A Semantic Collaboration Method Based on Uniform Knowledge Graph

Li, Qi; Cao, Zehong; Tanveer, Mohammad; Pandey, Hari Mohan; Wang, Chen

doi:10.1109/jiot.2019.2960150

Cited by 13 publications

(5 citation statements)

References 36 publications

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“…While Li et al [11] study the characteristics of semantic data and propose the concept of a uniform knowledge graph (UKG), to apply to an Internet of Things environment; we disentangle these 'semantic data' of which the authors speak and structurally analyze them by relating them to the universal structure of language [3][4][5]16,17]. Therefore, we propose a structural architecture for the systems that contemplates the dynamic aspect of human cognition, which is responsible for making it more intuitive.…”

Section: Preliminary Remarksmentioning

confidence: 98%

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The Language Conceptual Formation to Inspire Intelligent Systems

Monte-Serrat

Cattani

2022

Sci

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The semantic web invests in systems that work collaboratively. In this article we show that the collaborative way is not enough, because the system must ‘understand’ the data resources that are provided to it, to organize them in the direction indicated by the system’s core, the algorithm. In order for intelligent systems to imitate human cognition, in addition to technical skills to model algorithms, we show that the specialist needs a good knowledge of the principles that explain how human language constructs concepts. The content of this article focuses on the principles of the conceptual formation of language, pointing to aspects related to the environment, to logical reasoning and to the recursive process. We used the strategy of superimposing the dynamics of human cognition and intelligent systems to open new frontiers regarding the formation of concepts by human cognition. The dynamic aspect of the recursion of the human linguistic process integrates visual, auditory, tactile input stimuli, among others, to the central nervous system, where meaning is constructed. We conclude that the human linguistic process involves axiomatic (contextual/biological) and logical principles, and that the dynamics of the relationship between them takes place through recursive structures, which guarantee the construction of meanings through long-range correlation under scale invariance. Recursion and cognition are, therefore, interdependent elements of the linguistic process, making it a set of sui generis structures that evidence that the essence of language, whether natural or artificial, is a form and not a substance.

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Section: Preliminary Remarksmentioning

confidence: 98%

“…The semantic web aims to build a system that operates collaboratively [2,10,11]. We, on the other hand, suggest that it is not enough that the form is collaborative.…”

Section: Preliminary Remarksmentioning

confidence: 99%

The Language Conceptual Formation to Inspire Intelligent Systems

Monte-Serrat

Cattani

2022

Sci

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“…The interactive information of users and the items for recommendation are usually used by the collaborative filtering algorithms [10][11][12][13]. It seems to be a recommendation algorithm under collective wisdom, which is widely used in the field of recommendation systems.…”

Section: Collaborative Filtering Recommendationmentioning

confidence: 99%

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

Liu¹,

Wan²,

Yan³

2023

Computer Modeling in Engineering &Amp; Sciences

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With the popularity of 5G and the rapid development of mobile terminals, an endless stream of short video software exists. Browsing short-form mobile video in fragmented time has become the mainstream of user's life. Hence, designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements. Nevertheless, the explosive growth of data leads to the low efficiency of the algorithm, which fails to distill users' points of interest on one hand effectively. On the other hand, integrating user preferences and the content of items urgently intensify the requirements for platform recommendation. In this paper, we propose a collaborative filtering algorithm, integrating time context information and user context, which pours attention into expanding and discovering user interest. In the first place, we introduce the temporal context information into the typical collaborative filtering algorithm, and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos. There remains one more point. We also introduce the user situation into the traditional collaborative filtering recommendation algorithm, considering the context information of users in the generation recommendation stage, and weight the recommended short-form videos of candidates. At last, a diverse approach is used to generate a Top-K recommendation list for users. And through a case study, we illustrate the accuracy and diversity of the proposed method.

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“…To solve the heterogeneity problem, using the core of semantic technology (ontology) to semantic annotation for the information of things can provide machines with more understandable information [49]. The term "ontology" derived from philosophy is defined as a formal description of knowledge in computer science.…”

Section: Ontology and Internet Of Thingsmentioning

confidence: 99%

Semantic Web and Internet of Things: Challenges, Applications and Perspectives

Amara

Hemam

Djezzar

et al. 2022

JICTS

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The apparent growth of the internet of things (IoT) has allowed its deployment in many domains. The IoT devices sense their surroundings and transmit the data via the Web. According to statistics, due to the proliferation of smart devices, the number of active IoT devices is expected to exceed 25.4 billion by 2030.1 A large number of IoT objects gather an enormous amount of raw data. The data generated by various IoT objects and sensors are heterogeneous, with varying types and formats. Therefore, it is difficult for IoT systems to share and reuse raw IoT data, which causes the problem of lack of interoperability. The lack of interoperability in IoT systems creates a problematic issue that prevents IoT systems from performing well. To address this issue, data modeling and knowledge representation using semantic web technologies may be an appropriate solution to give meaning to raw IoT data and convert it to an enriched data format. The primary goal of this research section is to highlight the best outcomes for semantic interoperability among IoT systems, which can serve as a guideline for future studies via the presentation of a literature review on semantic interoperability for Internet of Things systems, including challenges, prospects, and recent work. The paper also provides an overview of the application of semantic web technologies in IoT systems, such as specific ontologies, frameworks, and application domains that use semantic technologies in the IoT areas to solve interoperability and heterogeneity problems.

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A Semantic Collaboration Method Based on Uniform Knowledge Graph

Cited by 13 publications

References 36 publications

The Language Conceptual Formation to Inspire Intelligent Systems

The Language Conceptual Formation to Inspire Intelligent Systems

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

Semantic Web and Internet of Things: Challenges, Applications and Perspectives

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