Search results

1 – 10 of 42
To view the access options for this content please click here
Article
Publication date: 5 July 2021

Xuhui Li, Liuyan Liu, Xiaoguang Wang, Yiwen Li, Qingfeng Wu and Tieyun Qian

The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build…

Abstract

Purpose

The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data.

Design/methodology/approach

A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail.

Findings

MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance.

Originality/value

The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.

Details

The Electronic Library , vol. 39 no. 3
Type: Research Article
ISSN: 0264-0473

Keywords

To view the access options for this content please click here
Article
Publication date: 12 June 2019

Xuhui Li, Yanqiu Wu, Xiaoguang Wang, Tieyun Qian and Liang Hong

The purpose of this paper is to explore a semantics representation framework for narrative images, conforming to the image-interpretation process.

Abstract

Purpose

The purpose of this paper is to explore a semantics representation framework for narrative images, conforming to the image-interpretation process.

Design/methodology/approach

This paper explores the essential features of semantics evolution in the process of narrative images interpretation. It proposes a novel semantics representation framework, ESImage (evolution semantics of image) for narrative images. ESImage adopts a hierarchical architecture to progressively organize the semantic information in images, enabling the evolutionary interpretation under the support of a graph-based semantics data model. Also, the study shows the feasibility of this framework by addressing the issues of typical semantics representation with the scenario of the Dunhuang fresco.

Findings

The process of image interpretation mainly concerns three issues: bottom-up description, the multi-faceted semantics representation and the top-down semantics complementation. ESImage can provide a comprehensive solution for narrative image semantics representation by addressing the major issues based on the semantics evolution mechanisms of the graph-based semantics data model.

Research limitations/implications

ESImage needs to be combined with machine learning to meet the requirements of automatic annotation and semantics interpretation of large-scale image resources.

Originality/value

This paper sorts out the characteristics of the gradual interpretation of narrative images and has discussed the major issues in its semantics representation. Also, it proposes the semantic framework ESImage which deploys a flexible and sound mechanism to represent the semantic information of narrative images.

Details

The Electronic Library , vol. 37 no. 3
Type: Research Article
ISSN: 0264-0473

Keywords

Content available
Article
Publication date: 2 November 2021

Oksana Zavalina, Xiaoguang Wang and Qikai Cheng

Abstract

Details

The Electronic Library , vol. 39 no. 3
Type: Research Article
ISSN: 0264-0473

To view the access options for this content please click here
Article
Publication date: 14 January 2021

Xiaoguang Wang, Ningyuan Song, Xuemei Liu and Lei Xu

To meet the emerging demand for fine-grained annotation and semantic enrichment of cultural heritage images, this paper proposes a new approach that can transcend the…

Downloads
274

Abstract

Purpose

To meet the emerging demand for fine-grained annotation and semantic enrichment of cultural heritage images, this paper proposes a new approach that can transcend the boundary of information organization theory and Panofsky's iconography theory.

Design/methodology/approach

After a systematic review of semantic data models for organizing cultural heritage images and a comparative analysis of the concept and characteristics of deep semantic annotation (DSA) and indexing, an integrated DSA framework for cultural heritage images as well as its principles and process was designed. Two experiments were conducted on two mural images from the Mogao Caves to evaluate the DSA framework's validity based on four criteria: depth, breadth, granularity and relation.

Findings

Results showed the proposed DSA framework included not only image metadata but also represented the storyline contained in the images by integrating domain terminology, ontology, thesaurus, taxonomy and natural language description into a multilevel structure.

Originality/value

DSA can reveal the aboutness, ofness and isness information contained within images, which can thus meet the demand for semantic enrichment and retrieval of cultural heritage images at a fine-grained level. This method can also help contribute to building a novel infrastructure for the increasing scholarship of digital humanities.

Details

Journal of Documentation, vol. 77 no. 4
Type: Research Article
ISSN: 0022-0418

Keywords

To view the access options for this content please click here
Article
Publication date: 11 December 2017

Xiaoguang Wang, Ningyuan Song, Lu Zhang and Yanyu Jiang

The purpose of this paper is to understand the subjects contained in the Dunhuang mural images as well as their relation structures.

Abstract

Purpose

The purpose of this paper is to understand the subjects contained in the Dunhuang mural images as well as their relation structures.

Design/methodology/approach

This paper performed content analysis based on Panofsky’s theory and 237 research papers related to the Dunhuang mural images. UNICET software was also used to study the correlation structures of subject network.

Findings

The results show that the three levels of subject have all captured the attention of Dunhuang mural researchers, the iconology occupy the critical position in the whole image study, and the correlation between iconography and iconology was strong. Further analysis reveals that cultural development, production, and power and domination have high centralities in the subject network.

Research limitations/implications

The research samples come from three major Chinese journal databases. However, there are still many authoritative monographs and foreign publications about the Dunhuang murals which are not included in this study.

