Knowledge Reuse Research Articles

Solution of Spatial Transformation Relationship of Similar Ruled Surfaces Based on Registration of Divided Regions

Since the geometric transformation relationship of similar surfaces with complex features, such as local deformation and curvature changes, is hard to be solved through global registration, this paper proposes a method for solving the spatial transformation relationship of similar ruled surfaces based on registration of divided regions. First, an adaptive region division algorithm is proposed to divide similar surfaces, and then, an improved registration algorithm is proposed by adding two constraints which are the curvature feature and differential geometric features of point clouds. Through this improved registration algorithm, the geometric transformation relationship of each sub-region can be solved, and then the spatial geometric transformation relationship of the overall similar surface can be established. Moreover, the improved registration algorithm can ensure that the differential geometric properties of corresponding points are similar after registration, which may provide a basis for mapping and reuse of process knowledge between corresponding points on similar surfaces. Finally, two similar ruled surface blades are taken as examples for simulation verification, the results show that the maximum registration error of each sub-region is 0.025 mm, which is within the allowable error range, and the registration speed of the proposed algorithm is better than the S-ICP algorithm. This proves that the method in this paper is feasible and effective.

KAiPP: An interaction recommendation approach for knowledge aided intelligent process planning with reinforcement learning

Effective reuse of historical process knowledge has been one of the most important technical roadmaps driving traditional human experience-based process planning all the way to intelligent process planning in the context of Industry 4.0. However, effective knowledge reuse in process planning is still made difficult nowadays due to the lack of means to capture the technologists’ preference embedded in the interaction history of technologists with the system, and the mutual influence of process decision-making activities for future knowledge recommendations. To bridge the gap, this work takes in-depth integration of deep learning and reinforcement learning in process planning and proposes a novel dynamic interaction recommendation approach for knowledge aided intelligent process planning (KAiPP) towards Industry 4.0. This work first formalizes KAiPP as a sequential interaction knowledge recommendation scenario, to which a Markov decision process is employed to model the KAiPP scenario. On that basis, a knowledge-aware reinforcement learning model is designed to learn a KAiPP agent through interacting with the KAiPP environment for effective interaction knowledge recommendation in process planning. Here, the technologists’ preference-task information model and dynamic knowledge sub-graph are elaborated as the KAiPP environment to capture the preference of the technologist and mutual influence of process decision-making activities. Finally, experimental results show that the proposed approach achieves a very high recommendation performance that outperforms the state-of-the-art approaches in the process planning domain. In addition, a thus developed KAiPP prototype system provides a reference for industrial implementation of the approach.

Keeping a lower profile: how firms can reduce their digital carbon footprints

Purpose In the pursuit of net-zero, the decarbonization activities of organizations are a critical feature of any sustainability strategy. However, government policy and recent technological innovations do not address the digital carbon footprint of organizations. The paper aims to present the concept of single-use dark data and how knowledge reuse by organizations is a means to digital decarbonization. Design/methodology/approach Businesses in all sectors must contribute to reducing digital carbon emissions globally, and to the best of the authors’ knowledge, this paper is the first to examine “how” from a knowledge (re)use perspective. Drawing on insights from the knowledge creation process, the paper presents a set of pathways to greater knowledge reuse for the reduction of organizations’ digital carbon footprint. Findings Businesses continually collect, process and store knowledge but generally fail to reuse these knowledge assets – referred to as dark data. Consequently, this dark data has a huge impact on energy use and global emissions. This model is the first to show explicit pathways that businesses can follow to sustainable knowledge practices. Practical implications If businesses are to be proactive in their collective pursuit of net-zero, then it becomes paramount that reducing the digital carbon footprint becomes a key sustainability target. The paper presents how this might be accomplished, offering practical and actionable guidance to businesses for digital decarbonization. Originality/value Two critical questions are facing businesses: how can decarbonization be achieved? And can it be achieved at a low-cost? Awareness of the damaging impact digitalization may be having on the environment is in its infancy, yet knowledge reuse is a proactive and cost-effective route to reduce carbon emissions, which is explored in the paper.

