A novel method for adaptive knowledge map construction in the aircraft development

Lv, Yanjie; Zhao, Gang; Yu, Yi

doi:10.1007/s11042-015-3113-4

Cited by 9 publications

(5 citation statements)

References 35 publications

(35 reference statements)

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“…A knowledge map, which organizes scattered knowledge artifacts and presents them visually in meaningful categorizations, can expressively, efficiently and effectively facilitate user browsing, navigating and understanding knowledge artifacts in a large knowledge space (Brookes, 1980; Lv et al , 2016; Yoon et al , 2010). Knowledge maps have been widely investigated in practice, as well as in research, in centralized knowledge management (Chiu and Pan, 2014; Kim et al , 2003; Lin et al , 2006; Lv et al , 2014; Wu et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

Construction of personal knowledge maps for a peer-to-peer information-sharing environment

Qin

Zhao

Mou

et al. 2018

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Purpose Browsing knowledge documents in a peer-to-peer (P2P) environment is difficult because knowledge documents in such an environment are large in quantity and distributed over different peers who organize the documents according to their own views. This paper aims to propose a method for constructing a personal knowledge map for a peer to facilitate knowledge browsing and alleviate information overload in P2P environments. Design/methodology/approach The research presents a method for constructing a personal knowledge map. The method adopts an ontology-concept-tree-based classification algorithm to recognize a peer’s personal knowledge structure and construct a personal knowledge map, and uses a self-organizing map algorithm to cluster and visualize the knowledge documents. The correctness of the created knowledge map is evaluated with a collection of abstracts of academic papers. Findings The method for constructing a personal knowledge map is the main finding of this research. The evaluation shows that the created knowledge map is good in quality. Research limitations/implications The proposed method provides a way for P2P platforms to understand their users’ knowledge background, as well as to improve the P2P platform environment. However, the proposed method will not help a peer when he has nothing in his individual knowledge document repository (i.e. the “cold start” problem). The method also requires a relatively good ontology base for a P2P document sharing system to use the method effectively. Originality/value It is novel that the proposed method organizes the knowledge documents related to a peer’s knowledge background into a personal knowledge map. Moreover, the created knowledge map combines the advantages of a hierarchical display and a map display. It has values for a distributed P2P environment to facilitate users’ knowledge browsing and to alleviate information overload.

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Section: Introductionmentioning

confidence: 99%

Construction of personal knowledge maps for a peer-to-peer information-sharing environment

Qin

Zhao

Mou

et al. 2018

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“…This study hypothesises that the AAUKG approach has more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and SSR than do the AAKG and TOCL approaches. Furthermore, previous studies revealed that knowledge graphs and useful information do not increase cognitive load (Lv et al, 2016; Redifer et al, 2021). Thus, we hypothesised that the AAUKG approach does not negatively affect cognitive load.…”

Section: Research Questions and Hypothesesmentioning

confidence: 88%

“…A knowledge graph is defined as a graph used to accumulate and convey knowledge of the real world, whose nodes denote entities of interest and whose edges denote relations between these entities (Hogan et al, 2021). The benefits of knowledge graphs, including structuralising information and knowledge (Shaw, 2019), delivering substantial knowledge, and reducing cognitive load (Lv et al, 2016), have been well documented. In addition, Cui and Yu (2019) compared the effects of knowledge graphs and concept maps in a flipped classroom setting and found that learning with knowledge graphs yielded better performance in terms of the breadth and depth of knowledge.…”

Section: Introductionmentioning

confidence: 99%

An automatic knowledge graph construction approach to promoting collaborative knowledge building, group performance, social interaction and socially shared regulation in CSCL

Zheng

Niu

Long

et al. 2022

Brit J Educational Tech

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Computer‐supported collaborative learning (CSCL) has been an effective pedagogy in the field of education. However, productive collaborative learning often does not occur spontaneously, and learners often have difficulties with collaborative knowledge building and socially shared regulation. To address this research gap, this study proposes an automatic knowledge graph construction approach based on deep neural network models. In total, 63 groups comprising 189 college students participated in this study and were assigned to three conditions, namely, the automatic activated and unactivated knowledge graph (AAUKG) condition, the automated activated knowledge graph (AAKG) condition, and the traditional online collaborative learning (TOCL) condition. The findings revealed that the AAUKG approach had more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. This study provides substantial evidence of utilising both automatically activated and unactivated knowledge graphs to improve collaborative learning performance. What is already known about this topic Knowledge graphs have been widely used in many fields for searches, recommendations, analytics, and automation. Researchers have explored how to construct knowledge graphs. However, few studies have automatically constructed knowledge graphs in the CSCL context. What this paper adds An automatic knowledge graph construction approach is proposed and validated in the CSCL context. The findings indicate that the AAUKG approach has more significant and positive impacts on collaborative knowledge building, group performance, social interaction, and socially shared regulation than the AAKG and TOCL approaches. Implications for practice and/or policy The AAUKG approach is an effective method for improving knowledge building and group performance in the CSCL context. Researchers and practitioners are encouraged to consider how knowledge graphs can be applied to facilitate collaborative learning efficiently.

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“…Lee and Segev (2012) developed a tool for automatically building a domain knowledge map for e-learning using text mining techniques and found that knowledge map learning identifies the more important ideas in the text, compared with document-based learning. Lv, Zhao, and Yu (2016) proposed that a novel method for adaptive knowledge map construction was to solve problems of knowledge overload and indicated that the constructed knowledge map can guide users efficiently, and knowledge overload can be reduced significantly. Gupta and Deotale (2014) found that the knowledge map is useful to increase the growth of the company by giving some useful measures according to the business criteria.…”

Section: Literature Review and Research Framework Knowledge Mapmentioning

confidence: 99%

“…Lv, Zhao, and Yu (2016) proposed that a novel method for adaptive knowledge map construction was to solve problems of knowledge overload and indicated that the constructed knowledge map can guide users efficiently, and knowledge overload can be reduced significantly. Gupta and Deotale (2014) found that the knowledge map is useful to increase the growth of the company by giving some useful measures according to the business criteria.…”

Section: Literature Review and Research Frameworkmentioning

confidence: 99%

The Learning Performance of Different Knowledge Map Construction Methods and Learning Styles Moderation for Programming Language Learning

Shaw

2017

Journal of Educational Computing Research

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This study focused on the relationships between knowledge mapping construction methods and learning performance. The different knowledge mapping methods (by experts vs. by self-learners) were assumed to achieve different learning performance levels. The results of this study show that neither different knowledge map construction methods nor learning styles significantly influenced individual learning performance. Either of the knowledge map construction methods applied to the programming language learning and the learning scores are significantly higher than average. Learning style does not moderate knowledge map construction methods on learning scores. However, learning style is a significant moderator of knowledge map construction methods on learning satisfaction. Different knowledge map construction methods should be considered with different learning styles to significantly increase learning satisfaction. The results could be used to design effective programming language learning materials.

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A novel method for adaptive knowledge map construction in the aircraft development

Cited by 9 publications

References 35 publications

Construction of personal knowledge maps for a peer-to-peer information-sharing environment

Construction of personal knowledge maps for a peer-to-peer information-sharing environment

An automatic knowledge graph construction approach to promoting collaborative knowledge building, group performance, social interaction and socially shared regulation in CSCL

The Learning Performance of Different Knowledge Map Construction Methods and Learning Styles Moderation for Programming Language Learning

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