Originality/value

The results uncover the subject hierarchies and structures contained in the Dunhuang murals from the angle of image scholarship which express scholars’ intention and contribute to the deep semantic annotation on digital Dunhuang mural images.

Details

Journal of Documentation, vol. 74 no. 2
Type: Research Article
ISSN: 0022-0418

Keywords

To view the access options for this content please click here
Article
Publication date: 30 December 2019

Xiaoguang Wang, Tao Lv and Donald Hamerly

The purpose of this paper is to provide insights on the improvement of academic impact and social attention of Chinese collaboration articles from the perspective of altmetrics.

Abstract

Purpose

The purpose of this paper is to provide insights on the improvement of academic impact and social attention of Chinese collaboration articles from the perspective of altmetrics.

Design/methodology/approach

The authors retrieved articles which are from the Chinese Academy of Sciences (CAS) and indexed by Nature Index as sampled articles. With the methods of distribution analysis, comparative analysis and correlation analysis, authors compare the coverage differences of altmetric sources for CAS Chinese articles and CAS international articles, and analyze the correlation between the collaborative information and the altmetric indicators.

Findings

Results show that the coverage of altmetric sources for CAS international articles is greater than that for CAS Chinese articles. Mendeley and Twitter cover a higher percentage of collaborative articles than other sources studied. Collaborative information, such as number of collaborating countries, number of collaborating institutions, and number of collaborating authors, show moderate or low correlation with altmetric indicator counts. Mendeley readership has a moderate correlation with altmetric indicators like tweets, news outlets and blog posts.

Practical implications

International scientific collaboration at different levels improves attention, academic impact and social impact of articles. International collaboration and altmetrics indicators supplement each other. The results of this study can help us better understand the relationship between altmetrics indicators of articles and collaborative information of articles. It is of great significance to evaluate the influence of Chinese articles, as well as help to improve the academic impact and social attention of Chinese collaboration articles.

Originality/value

To the best of authors’ knowledge, few studies focus on the use of altmetrics to assess publications produced through Chinese academic collaboration. This study is one of a few attempts that include the number of collaborating countries, number of collaborating institutions, and number of collaborating authors of scientific collaboration into the discussion of altmetric indicators and figured out the relationship among them.

Details

Library Hi Tech, vol. 38 no. 3
Type: Research Article
ISSN: 0737-8831

Keywords

To view the access options for this content please click here
Article
Publication date: 29 March 2013

Xiaoguang Wang

This paper aims to analyze the exchange and reciprocal mechanism behind individual knowledge transfer activities as well as their impact on the individual knowledge

Downloads
1674

Abstract

Purpose

This paper aims to analyze the exchange and reciprocal mechanism behind individual knowledge transfer activities as well as their impact on the individual knowledge transfer networks.

Design/methodology/approach

The author conducted theoretical and simulation research. Agent‐based technology is employed to construct an agent dynamics agent‐based model that simulates and explains how an individual initiates the evolution of a knowledge network through knowledge transfer activities.

Findings

The results demonstrate that the two mechanisms can improve the knowledge levels of the network members; the exchange mechanism is more efficient as it can improve the values of both sides. Individual knowledge transfer networks evolve from random networks to small‐world networks.

Research limitations/implications

The research model must include more variables. Computer simulation research will be cross‐confirmed by other research methods in future studies.

Practical implications

Individual knowledge transfer networks form and subsequently evolve as a result of social interaction. The research findings will contribute to the policy making for knowledge management in organizations.

Originality/value

Little has been published about the dynamics of individual knowledge transfer networks. The author believes that the paper is the first to analyze the internal mechanisms behind individual knowledge transfer activities and test them with agent‐based technologies.

To view the access options for this content please click here
Article
Publication date: 8 March 2021

Xin Feng, Liangxuan Li, Jiapei Li, Meiru Cui, Liming Sun and Ye Wu

This paper aims to study the characteristics and evolution rules of tagging knowledge network for users with different activity levels in question-and-answer (Q&A…

Abstract

Purpose

This paper aims to study the characteristics and evolution rules of tagging knowledge network for users with different activity levels in question-and-answer (Q&A) community represented by Zhihu.

Design/methodology/approach

A random sample of issue tag data generated by topics in the Zhihu network environment is selected. By defining user quality and selecting the top 20% and bottom 20% of users to focus on, i.e. top users and bot users, the authors apply time slicing for both types of data to construct label knowledge networks, use Q-Q diagrams and ARIMA models to analyze network indicators and introduce the theory and methods of network motif.

Findings

This study shows that when the power index of degree distribution is less than or equal to 3.1, the ARIMA model with rank index of label network has a higher fitting degree. With the development of the community, the correlation between tags in the tagging knowledge network is very weak.

