Predicting Semantic Categories in Text Based on Knowledge Graph Combined with Machine Learning Techniques

Mosa, Mohamed Atef

doi:10.1080/08839514.2021.1966883

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…(2019) proposed that the knowledge graph was the next-generation infrastructure for semantic scholarly knowledge [ 14 ]. Mosa (2021) proposed that the knowledge graph could help with semantic category prediction [ 15 ]. Zhou et al (2022) combined the knowledge graph with semantic communication to improve the validity of communication [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Reliable Semantic Communication System Enabled by Knowledge Graph

Jiang

Liu

Zhang

et al. 2022

Entropy

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Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.

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Section: Introductionmentioning

confidence: 99%

Reliable Semantic Communication System Enabled by Knowledge Graph

Jiang

Liu

Zhang

et al. 2022

Entropy

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“…Belajar Al-Qur'an adalah suatu kewajiban setiap umat islam, begitu pula mengajarkannya. Menjadikan anak-anak dapat belajar Al-Qur'an dimulai semenjak kecil dengan kewajiban orang tuanya masing-masing (Alkouatli, 2018;Bahari, 2018;Borhani, 2020;Hanafi, 2020;Mosa, 2021;Pratama, 2020). Dengan memberikan pendidikan dan pengajaran Al-Qur'an sejak dini kepada anak-anak muslim akan dapat menunjang perkembangan jiwanya, sesuai dengan nilai islam demi terbentuknya kepribadian muslim yang diharapkan (Syaifullah et al, 2021;Al-Ayyoub, 2018;Alkhateeb, 2020;Hamed, 2021;Hanafi, 2019;Izzaty, 2018;Mohd, 2021;Qayyum, 2018).…”

Section: Pendahuluanunclassified

Penerapan Program Metode Ummi dalam Pembelajaran Membaca Al-Qur’an di SDIT Widya Cendekia (Pengenalan Lapangan Persekolahan di SDIT Widya Cendekia Kelompok 44)

Nugraha,

Desabina,

Haeliah

et al. 2023

pgsd

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This study aims to determine the application of the Ummi method program in learning to read the Qur'an by SDIT Widya Cendekia students and the results obtained from the Ummi method. In carrying out this study the authors used qualitative research this study uses data collection techniques, namely observation, interviews, and documentation. After the data is obtained, the data is processed with several techniques, namely editing and data interpretation. Furthermore, all data were analyzed using qualitative descriptive techniques. The results of this study indicate that the application of the Ummi method program has good results so that it has a good effect on students at Sdit Widya Cendekia.

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“…Global iteration will certainly improve the coverage and accuracy, but this is often at the expense of the computational efficiency of the algorithm. At the same time, the knowledge graph structure or the baseline models of knowledge representations, such as TransE, TransR [ 12 ], and PTransE [ 13 ], considers the 1–3 step relationships and proves that the algorithm's reasoning performance can be improved [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Multipath Cross Graph Convolution for Knowledge Representation Learning

Tian

Yang

Yin

et al. 2021

Computational Intelligence and Neuroscience

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In the past, most of the entity prediction methods based on embedding lacked the training of local core relationships, resulting in a deficiency in the end-to-end training. Aiming at this problem, we propose an end-to-end knowledge graph embedding representation method. It involves local graph convolution and global cross learning in this paper, which is called the TransC graph convolutional network (TransC-GCN). Firstly, multiple local semantic spaces are divided according to the largest neighbor. Secondly, a translation model is used to map the local entities and relationships into a cross vector, which serves as the input of GCN. Thirdly, through training and learning of local semantic relations, the best entities and strongest relations are found. The optimal entity relation combination ranking is obtained by evaluating the posterior loss function based on the mutual information entropy. Experiments show that this paper can obtain local entity feature information more accurately through the convolution operation of the lightweight convolutional neural network. Also, the maximum pooling operation helps to grasp the strong signal on the local feature, thereby avoiding the globally redundant feature. Compared with the mainstream triad prediction baseline model, the proposed algorithm can effectively reduce the computational complexity while achieving strong robustness. It also increases the inference accuracy of entities and relations by 8.1% and 4.4%, respectively. In short, this new method can not only effectively extract the local nodes and relationship features of the knowledge graph but also satisfy the requirements of multilayer penetration and relationship derivation of a knowledge graph.

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Predicting Semantic Categories in Text Based on Knowledge Graph Combined with Machine Learning Techniques

Cited by 9 publications

References 24 publications

Reliable Semantic Communication System Enabled by Knowledge Graph

Reliable Semantic Communication System Enabled by Knowledge Graph

Penerapan Program Metode Ummi dalam Pembelajaran Membaca Al-Qur’an di SDIT Widya Cendekia (Pengenalan Lapangan Persekolahan di SDIT Widya Cendekia Kelompok 44)

Multipath Cross Graph Convolution for Knowledge Representation Learning

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