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The purpose of this paper is to propose a Resource Description Framework (RDF)-based approach to transform metadata crosswalking from equivalent lexical element mapping into semantic mapping with various contextual relationships. RDF is used as a crosswalk model to represent the contextual relationships implicitly embedded between described objects and their elements, including semantic, hierarchical, granular, syntactic and multiple object relationships to achieve semantic metadata interoperability at the data element level.

This paper uses RDF to translate metadata elements and their relationships into semantic expressions, and also as a data model to define the syntax for element mapping. The feasibility of the proposed approach for semantic metadata crosswalking is examined based on two use cases – the Archives of Navy Ships Project and the Digital Artifacts Project of National Palace Museum in Taipei – both from the Taiwan e-Learning and Digital Archives Program.

As the model developed is based on RDF-based expressions, unsolved issues related to crosswalking, such as sets of shared terms, and contextual relationships embedded between described objects and their metadata elements could be manifested into a semantic representation. Corresponding element mapping and mapping rules can be specified without ambiguity to achieve semantic metadata interoperability.

Five steps were developed to clarify the details of the RDF-based crosswalk. The RDF-based expressions can also serve as a basis from which to develop linked data and Semantic Web applications. More use cases including biodiversity artifacts of natural history museums and literary works of libraries, and conditions, constraints and cardinality of metadata data elements will be required to make revisions to fine tune the proposed RDF-based metadata crosswalk.

In addition to reviving contextual relationships embedded between described objects and their metadata elements, nine types of mapping rules were developed to achieve a semantic metadata crosswalk which will facilitate the design of related mapping software. Furthermore, the proposed approach complements existing crosswalking documents provided by authoritative organizations, and enriches mapping language developed by the CIDOC community.

The development of strategic supply chain mapping techniques has been slowed by four critical issues. The purpose of this paper is to answer a call to address supply chain mapping techniques and issues. It offers recommendations for more readable and useful strategic supply chain maps.

The paper utilized executive MBA, traditional MBA, and graduating logistics undergraduates' applied economic input/output data to refine mapping techniques.

The paper addresses strategic supply chain mapping issues to serve as the next step in advancing the evolution of strategic supply chain mapping techniques. It utilizes geovisualization to provide useful guidance to individuals trying to map their current supply chain and seeking potential improvements.

The paper is limited in that strategic supply chain mapping is in its infancy. Future research may consider use of the technique comparing alternative approaches to a supply chain, as well as the application of Pareto analysis and other metrics to focus on critical components for mapping.

The use of input/output analysis initiates mapping at the macro industry level as a key starting point. Geovisualization techniques allow those mapping to offer a great amount of detail in a simple, easy‐to‐read format to identify the critical components of the specific supply chain.

This paper is the critical next step to help further advance the evolution of strategic supply chain mapping techniques by both practitioner and academic.

The purpose of this paper is to make an overview of 474 publications and 512 patents of FTL from 1987 to 2020 in order to provide a conclusive and comprehensive analysis for researchers in this field, as well as a preliminary knowledge of FTL for interested researchers.

Firstly, the FTL algorithms are classified and its functions are introduced in detail. Secondly, the structures of the publications are analyzed in terms of the fundamental information and the publication of the most productive countries/regions, institutions and authors. After that, co-citation networks of institutions, authors and papers illustrated by VOS Viewer are given to show the relationship among those and the most influential of them is further analyzed. Then, the characteristics of the patent are analyzed based on the basic information and classification of the patent and the most productive inventors. In order to obtain research hotspots and trends in this field, the time-line review and citation burst detection of keywords carried out by Cite Space are made to be visual. Finally, based on the above analysis, it draws some other important conclusions and the development trend of this field.

The research on FTL algorithm is still the top priority in the future, and how to improve the performance of SSD in the era of big data is one of the research hotspots.

This paper makes a comprehensive analysis of FTL with the method of bibliometrics, and it is valuable for researchers can quickly grasp the hotspots in this area.

This article draws the structural characteristics of the publications in this field and summarizes the research hotspots and trends in this field in recent years, aiming to inspire new ideas for researchers.

