Dynamic Knowledge Research Articles

How does product innovation affect the performance of university-industry collaboration A dynamic knowledge transfer perspective

How does product innovation affect the performance of university-industry collaboration A dynamic knowledge transfer perspective

Investigating the Challenges and Prospects of Construction Models for Dynamic Knowledge Graphs

Investigating the Challenges and Prospects of Construction Models for Dynamic Knowledge Graphs

Semantic agent framework for automated flood assessment using dynamic knowledge graphs

Abstract This article proposes a framework of linked software agents that continuously interact with an underlying knowledge graph to automatically assess the impacts of potential flooding events. It builds on the idea of connected digital twins based on the World Avatar dynamic knowledge graph to create a semantically rich asset of data, knowledge, and computational capabilities accessible to humans, applications, and artificial intelligence. We develop three new ontologies to describe and link environmental measurements and their respective reporting stations, flood events, and their potential impact on population and built infrastructure as well as the built environment of a city itself. These coupled ontologies are deployed to dynamically instantiate near real-time data from multiple fragmented sources into the World Avatar. Sequences of autonomous agents connected via the derived information framework automatically assess consequences of newly instantiated data, such as newly raised flood warnings, and cascade respective updates through the graph to ensure up-to-date insights into the number of people and building stock value at risk. Although we showcase the strength of this technology in the context of flooding, our findings suggest that this system-of-systems approach is a promising solution to build holistic digital twins for various other contexts and use cases to support truly interoperable and smart cities.

Unveiling Causality in Stateful Enterprise Knowledge Graphs: An Exploration of Process-Driven Object Relationships

The exponential growth of enterprise data generation demands innovative approaches for efficient information management across highly interconnected domains. Particularly, the process of systems engineering involves inhomogeneous data sets requiring a high degree of flexibility and traceability throughout its lifecycle. While structured data resides comfortably in dedicated systems, managing unstructured information within annotations, documents and emails often comes with challenging holistic comprehension of linking, context and revisioning. Graph technology holds promise in establishing adaptable connections among decentralized data sets. However, leveraging graph databases comes with challenges. Schema standardization, crucial for framing complex processes, is a difficult undertaking due to evolving data interrelationships and changing requirements. This paper proposes a two-fold solution. First, we introduce a conceptual schema that decouples stateful object descriptions from its headers and furthermore documents transactions. This allows flexible object associations, conditional state versioning and improved traceability while maintaining manageability. Second, we leverage the Business Process Model and Notation (BPMN) for process design, service orchestration and automated graph manipulations. Through application to the publicly available Northwind data set using neo4j and Flowable BPM, we illustrate our methodology’s efficacy and the seamless integration of type-safer data science into dynamic knowledge graphs, contributing to the future of enterprise data management.

How does product innovation affect the performance of university-industry collaboration A dynamic knowledge transfer perspective

How does product innovation affect the performance of university-industry collaboration A dynamic knowledge transfer perspective

Dynamic decision-making for knowledge-enabled distributed resource configuration in cloud manufacturing considering stochastic order arrival

The emergence of COVID-19 caused the stagnation of production activities and promoted the changing market demand. These uncertainties not only brought great challenges to the manufacturing approaches led by a single enterprise, but also threatened the stability of inherent supply chain. To maintain market competitiveness, an efficient distributed manufacturing resource allocation method is urgently needed by manufacturers. Cloud manufacturing (CMfg) is an advanced service-oriented manufacturing paradigm that breaks physical space constraints to integrate distributed resources across enterprises, and provides on-demand configuration of manufacturing services for personalized consumer needs in real-time. The focus of this paper is to achieve dynamic configuration of distributed resources in CMfg considering stochastic order arrival, while reducing overall completion time and improving resource utilization. First, a dynamic knowledge graph for distributed resources is constructed, and its definition and construction methods are introduced. Secondly, semantic matching between massive optional manufacturing resources and multiple types of subtasks is achieved through knowledge extraction, thereby obtaining a candidate set of manufacturing resources that meet basic requirements for each subtask. An artificial intelligence (AI) scheduler based on deep reinforcement learning is developed, and order urgency is incorporated into the design of state observation vectors. AI scheduler can generate optimal decision results based on environmental observations, select high-quality manufacturing service compositions over candidate sets, and ultimately achieve efficient distributed resources configuration. Finally, Dueling DQN-based training method is put forward to optimize AI scheduler, enabling adaptable decision-making performance in dynamic environment. In the experiment, a simulation environment with 18 different settings is designed that considers stochastic processing time, random order compositions and various order arrival patterns. The proposed graph-based matching method, scheduling policy learning method and dynamic decision-making method are tested in the simulation environment. The experiment results demonstrate that the cognitive and AI joint driven distributed manufacturing resource configuration method is superior to traditional methods in terms of policy learning speed and scheduling solution quality.

