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  • Open Access

    ARTICLE

    Construction Method of Equipment Defect Knowledge Graph in IoT

    Huafei Yang1, Wenqing Yang1, Nan Zhang1, Shanming Wei2,*, Yingnan Shang1

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2745-2765, 2023, DOI:10.32604/iasc.2023.036614

    Abstract Equipment defect detection is essential to the security and stability of power grid networking operations. Besides the status of the power grid itself, environmental information is also necessary for equipment defect detection. At the same time, different types of intelligent sensors can monitor environmental information, such as temperature, humidity, dust, etc. Therefore, we apply the Internet of Things (IoT) technology to monitor the related environment and pervasive interconnections to diverse physical objects. However, the data related to device defects in the existing Internet of Things are complex and lack uniform association hence building a knowledge graph is proposed to solve… 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

    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, uses a variety of databases… More >

  • Open Access

    ARTICLE

    A Weakly-Supervised Method for Named Entity Recognition of Agricultural Knowledge Graph

    Ling Wang, Jingchi Jiang*, Jingwen Song, Jie Liu

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 833-848, 2023, DOI:10.32604/iasc.2023.036402

    Abstract It is significant for agricultural intelligent knowledge services using knowledge graph technology to integrate multi-source heterogeneous crop and pest data and fully mine the knowledge hidden in the text. However, only some labeled data for agricultural knowledge graph domain training are available. Furthermore, labeling is costly due to the need for more data openness and standardization. This paper proposes a novel model using knowledge distillation for a weakly supervised entity recognition in ontology construction. Knowledge distillation between the target and source data domain is performed, where Bi-LSTM and CRF models are constructed for entity recognition. The experimental result is shown… More >

  • Open Access

    ARTICLE

    Code Reviewer Intelligent Prediction in Open Source Industrial Software Project

    Zhifang Liao1, Bolin Zhang1, Xuechun Huang1, Song Yu1,*, Yan Zhang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 687-704, 2023, DOI:10.32604/cmes.2023.027466

    Abstract Currently, open-source software is gradually being integrated into industrial software, while industry protocols in industrial software are also gradually transferred to open-source community development. Industrial protocol standardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informal proposals, and different workflows will lead to increased operating costs. The open-source community maintenance team needs software that is more intelligent to guide the identification and classification of these issues. To solve the above problems, this paper proposes a PR review prediction model based on multi-dimensional features. We extract 43 features of PR and divide them into five dimensions: contributor,… More >

  • Open Access

    ARTICLE

    Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

    Zibo Wang1,3, Chaobin Huo2, Yaofang Zhang1,3, Shengtao Cheng1,3, Yilu Chen1,3, Xiaojie Wei5, Chao Li4, Bailing Wang1,3,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2957-2979, 2023, DOI:10.32604/cmc.2023.035694

    Abstract With the growing discovery of exposed vulnerabilities in the Industrial Control Components (ICCs), identification of the exploitable ones is urgent for Industrial Control System (ICS) administrators to proactively forecast potential threats. However, it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods. To address these challenges, we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph (KG) in which relation paths contain abundant potential evidence to support the reasoning. The reasoning task in this work refers to determining whether a specific relation is valid between an… More >

  • 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

    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 the multimodal knowledge graph VisualSem… 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

    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 in concealment. In this paper,… 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

    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, a novel knowledge graph embedding… 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

    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, schema graph expansion to recent… 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

    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 filling the specific data according… More >

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