Examines the who, what and why of the knowledge mapping process used in the visual display of information in contemporary organizations. Knowledge mapping serves as the continuously evolving organizational memory, capturing and integrating strategic explicit knowledge within an organization and between an organization and its external environment. Knowledge map making is treated as a medium of communication over what is important and actionable information (knowledge) in organizational contexts. Effective knowledge maps take into account the who, what and why of the knowledge mapping process. Effective knowledge maps help identify intellectual capital, socialize new members, enhance organizational learning and help anticipate impending threats and/or opportunities.

Properly allocating an organization's activities within a building is vital to reducing the relational complexity arising from process–environment interactions. Multiple relationships are mapped, and certain interferences are only identified after these have been processed. The method/software employed for this task is Mapping Activity Environment Allocation (MAEA). However, data input and interpretation of results depend on the usability conditions of the organization's agents. This paper presents MAEA's usability test results.

Test sessions and interviews were carried out with seven agents registered at a University Hospital. Participants were instructed to think aloud during its use, and immediately afterward, responded to semi-structured interviews. Test sessions were audio recorded and screen captured.

Participants found the software easy to use and pointed out valuable implications for professional and academic use. In addition to relationship, priority and parallelism data, customized visualizations were created, including organizational charts, flowcharts and activity flow routes on the floor plan.

MAEA's simplicity allows non-designers to conduct evidence-based assessments and decisions. It allows designers to test their proposals during the programming and outline proposal stages.

A more detailed definition of design requirements from the beginning increases the conditions to successfully achieve project goals.

The ability to map the allocation of activity-spaces in the pre-design phase of building architecture allows for early identification of interactions, aiding in the development of more robust project requirements during programming.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

This paper aims to propose a new genetically evolved fuzzy cognitive mapping approach as a decision-making framework for analyzing the relationships between the drivers and strategies for green computing adoption.

A focus group discussion among stakeholders in the Philippines is used to establish the relationships between the drivers and strategies of green computing adoption.

The proposed approach significantly reduces the time complexity for developing the fuzzy cognitive maps and provides a basis for comprehensively clustering drivers and strategies that share similar characteristics.

This paper’s results provide insights into how the drivers and strategies of green computing adoption facilitate the intention of adopting stakeholders. Moreover, it provides a framework for analyzing structural relationships that exist between factors in a compliant manner.

To the best of the author’s knowledge, the paper is the first to analyze the drivers and strategies of green computing under a complex systems’ perspective. Moreover, this is the first study to offer lenses in a Philippine scenario.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

This paper aims to present a new kinematics mapping method based on dynamic equivalent points. In teleoperation, this method enables a robotic (follower) arm to mimic human (leader) arm postures and avoid obstacles in a human-like manner.

The information of the human arm is extracted based on the characteristics of human arm motion, and the concept of equivalent points is introduced. Then, an equivalent point is determined to transform the robotic arm with a nonhuman-like kinematic structure into an anthropomorphic robotic arm. Based on this equivalent point, a mapping method is developed to ensure that the two arms are similar. Finally, the similarity between the human elbow angle and robot elbow angle is further improved by using this method and an augmented Jacobian matrix with a compensation coefficient.

Numerical simulations and physical prototype experiments are conducted to verify the effectiveness and feasibility of the proposed method. In environments with obstacles, this method can adjust the position of the equivalent point in real time to avoid obstacles. In environments without obstacles, the similarity between the human elbow angle and robot elbow angle is further improved at the expense of the end-effector accuracy.

This study presents a new kinematics mapping method, which can realize the complete mapping between the human arm and heterogeneous robotic arm in teleoperation. This method is versatile and can be applied to various mechanical arms with different structures.

The purpose of this paper is to map research communities related to heroin-assisted treatment (HAT) and the scientific network they are part of to determine their structure and content.

Co-authorship as the basis for conducting social network analysis with regard to degree, weighted degree, betweenness centrality, and edge betweenness centrality.

A number of central researchers were identified on the basis of the number of their collaborative relations. Central actors were also identified on the basis of their position in the research network. In total, 11 research communities were constructed with different scientific content. HAT research communities are closely connected to medical, psychiatric, and epidemiological research and very loosely connected to social research.

The first mapping of the collaborative network HAT researchers using social network methodology.