms are mostly based on data-driven, especially for the current popular deep learning algorithms. Therefore, obtaining a large number of accurate triples is the key to build a good KG as well as train a good entity relationship extraction algorithm. Because of business requirements, this KG’s application field is determined and the experts’ opinions also must be satisfied. Considering these factors we adopt the top-down method which refers to determining the data schema firstly, then… More >

  • Open Access

    ARTICLE

    Prerequisite Relations among Knowledge Units: A Case Study of Computer Science Domain

    Fatema Nafa1,*, Amal Babour2, Austin Melton3

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 639-652, 2022, DOI:10.32604/cmes.2022.020084 - 03 August 2022

    Abstract The importance of prerequisites for education has recently become a promising research direction. This work proposes a statistical model for measuring dependencies in learning resources between knowledge units. Instructors are expected to present knowledge units in a semantically well-organized manner to facilitate students’ understanding of the material. The proposed model reveals how inner concepts of a knowledge unit are dependent on each other and on concepts not in the knowledge unit. To help understand the complexity of the inner concepts themselves, WordNet is included as an external knowledge base in this model. The goal is More >

  • Open Access

    ARTICLE

    KGSR-GG: A Noval Scheme for Dynamic Recommendation

    Jun-Ping Yao1, Kai-Yuan Cheng1,*, Meng-Meng Ge2, Xiao-Jun Li1, Yi-Jing Wang1

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5509-5524, 2022, DOI:10.32604/cmc.2022.030150 - 28 July 2022

    Abstract Recommendation algorithms regard user-item interaction as a sequence to capture the user’s short-term preferences, but conventional algorithms cannot capture information of constantly-changing user interest in complex contexts. In these years, combining the knowledge graph with sequential recommendation has gained momentum. The advantages of knowledge graph-based recommendation systems are that more semantic associations can improve the accuracy of recommendations, rich association facts can increase the diversity of recommendations, and complex relational paths can hence the interpretability of recommendations. But the information in the knowledge graph, such as entities and relations, often fails to be fully utilized… More >

  • Open Access

    ARTICLE

    Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph

    Donglei Lu1, Dongjie Zhu2,*, Haiwen Du3, Yundong Sun3, Yansong Wang2, Xiaofang Li4, Rongning Qu4, Ning Cao1, Russell Higgs5

    Computer Systems Science and Engineering, Vol.42, No.3, pp. 1133-1146, 2022, DOI:10.32604/csse.2022.021525 - 08 February 2022

    Abstract The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to the user based on the known historical interaction data of the target user. Furthermore, the combination of the recommended algorithm based on collaborative filtration and other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model (CoFM) is one representative research. CoFM, a fusion recommendation model combining the collaborative filtering model FM and the graph embedding model TransE, introduces the information of many entities and their relations in the… More >

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