A Multiscale Modelling Approach to Support Knowledge Representation of Building Codes

Knowledge representations of building codes are essential and critical resources for the organization, retrieval, sharing, and reuse of implicit knowledge in the AEC industry. Against this background, traditional code compliance checking is time-consuming and error-prone. This research aimed to utilize various knowledge representation techniques to establish a knowledge model of building codes to facilitate the automated code compliance checking. The proposed knowledge model consists of three levels to achieve conceptual, logical, and correlational representations of building codes. The concept-level model provides the basic knowledge elements. The clause-level model was developed based on a unified top schema and provides the conceptual graph, mapping logics, and checking logics of each clause. The code-level model is constructed based on the explicit cross-references and semantic connections between clauses. The investigations on the model applications indicate two aspects. On the one hand, the proposed knowledge model shows high potential for semantic searching and knowledge recommendation. On the other hand, the automated code-compliance-checking processes based on the proposed multiscale knowledge model can achieve three main advantages: guiding designers to create a building model with completely necessary information, mitigating the differences between building information and regulatory information, and making the checking procedures more friendly and relatively transparent to users.

Assessing Design Repository Search Effectiveness

Reuse of design knowledge in systems engineering (SE) has been identified as a potential way to decrease design cycle times and development costs of engineering projects. Design repositories (DR) have been proposed and demonstrated as a way to store design knowledge in a reusable way. Yet little attention has been devoted specifically to the search methods used to identifying useful information within a DR, or how search method effectiveness can be measured or assessed. This paper demonstrates a method for assessing the effectiveness of search approaches within DR. The use of this method in assessing multiple search algorithms is demonstrated by example. The assessment method presented herein can also be used to identify the characteristics of repository data or intended reuse applications that affect the performance of search methods. These results can be used by engineers to inform selection of search methods based on anticipated future DR use cases.

Time identification of design knowledge push based on cognitive load measurement

The reuse of multidisciplinary design knowledge is pivotal in product development because of the increasingly fierce market competition. It can assist designers, particularly those who lack sufficient experience, in making correct decisions and achieving rapid design. Traditionally, designers primarily acquire design knowledge through information retrieval, which is typically time-consuming and inefficient. A solution that is widely considered to overcome the deficiencies of traditional knowledge retrieval approaches and actively provide designers with necessary knowledge is knowledge push. However, achieving the timely push of required knowledge to designers during the design process remains a challenging task. Accordingly, this paper presents a time identification method based on cognitive load measurement to identify the suitable time for design knowledge push. First, behavioral indicators related to the changes in cognitive load are identified by investigating the influence of the load on three types of behaviors: mouse dynamics, keystroke dynamics, and emotional states. Second, the possibility and efficacy of inferring the cognitive load by simultaneously and unobtrusively tracking the three aforementioned behaviors are considered through behavioral observations. Finally, predicting the knowledge push time based on the cognitive load using classification algorithms is investigated. The experimental results indicate that the accuracy of the proposed method in inferring the cognitive load is 55%, and that of push time is 83%.

Open-world link prediction via type-constraint embedding and hybrid attention for knowledge reuse of AI chip design

AI chips play a vital role in real-time AI applications. AI chip design requires vast knowledge reuse in integrated circuits and neural networks, and this knowledge continues to grow at a rapid rate, leading to a heavy burden on designers. Automated methods are urgently needed to handle the massive and rapidly growing literature. In the realm of traditional EDA, deep learning can produce excellent designs by learning from a large number of design examples. However, in front-end design such as algorithm and architecture design, a considerable amount of knowledge exists in the fast-growing literature. The use of this knowledge is still in a primitive state of manual processing. Therefore, this paper leverages the knowledge graph and the link prediction to organize and reuse AI chip design knowledge (ACDK). First, a knowledge graph is built to organize ACDK in triple format. Second, an open-world link prediction model named TC-THA is proposed, which aims at the closed-world limitation, the lack of type constraint, and the deep inner-association learning weakness of the related methods. TC-THA converts the type constraint of ACDK triples into vectors for better training and inference. It also uses a hybrid attention mechanism for the sufficient semantic interaction among inner-triple information, which is used to capture the deep internal associations of knowledge for the superior ACDK reuse. Experiments show that the TC-THA outperforms other methods and achieves the best MRR and H@10 score of 0.34 and 0.51 in ACDK link prediction. A case study of ACDK reuse shows that, for the given problems, the TC-THA predicts highly feasible solutions, which can be confirmed in the latest AI chip studies.

