Trust estimation of the semantic web using semantic web clustering

Shirgahi, Hossein; Mohsenzadeh, Mehran; Javadi, Hamid Haj Seyyed

doi:10.1080/0952813x.2016.1199601

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…For this reason, one of the risks is the security of private data on social networks, according to the concept of FOAF. Such risks have been experienced on social networking sites and are reported in [8,20].…”

Section: The Importance Of Trust In Social Networkmentioning

confidence: 99%

“…A notation for specifying trust and recommendation concepts was presented along with a set of tools for specifying, analyzing and monitoring trust relations. In [20], Shirgahi et al used parameters of social network authority, the value of pages' links authority and semantic authority to assess the trust. In [21], the importance of the trust model was discussed based on user beliefs and credibility.…”

Section: Related Workmentioning

confidence: 99%

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A Fuzzy System for Evaluating Trustworthiness of Users in a Social Network

SHAFIEI¹,

Shirgahi²,

Barzegar³

2022

IIUMEJ

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In recent years, the emergence of various web-based social networks has led to the growth of social network users. These networks have become popular as a medium for disseminating information and communication. Governments and organizations also use social networks as a platform for better services. However, acting in such networks depends on the level of trust that members have with each other. The combination of personality attributes of a person can create a mental impression of the amount of trust that a person has. This amount of trust can affect the person's future interactions. Therefore, trust is an essential and important matter in these networks, especially when someone interacts with someone else on a web-based social network. We discuss this issue in this paper and provide a method for evaluating it. Measuring the accuracy is not easy for the users who are interacting with the social network. Here, the interactions are virtual. In this paper, we have used fuzzy logic to apply ambiguous data and to evaluate trustworthiness by taking into account the various personality attributes of users such as reliability, availability, interest, patience, and adaptability. As we used these attributes as input to the fuzzy system and based on the relevant fuzzy rules, we evaluated the trustworthiness of users in social networks. The proposed fuzzy system is extendable, because in this system, trust can be defined as a set of one or more personality attributes. Epinions social network dataset is also used to simulate and validate the proposed approach. In the proposed method, the MAE value is less than 0.015 and F-Score value more than 0.86. Based on the results, the presented fuzzy system shows an acceptable accuracy for evaluating the trustworthiness of users. ABSTRAK: Sejak beberapa tahun kebelakangan ini, kemunculan pelbagai rangkaian web sosial telah menyebabkan pertumbuhan pengguna rangkaian sosial. Rangkaian ini telah menjadi popular sebagai medium penularan informasi dan komunikasi. Kerajaan dan organisasi juga menggunakan rangkaian sosial sebagai platfom bagi menyediakan servis perkhidmatan terbaik. Namun, pemakaian rangkaian ini bergantung kepada kepercayaan pengguna antara sesama pengguna. Gabungan ciri-ciri personaliti terhadap seseorang menyebabkan terciptanya persepsi secara mental pada kepercayaan ke atas seseorang. Jumlah kepercayaan ini akan memberi kesan terhadap interaksi yang akan berlaku pada masa depan ke atas individu tersebut. Oleh itu, kepercayaan sangat penting dalam rangkaian ini, terutama apabila seseorang berinteraksi dengan mereka di jaringan sosial web. Isu ini dibincangkan dalam kajian ini dan kaedah evaluasi turut dihuraikan. Mengukur ketepatan pengguna dalam jaringan sosial tidak mudah. Di sini, interaksi berlaku secara maya. Kajian ini menggunakan logik kabur pada data tidak jelas dan bagi mengukur tahap kepercayaan, pelbagai ciri personaliti individu diukur, seperti kebolehpercayaan, kebolehdapatan, minat, kesabaran dan kebolehsesuian. Ciri-ciri tersebut digunakan sebagai input kepada sistem rawak dan berdasarkan peraturan rawak, tahap kebolehpercayaan pengguna diukur dalam rangkaian sosial. Sistem rawak yang dicadangkan ini boleh dilanjutkan, kerana dalam sistem ini kepercayaan boleh dimaksudkan sebagai satu set atau lebih ciri-ciri personaliti. Anggapan pada set data rangkaian sosial turut digunakan bagi simulasi dan pengesahan kaedah yang dicadangkan. Bagi kaedah yang dicadangkan ini, nilai MAE adalah kurang daripada 0.015 dan nilai skor-F lebih daripada 0.86. Berdasarkan dapatan kajian ini, sistem rawak yang dikaji ini menunjukkan ketepatan yang boleh diterima bagi mengukur tahap kebolehpercayaan pengguna.

