A personal knowledge management metamodel based on semantic analysis and social information

López-Quintero, J. F.; Lovelle, Juan Manuel Cueva; Crespo, Rubén González; García‐Díaz, Vicente

doi:10.1007/s00500-016-2437-y

Cited by 18 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since news images have the characteristics of high visual similarity, colour features cannot be used to describe and distinguish news images [14][15][16]. Therefore, in the research of automatic semantic annotation of news images, it is necessary to extract other features of news images.…”

Section: Feature Extractionmentioning

confidence: 99%

Automatic Semantic Annotation of News Images in Mobile Internet of Things and Construction of Semantic Internet of Things System

Wang

Ling

Chen

2022

Preprint

View full text Add to dashboard Cite

With the continuous development of mobile and semantic IoT (Internet of Things), the number of news images is increasing sharply. Automatic semantic annotation technology is an important tool to manage and retrieve news images. Therefore, the study is focused on the automatic semantic annotation of news images. First, the problems of the existing automatic annotation methods of news images are analyzed and studied, and then an automatic semantic annotation method of news images based on the fusion of weight and features is proposed. The proposed method extracts the colour features, texture features, and shape features of news images to determine their stability by using the standard deviation of features, and then calculates the weighting coefficient to realize automatic annotation of the news images. The results of the study in the corel5k dataset show that the accuracy of the weight-feature fusion annotation model is 60% for the colour feature, 62% for the texture feature, and 67% for the mixture of colour, shape, and texture. The accuracy of the fusion of weight and feature annotation method is 7% higher than that of the single feature annotation method. The maximum accuracy of the fusion of weight and feature annotation method is 91.2%. The study provides a reference for the research of automatic annotation of news images.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Automatic Semantic Annotation of News Images in Mobile Internet of Things and Construction of Semantic Internet of Things System

Wang

Ling

Chen

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It is a well-established algorithm to convert unstructured raw textual data into organized information objects and further analyze these objects to recognize patterns for the revelation of learning [2,46,47]. It employs a systematic and comprehensive approach to uncover the research trends in a vast literature dataset [3,21,24,25,[48][49][50][51][52]. is study aims to map the semantic relationship between documents and terms in a large corpus to reveal the varied contextual latent classes using LSA.…”

Section: Proposed Sear Frameworkmentioning

confidence: 99%

Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

et al. 2021

View full text Add to dashboard Cite

Contemporary technologies have ensured the availability of high-quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever-evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty-seven research trends. The study also suggests the potential future research directions.

show abstract

“…Although the knowledge integrator is intended to be easily adapted for use with multiple geo-location data sources in a seamless manner, the prototype developed in this article has been validated using Google and Facebook’s geo-location data. The concept of social network, along with that of collaboration, in its broadest sense, has been extensively applied in knowledge management in a variety of domains such as business and software engineering domains [36–38].…”

Section: Proposed Approachmentioning

confidence: 99%

Towards a knowledge-based probabilistic and context-aware social recommender system

Colombo-Mendoza

Valencia‐García

Rodríguez‐González

et al. 2017

Journal of Information Science

View full text Add to dashboard Cite

In this article, we propose (1) a knowledge-based probabilistic collaborative filtering (CF) recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation (LDA) model-based recommendation technique and (2) a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain (foodservice places). The ontology on which the similarity metric is based is additionally leveraged to model and reason about users’ contexts; the proposed LDA model also guides the users’ context modelling to some extent. An evaluation method in the form of a comparative analysis based on traditional information retrieval (IR) metrics and a reference ranking-based evaluation metric (correctly ranked places) is presented towards the end of this article to reliably assess the efficacy and effectiveness of our recommendation approach, along with its utility from the user’s perspective. Our recommendation approach achieves higher average precision and recall values (8% and 7.40%, respectively) in the best-case scenario when compared with a CF approach that employs a baseline similarity metric. In addition, when compared with a partial implementation that does not consider users’ preferences for topics, the comprehensive implementation of our recommendation approach achieves higher average values of correctly ranked places (2.5 of 5 versus 1.5 of 5).

show abstract

A personal knowledge management metamodel based on semantic analysis and social information

Cited by 18 publications

References 30 publications

Automatic Semantic Annotation of News Images in Mobile Internet of Things and Construction of Semantic Internet of Things System

Automatic Semantic Annotation of News Images in Mobile Internet of Things and Construction of Semantic Internet of Things System

Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

Towards a knowledge-based probabilistic and context-aware social recommender system

Contact Info

Product

Resources

About