A hybrid evolutionary multitask algorithm for the multiobjective vehicle routing problem with time windows

The vehicle routing problem with time windows (VRPTW), as an important logistic problem, has been widely investigated in recent decades. However, it is still a great challenge for most existing approaches to solve multiobjective VRPTWs (MOVRPTWs) with many conflicting objectives. In this study, a hybrid evolutionary multitask algorithm, termed HEMT, is proposed to address MOVRPTWs under the framework of evolutionary multitasking, where multiple MOVRPTWs are optimized simultaneously by leveraging the similarity between them. In particular, HEMT is characterized by three aspects: (1) an exploration stage with knowledge transfer (ESKT) is designed to globally explore the search space by transferring knowledge across different MOVRPTWs; (2) an exploitation stage with knowledge reuse (ESKR) is employed to further promote the quality of solutions by conducting local searches and reusing problem-specific knowledge; and (3) a tradeoff mechanism (TOM) is suggested to adaptively switch the search between ESKT and ESKR. Furthermore, to verify the efficacy of the proposed HEMT, four suites of new multitasking instances are generated based on 45 real-world MOVRPTW benchmarks under different multitask environments. The experimental results show that HEMT not only effectively solves multiple MOVRPTWs simultaneously but also achieves better performances than the traditional single-task counterparts.

Knowledge Management in Construction Health and Safety Based on Ontology Modeling

Knowledge management in construction health and safety is an intensive process involving different stakeholders. However, this domain’s information is still fragmented and stored in various disordered formats that require systematic structure for reusing and sharing. This study aims to develop a domain ontology, HSM-Onto (Health and Safety Management-Ontology), to construct health and safety knowledge and improve health and safety management decision making. The HSM-Onto could implement the organization, storage and reuse of construction health and safety knowledge. It comprises two primary domain knowledge contexts, including construction project context and risk context. Based on the conducted analyses, the findings show that the HSM-Onto’s health and safety knowledge sharing is effective and equips health and safety employees with sound recommendations for decision making.

Knowledge Management Practices in a Religious Organisation in South Africa

Knowledge Management has developed over the years into a mainstream organisational necessity to achieve success and organisational effectiveness. Religious organisations are one of the most producers of knowledge. In many parts of Africa, several Pentecostal churches are established regularly, especially in crowded informal settlements. The knowledge conversion model was utilised to assess the knowledge management practices at a Pentecostal church in an informal settlement in Cape Town, South Africa. The objectives of the study were to examine the awareness and uptake of KM in the religious organisations, determine the level of openness of the members and leaders to knowledge sharing and conversion as well as assess how religious organisations leverage knowledge conversion and KM practices in achieving their goals in spreading the gospel and to enhance their community services. The church leadership and adult members of the church constituted the study population. The findings of this study revealed that the level of awareness and uptake of KM was very low because there was no documented training as well as responsible individuals responsible for the management of knowledge. Tacit knowledge is being shared informally through dialogue communication and religious meetings. However, this knowledge is not formally extracted, documented, or converted into explicit knowledge for organisational productivity, further sharing and future reuse. Although some of the leaders were not keen on knowing explicitly managed and shared in the organisation, religious members thought it is very important for knowledge to be documented and shared. Furthermore, as knowledge sharing is not prioritised within the organisation, the goal of spreading the gospel and to enhance their community services was found to be sluggish. The study developed a conceptual model for implementing knowledge management in Pentecostal religious organisations.

