Trust Management for Semantic Web

Tahajod, Maryam; Iranmehr, Azadeh; Khozooyi, Nasim

doi:10.1109/iccee.2009.241

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…In the EMAIL network nodes represent Enron email addresses and two addresses are connected if there has been at least one email exchanged between them [22] (36,692 nodes and 183,831 edges). In the TRUST network nodes represent epinions.com members and two members are connected if one trusts the other [41] (75,879 nodes and 405,740 edges). In the SDOT network nodes represent slashdot.org members and two members are connected if they are friends or foes [28] (82,168 nodes and 504, 230 edges).…”

Section: Preferential Attachment Networkmentioning

confidence: 99%

Density Decompositions of Networks

Borradaile

Migler

Wilfong

2018

Complex Networks IX

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We introduce a new topological descriptor of a network called the density decomposition which is a partition of the nodes of a network into regions of uniform density. The decomposition we define is unique in the sense that a given network has exactly one density decomposition. The number of nodes in each partition defines a density distribution which we find is measurably similar to the degree distribution of given real networks (social, internet, etc.) and measurably dissimilar in synthetic networks (preferential attachment, small world, etc.).

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Section: Preferential Attachment Networkmentioning

confidence: 99%

Density Decompositions of Networks

Borradaile

Migler

Wilfong

2018

Complex Networks IX

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“…Therefore, the similarity function θ defined above, can be extended to deal with propagation, following a natural interpretation that, as trust, similarity is transitive at least by some extent [23], [25]. This allows to yield an overall tag scoring scheme that depends on the entire network instead of only the user's vicinity.…”

Section: Preliminariesmentioning

confidence: 98%

“…This allows to yield an overall tag scoring scheme that depends on the entire network instead of only the user's vicinity. We infer the similarity value between two users u 1 and u n , connected through a path p = (u 1 , ..., u n ), by multiplying the similarity values observed at each step [25], [23]. In the following, we denote this extended similarity by θ + .…”

Section: Preliminariesmentioning

confidence: 99%

“…For instance, two users u 1 and u 2 may have different areas of interest and yet share a common neighbor v who may be, by some aspects, similar to u 1 and u 2 . Many works addressed the problem of social link propagation, mainly in the case of trust networks [23], [5]. In this paper, we consider transitivity as a key element for similarity propagation, and we compute it by multiplying the similarity values observed at each step.…”

Section: Preliminariesmentioning

confidence: 99%

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A Social and Popularity-Based Tag Recommender

Abdessalem

Nacke

2014

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

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Tag recommendation aims to recommend to a user the most suited tags for a given item. It is an important functionality of resource sharing systems. In this paper we propose a recommendation algorithm, called FasTag, that links the relevance of the tags to both their popularity and the opinions of the user's neighbors. FasTag assumes that the users are organized in a weighted graph representing, for instance, the similarity or the trust between them.The two salient aspects of FasTag are the scoring function it uses to evaluate the relevance of the tags, and its low computation cost. Thus, FasTag can make online recommendations, even for large datasets.Moreover, we improve the accuracy of its recommendations by adjusting automatically the size of the recommended list of tags. The experiments we did on several datasets show a significant improvement of the accuracy of the recommendations.

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“…Fuzzy logic works for degrees of members of different intervals of values. These fuzzy values are combined to reach a single trust value[38]. Fuzzy is categorized into three steps, fuzzification, fuzzy rules, and defuzzification.…”

mentioning

confidence: 99%

A Review on Trust Management in the Social Internet of Things: Issues and Challenges

Singh¹,

Chaurasia²,

Lal³

2022

tijer.org

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In today's era, every electrical and electronic device is connected very intelligently. These electronic devices like sensors, RFIDs, actuators are becoming an important part of the structure. It is required to connect to the internet. Internet is used in every aspect of daily life. There are drastic changes in the usage of the most demanding and useful technology known as the Internet of Things (IoT). In this field, IoT has been shown one of the important which connect various physical objects with the internet with unique identification. Billions of objects are enabled with the internet and interconnected with each other to interface with Human-to-Machine or Machine-to-Machine to take decisions of data transaction without human involvement. This internet enables devices connected to our social life which is terme as Social Internet of Things (SIoT). Hence we can say SIoT is an expansion IoT. People and things are connected with social networks which are known as Human to Things (H2T) interactions. Some of the devices are authentic and some are suspicious. The essential utilization of services within SIoT makes a secured network closely rely on origin of services (Service provider) and services accesed by end terminal (Service requester). Due to a lack of trust technologies, trust SIoT is still not so much popular in the market as demanding technology for the researcher and developer. Device owners are worried about sharing the data or receiving any information. Therefore, trust devices are required to identify in the connected network..Hence minimization of risk and uncertaintity within the system are analyse by the degree of trust for Particular Trust Management System(TMS).Trust and Trustworthiness are novel fields in social networking environment clearlify by investigating the research article under SIoT. In present paper, our contribution starts with the introduction and then is further fragmented in three phases.The first phase represent the basic of trust and SIoT. Second phase categorizes the trust management solutions from the literature review. The third phase identifies and discuses issues, challenges and requirement in current scenario of SIoT and also depicts how developing trust and trustworthiness among social devices interaction as challenging task.

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Trust Management for Semantic Web

Cited by 8 publications

References 8 publications

Density Decompositions of Networks

Density Decompositions of Networks

A Social and Popularity-Based Tag Recommender

A Review on Trust Management in the Social Internet of Things: Issues and Challenges

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