Research limitations/implications

It is not comprehensive and sufficient to classify users only according to their activity levels. And traditional statistical analysis is not applicable to large data sets. In the follow-up work, the authors will further explore the characteristics of the network at a larger scale and longer timescale and consider adding more node features, including some edge features. Then, users are statistically classified according to the attributes of nodes and edges to construct complex networks, and algorithms such as machine learning and deep learning are used to calculate large-scale data sets to deeply study the evolution of knowledge networks.

Practical implications

This paper uses the real data of the Zhihu community to divide users according to user activity and combines the theoretical methods of statistical testing, time series and network motifs to carry out the time series evolution of the knowledge network of the Q&A community. And these research methods provide other network problems with some new ideas. Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates.

Social implications

Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. For the community, understanding the formation mechanism of its network structure and key nodes in the network is conducive to improving the knowledge system of the content, finding user behavior preferences and improving user experience. Future research work will focus on identifying outbreak points from a large number of topics, predicting topical trends and conducting timely public opinion guidance and control.

Originality/value

In terms of data selection, the user quality is defined; the Zhihu tags are divided into two categories for time slicing; and network indicators and network motifs are compared and analyzed. In addition, statistical tests, time series analysis and network modality theory are used to analyze the tags.

Details

Information Discovery and Delivery, vol. 49 no. 2
Type: Research Article
ISSN: 2398-6247

Keywords

To view the access options for this content please click here
Article
Publication date: 14 November 2016

Yuye Wang, Guofeng Zhang and Xiaoguang Hu

Infrared simulation plays an important role in small and affordable unmanned aerial vehicles. Its key and main goal is to get the infrared image of a specific target…

Abstract

Purpose

Infrared simulation plays an important role in small and affordable unmanned aerial vehicles. Its key and main goal is to get the infrared image of a specific target. Infrared physical model is established through a theoretical research, thus the temperature field is available. Then infrared image of a specific target can be simulated properly while taking atmosphere state and effect of infrared imaging system into account. For recent years, some research has been done in this field. Among them, the infrared simulation for large scale is still a key problem to be solved. In this passage, a method of classification based on texture blending is proposed and this method effectively solves the problem of classification of large number of images and increase the frame rate of large infrared scene rendering. The paper aims to discuss these issues.

Design/methodology/approach

Mosart Atmospheric Tool (MAT) is used first to calculate data of sun radiance, skyshine radiance, path radiance, temperatures of different material which is an offline process. Then, shader in OGRE does final calculation to get simulation result and keeps a high frame rate. Considering this, the authors convert data in MAT file into textures which can be easily handled by shader. In shader responding, radiance can be indexed by information of material, vertex normal, eye and sun. Adding the effect of infrared imaging system, the final radiance distribution is obtained. At last, the authors get infrared scene by converting radiance to grayscale.

Findings

In the fragment shader, fake infrared textures are used to look up temperature which can calculate radiance of itself and related radiance.

Research limitations/implications

The radiance is transferred into grayscale image while considering effect of infrared imaging system.

Originality/value

Simulation results show that a high frame rate can be reached while guaranteeing the fidelity.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 9 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

To view the access options for this content please click here
Article
Publication date: 19 May 2021

Jingjing Guan, Wanfei Wang, Zhigang Guo, Jin Hooi Chan and Xiaoguang Qi

This study aims to propose a comprehensive causal model to examine the relationships between customer experience and four key factors in brand building, i.e., brand…

Abstract

Purpose

This study aims to propose a comprehensive causal model to examine the relationships between customer experience and four key factors in brand building, i.e., brand loyalty, brand trust, brand affect and brand involvement. The dimensionality of customer experience in full-service hotel is also particularly examined in relation to brand building.

Design/methodology/approach

Three steps of data collection were used: interviews of 50 customers on their experiences of staying full-service hotels, a small survey of 176 hotel guests to establish the measurement scale of customer experience and a major survey of 732 hotel customers in ten major Chinese cities to test the model of brand loyalty.

Findings

Customers’ experiences with full-service hotels are proposed to be categorized into functional, affective and social. There is a chain effect from customer experience to brand trust and to brand affect and then to brand loyalty. The brand involvement does moderate relationships between customer experience and brand trust and brand affect but not brand loyalty.

Practical implications

For full-service hotels, social and functional experiences are critical in building brand loyalty, and therefore, they need to be the focal points in the enhancement of customer experience. Also, hoteliers are advised to develop emotional connections between the customers and the hotel brand – an effective way of building trust and affection.

Originality/value

According to the authors’ knowledge, this paper is one of the first few studies to link customer experience to brand loyalty with comprehensive causal effect analysis. This study also contributes to the knowledge of customer experience in the context of the full-service hotel sector.

Details

International Journal of Contemporary Hospitality Management, vol. 33 no. 5
Type: Research Article
ISSN: 0959-6119

Keywords

1 – 10 of 42