An Applied Knowledge Framework to enable knowledge reuse in consulting firms

Knowledge is a core product of management consultancies. Consulting firms consider knowledge management to be a core capability for achieving competitive advantage. ​Knowledge management, especially in project management, faces a lot of challenges that are crucial for the efficiency of work and as a result the success of the projects. According to ISO 30401, there are some knowledge management enablers like human capital, knowledge processes, technology and infrastructure, governance and knowledge sharing culture. Among them are corporate sharing culture, technology and the structure of knowledge which is the most important for companies. The challenges of knowledge management in a consulting business, for example, time saving and efficiency can be solved within the creation of a structured and target-oriented knowledge framework which enables the reuse of knowledge in the whole consulting company. Qualitative methods were used to conceive, design, and to implement the knowledge framework. The approach has involved workshops that included associates, managers, senior consultants, and project directors. This approach enabled the author to record internal requirements and pain points in the current knowledge management base approach, conduct analysis, set priorities, and conceive a new knowledge framework that was able to solve the pain points of consultants and project managers and support them in their daily business. The new framework includes the following components: a flexible structure that adopts itself to defined tagging structure, the storage of knowledge according to the allocation of the content format, and automatically generated statistics to newly upload and download as well as most used files and documents. The results of conducted feedback workshops and interviews showed that the alignment of the new knowledge framework information architecture to the company’s service portfolio and user-friendly design enabled a quick and target-oriented search for the documents and awareness of relevant and important information and knowledge and as a result lead to the increased efficiency of project work in the consulting company.

A knowledge-driven approach for designing data analytics platforms

Big data analytics technologies are rapidly expanding across all industry sectors as organisations try to make analytics an integral part of their everyday decision-making. Although there are many software tools and libraries to assist analysts and software engineers in developing solutions, organisations are looking for flexible analytics platforms that can address their specific objectives and requirements. To minimise costs, such platforms also need to co-exist with existing IT infrastructures and reuse knowledge and resources already accumulated within the organisation. To address such needs, this paper proposes the Data Analytics Solution Engineering (DASE) framework—a knowledge-driven approach supported by semantic web technologies for requirements engineering, design and development of new data analytics platforms. It includes a meta-model that captures data analytics platform requirements via a Knowledge Base, a set of guidelines that organisations can follow in engineering data analytics platforms and a reference architecture that demonstrates how to use these guidelines. We evaluate the DASE framework through two case studies and demonstrate how it can facilitate knowledge-based and requirements-driven data analytics platform engineering. The resulting data analytics platforms are observed to be user friendly, easy to maintain and flexible in handling changes to requirements. This work contributes to the body of knowledge in knowledge-driven requirements engineering, and data analytics platform engineering by providing a meta-model and a reference architecture that can be tailored to different analytics application domains.

ALF-Score++, a novel approach to transfer knowledge and predict network-based walkability scores across cities

Walkability is an important measure with strong ties to our health. However, there are existing gaps in the literature. Our previous work proposed new approaches to address existing limitations. This paper explores new ways of applying transferability using transfer-learning. Road networks, POIs, and road-related characteristics grow/change over time. Moreover, calculating walkability for all locations in all cities is very time-consuming. Transferability enables reuse of already-learned knowledge for continued learning, reduce training time, resource consumption, training labels and improve prediction accuracy. We propose ALF-Score++, that reuses trained models to generate transferable models capable of predicting walkability score for cities not seen in the process. We trained transfer-learned models for St. John’s NL and Montréal QC and used them to predict walkability scores for Kingston ON and Vancouver BC. MAE error of 13.87 units (ranging 0–100) was achieved for transfer-learning using MLP and 4.56 units for direct-training (random forest) on personalized clusters.

A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction

Isolated data silos and domain-specific knowledge pose challenges for knowledge graph construction in the manufacturing industry, where heterogeneous storage leads to distributed databases with complex schemas. In this article, a resource-based industrial knowledge graph is developed using a few-shot classification algorithm to save on labor and other related costs in industrial knowledge graph construction, and an attribute-based fusion strategy for data fusion and alignment is designed. We also propose a novel metrics-based meta-learning model with meta-pretraining (MMM) to address the few-shot text classification problem. Experiment results show that MMM achieves 87.13% accuracy on the 5-shot text classification benchmark Amazon Review Sentiment Classification (ARSC), outperforming other baselines, such as Induction Networks (85.63%) and Distributional Signatures (81.16%). The MMM achieves a 34.6% accuracy improvement compared with Distributional Signatures (84.34% vs. 62.66%) on 1-shot problems of ARSC, hence highlighting the applicability of our model in low-resource conditions. Based on the proposed methods, we further develop an industrial knowledge graph platform with industrial applications, such as value chain analysis and collaboration, to improve knowledge reuse and service innovation.