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Section: The Importance Of Trust In Social Networkmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Fuzzy System for Evaluating Trustworthiness of Users in a Social Network

SHAFIEI¹,

Shirgahi²,

Barzegar³

2022

IIUMEJ

Self Cite

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show abstract

“…Shirgahi et al [1] gained the assessment of trust in the Semantic Web and Social Network by clustering the Web of trust in their research first, after that they used distributed aggregation of indirect trust paths to predict trust for nodes that do not have direct trust. This method has an acceptable execution time.…”

Section: Previous Work Reviewmentioning

confidence: 99%

“…The researchers of WWW found that a method to do this is using quality assertion in the link structure between pages [1]. Such a function is also used in social networks.…”

Section: Introductionmentioning

confidence: 99%

Predicting Trust in a Social Network Based on Structural Similarities Using a Multi-Layered Perceptron Neural Network

Danesh

Shirgahi

2021

IIUMEJ

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Although research on social networks is progressing rapidly, the positive and negative effects of this area should be evaluated. One of the problems is that social networks are very broad and anyone can have influence on them. This matter can cause the issue of people with different beliefs. Therefore, determining the amount of trust to various resources on social networks, and especially resources for which there is no previous history on the web, is one of the main challenges in this field. In this paper, we present a method for predicting trust in a social network by structural similarities through the neural network. In this method, the web of trust data set is converted to a structural similarity data set based on the similarity of the trustors and trustees first. Then, on the created data set, a part of the data set is considered as the training data and it is trained based on the multilayer perceptron neural network and then the trained neural network is tested based on the test data. In the proposed method, the MSE value is less than 0.01, which has improved more than 0.02 compared to previous methods. Based on the obtained results, the proposed method has provided acceptable accuracy. ABSTRAK: Walaupun kajian tentang rangkaian sosial adalah sangat pesat, kesan positif dan negatif dalam ruang lingkup ini perlu dinilai. Masalah rangkaian sosial adalah sangat luas dan sesiapa sahaja boleh terpengaruh. Perkara ini akan menyebabkan manusia dengan pelbagai isu kepercayaan. Oleh itu, menentukan nilai kepercayaan melalui pelbagai sumber dalam rangkaian sosial, terutama sumber-sumber yang tidak mempunyai sejarah lepas dalam web, adalah salah satu cabaran dalam bidang ini. Kajian ini membentangkan jangkaan kepercayaan dalam rangkaian sosial melalui persamaan struktur dengan menggunakan rangkaian neural. Kaedah ini ditentukan dengan menukar set data web kepercayaan kepada struktur set data hampir sama berdasarkan kesamaan pemegang dan pemberi amanah. Kemudian, sebilangan set data yang telah dibina ini dipertimbangkan sebagai data latihan dan ia dilatih berdasarkan rangkaian neural perseptron berbagai lapisan dan kemudian rangkaian neural yang terlatih ini diuji berdasarkan data ujian. Dalam kaedah yang dicadangkan ini, nilai MSE adalah kurang daripada 0.01, di mana telah diperbaiki kepada 0.02 lebih daripada kaedah-kaedah sebelum ini. Berdasarkan dapatan kajian, didapati kaedah yang dicadangkan ini menunjukkan ketepatan yang boleh diterima.

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The Research on Fishery Metadata in Bohai Sea Based on Semantic Web

2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

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In this paper, a data sharing and management mechanism suitable for the characteristics of fishery industry was established to clarify the phenomenon of heterogeneous Web data and Information Island based on Semantic Web technology, and unified interface specification information platform was established. Form the specification of metadata from the physical and chemical database, developing and publishing the corresponding metadata management tool, assisting, assisting and guiding a specialized database centre, completing the construction of metadata from the professional database.

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Trust estimation of the semantic web using semantic web clustering

Cited by 13 publications

References 15 publications

A Fuzzy System for Evaluating Trustworthiness of Users in a Social Network

A Fuzzy System for Evaluating Trustworthiness of Users in a Social Network

Predicting Trust in a Social Network Based on Structural Similarities Using a Multi-Layered Perceptron Neural Network

The Research on Fishery Metadata in Bohai Sea Based on Semantic Web

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