An Applied Knowledge Framework to Enable Knowledge Reuse in Consulting Firms

Knowledge is a core product of management consultancies. Consulting firms consider knowledge management to be a core capability for achieving competitive advantage. Knowledge management, especially in project management, faces a lot of challenges that are crucial for the efficiency of work and as a result the success of the projects. According to ISO 30401, there are some knowledge management enablers like human capital, knowledge processes, technology and infrastructure, governance and knowledge sharing culture. Among them are corporate sharing culture, technology and the structure of knowledge which is the most important for companies. The challenges of knowledge management in a consulting business, for example, time saving and efficiency can be solved within the creation of a structured and target-oriented knowledge framework which enables the reuse of knowledge in the whole consulting company. Qualitative methods were used to conceive, design, and to implement the knowledge framework. The approach has involved workshops that included associates, managers, senior consultants, and project directors. This approach enabled the author to record internal requirements and pain points in the current knowledge management base approach, conduct analysis, set priorities, and conceive a new knowledge framework that was able to solve the pain points of consultants and project managers and support them in their daily business. The new framework includes the following components: a flexible structure that adopts itself to defined tagging structure, the storage of knowledge according to the allocation of the content format, and automatically generated statistics to newly upload and download as well as most used files and documents. The results of conducted feedback workshops and interviews showed that the alignment of the new knowledge framework information architecture to the company’s service portfolio and user-friendly design enabled a quick and target-oriented search for the documents and awareness of relevant and important information and knowledge and as a result lead to the increased efficiency of project work in the consulting company.

Media Convergence And The Change In Media Content Production And Distribution In Nigeria

The production, distribution, access, and reuse of information, knowledge, and entertainment by users have change as a result of media convergence. This has led to increased involvement, autonomy, and diversity. The growth of user-generated content and people's transition from audience to participants is a significant change brought on by media convergence. Content that was formerly exclusive to particular communication media, such as print and broadcast, particularly newspapers, magazines, radio, television, and cinema, is now accessible to billions of people. This paradigm shift, brought about by technological advancement, has affected how individuals in society think, feel, act, and even how the society operates as a whole. This study thus identifies the key points of transition observed in the contemporary media. Anchored on technological determinism theory, the study argues that breaking news has now become instantaneous as a result of convergence. Although newspapers still go to sleep, their social media handles never do. Similarly, radio and television stations no longer wait for the news schedule to break the news. The challenge posed by media convergence has prompted conventional media to switch to a multimedia approach to content production and distribution. The major conventional media organizations now have social media handles where they break news as well as source news. The study, however, recommends that the same professionalism displayed on conventional media platforms should be extended to their social media posts in the age of media convergence.

Media Convergence and the Change in Media Content Production and Distribution in Nigeria

The production, distribution, access, and reuse of information, knowledge, and entertainment by users have change as a result of media convergence. This has led to increased involvement, autonomy, and diversity. The growth of user-generated content and people's transition from audience to participants is a significant change brought on by media convergence. Content that was formerly exclusive to particular communication media, such as print and broadcast, particularly newspapers, magazines, radio, television, and cinema, is now accessible to billions of people. This paradigm shift, brought about by technological advancement, has affected how individuals in society think, feel, act, and even how the society operates as a whole. This study thus identifies the key points of transition observed in the contemporary media. Anchored on technological determinism theory, the study argues that breaking news has now become instantaneous as a result of convergence. Although newspapers still go to sleep, their social media handles never do. Similarly, radio and television stations no longer wait for the news schedule to break the news. The challenge posed by media convergence has prompted conventional media to switch to a multimedia approach to content production and distribution. The major conventional media organizations now have social media handles where they break news as well as source news. The study, however, recommends that the same professionalism displayed on conventional media platforms should be extended to their social media posts in the age of media convergence.

A method for the ontology-based risk management of PPP construction projects

PurposeWith the increasing complexity of public–private partnership (PPP) projects, the amount of data generated during the construction process is massive. This paper aims to develop a new information management method to cope with the risk problems involved in dealing with such data, based on domain ontologies of the construction industry, to help manage PPP risks, share and reuse risk knowledge.Design/methodology/approachRisk knowledge concepts are acquired and summarized through PPP failure cases and an extensive literature review to establish a domain framework for risk knowledge using ontology technology to help manage PPP risks.FindingsThe results indicate that the risk ontology is capable of capturing key concepts and relationships involved in managing PPP risks and can be used to facilitate knowledge reuse and storage beneficial to risk management.Research limitations/implicationsThe classes in the risk knowledge ontology model constructed in this research do not yet cover all the information in PPP project risks and need to be further extended. Moreover, only the framework and basic methods needed are developed, while the construction of a working ontology model and the relationship between implicit and explicit knowledge is a complicated process that requires repeated modifications and evaluations before it can be implemented.Practical implicationsThe ontology provides a basis for turning PPP risk information into risk knowledge to allow the effective sharing and communication of project risks between different project stakeholders. It can also have the potential to help reduce the dependence on subjectivity by mining, using and storing tacit knowledge in the risk management process.Originality/valueThe apparent suitability of the nine classes of PPP risk knowledge (project model, risk type, risk occurrence stage, risk source, risk consequence, risk likelihood, risk carrier, risk management measures and risk case) is identified, and the proposed construction method and steps for a complete domain ontology for PPP risk management are unique. A combination of criteria- and task-based evaluations is also developed for assessing the PPP risk ontology for the first time.

BIM-based design decisions documentation using design episodes, explanation tags, and constraints

The process of designing a building involves producing design concepts while fulfilling various requirements and regulations. Furthermore, during the project's life-cycle, multiple experts from multiple domains collaborate in developing the different partial models, including architectural, structural, and HVAC among others. Accordingly, clearly communicating the rationale behind design decisions is crucial for developing regulatory compliant designs that also fit the owner’s needs. The developed designs are the main deliverables exchanged and handed over. However, these deliverables do not include any explanation of design intentions or documentation of design decisions. Communication among parties and reuse of knowledge are hindered by the absent explanation of existing design. To overcome this deficiency, this paper proposes a methodology for digitally documenting design decisions, incorporating their intention and rationale. Architectural concepts and evaluation criteria are represented in the form of explanation tags as well as spatial and semantic constraints, which are assigned to the individual model elements and properties. Additionally, to document how design decisions fulfill owner requirements and regulatory documents, natural language processing (NLP) is employed to facilitate querying those documents and then the individual requirements are linked to specific elements, properties, and constraints. To evaluate the proposed methodology, a prototype was implemented as a plugin inside a BIM- Authoring tool and multiple real-world use cases are discussed.

Knowledge reuse in edge computing environments

To cope with the challenge of managing numerous computing devices, humongous data volumes and models in Internet-of-Things environments, Edge Computing (EC) has emerged to serve latency-sensitive and compute-intensive applications. Although EC paradigm significantly eliminates latency for predictive analytics tasks by deploying computation on edge nodes’ vicinity, the large scale of EC infrastructure still has huge inescapable burdens on the required resources. This paper introduces a novel paradigm where edge nodes effectively reuse local completed computations (e.g., trained models) at the network edge, coined as knowledge reuse. Such paradigm releases the burden from individual nodes, where they can save resources by relying on reusing models for various predictive analytics tasks (e.g., regression and classification). We study the feasibility of our paradigm by involving pair-wise (dis)similarity metrics among datasets over nodes based on statistical learning techniques (kernel-based Maximum Mean Discrepancy and eigenspace Cosine Dissimilarity). Our paradigm is enhanced with computationally lightweight monitoring mechanisms, which rely on Holt-Winters to forecast future violations and updates of the reused models. Such mechanisms predict when ‘borrowed’ models are insufficient for being reused, triggering a new process of finding more appropriate models to be reused at the network edge. We provide comprehensive performance evaluation and comparative assessment of our algorithms over different experimental scenarios using real and synthetic datasets. Our findings showcase the ability and robustness of our paradigm to maintain up-to-date reused models at the edge trading off quality of analytics and resource utilization.