Artificial intelligence at the pen's edge: Exploring the ethical quagmires in using artificial intelligence models like ChatGPT for assisted writing in biomedical research.

Chat generative pretrained transformer (ChatGPT) is a conversational language model powered by artificial intelligence (AI). It is a sophisticated language model that employs deep learning methods to generate human-like text outputs to inputs in the natural language. This narrative review aims to shed light on ethical concerns about using AI models like ChatGPT in writing assistance in the health care and medical domains. Currently, all the AI models like ChatGPT are in the infancy stage; there is a risk of inaccuracy of the generated content, lack of contextual understanding, dynamic knowledge gaps, limited discernment, lack of responsibility and accountability, issues of privacy, data security, transparency, and bias, lack of nuance, and originality. Other issues such as authorship, unintentional plagiarism, falsified and fabricated content, and the threat of being red-flagged as AI-generated content highlight the need for regulatory compliance, transparency, and disclosure. If the legitimate issues are proactively considered and addressed, the potential applications of AI models as writing assistance could be rewarding.

Cross-domain flood risk assessment for smart cities using dynamic knowledge graphs

This paper investigates the usage of knowledge graphs to bridge the gap between current data silos in deriving a holistic perspective on the impact of flooding. It builds on the idea of connected digital twins based on the World Avatar dynamic knowledge graph to deploy an ecosystem of autonomous software agents to continuously ingest new real-world information and operate on it. Multiple publicly available yet isolated data sources, including geospatial building information and property sales data as well as real-time river levels, weather observations, and flood warnings, are connected to instantiate a semantically rich ecosystem of knowledge, data, and computational capabilities to provide cross-domain insights in projected flooding events and their potential impact on population and built infrastructure. The extensibility of the proposed approach is highlighted by further integrating power, water, and telecoms infrastructure as part of the very same system, in order to analyse flood-induced asset failures and their propagation across networks. The World Avatar promotes evidence-based decision making during several disaster management phases, supporting both tactical and strategic risk assessments, which supports the United Nations Sustainable Development Goal 11 to improve the assessment of vulnerability, exposure, and risk of communities imposed by flooding events.

LOCAL ECOLOGICAL KNOWLEDGE AND IT’S BENEFIT TO CONSERVATION PROGRAMS IN INDONESIA

Conservation efforts in Indonesia in the last decade have been using many strategies to integrate Local Ecological Knowledge (LEK). Recognition of the role of local communities and indigenous peoples is part of efforts to mitigate climate change and the current biodiversity crisis. The mapping of the contribution of local ecological knowledge in forest management approaches for biodiversity conservation is very important. This study aims to describes the contribution of local ecological knowledge (LEK) adaptation in conservation programs in Indonesia. This research method uses a qualitative approach with an ethnoecological strategy. The research locations were in three local communities in Indonesia, namely: the Nusa Lembongan community in Bali, the Tobelo community in Halmahera, Maluku, and the Urang Kanekes/Bedouin community in Lebak, West Java. Local ecological knowledge contributes to resilience in the face of climate change, and how indigenous peoples ensure harmony between social capital, the forest environment, and forest use can be of particular concern to face the challenge of achieving zero hunger. This is related to forest ecosystems managed by indigenous peoples showing better results in sustainability based on environmental and social sustainability principles. This study shows that forest management processes based on local ecological knowledge (LEK) can be an alternative approach to biodiversity conservation. Local ecological knowledge shows an emphasis on the ongoing process of experience-based knowledge in communities that utilize the environment. The contribution of studies on LEK adaptation as dynamic knowledge can be used as a more flexible conservation approach and evaluation of forest conservation programs in Indonesia in relating to ecosystem adaptation. The finding of this study was important to LEK integration into the conservation program so it will become more adaptive and flexible to the changes. Understanding the dynamic characterization of LEK has benefit to conservation program that is based on community and participatory.

Expert System For Diagnosing Alzheimer's Using Certainty Factor Method

This expert system for diagnosing Alzheimer's disease is a computer-based stem system used as a tool to diagnose Alzheimer's disease based on a dynamic knowledge base. This knowledge base contains knowledge obtained from various sources including from the experience of experts who are proficient in their fields and also books related to diagnosis of Alzheimer's disease and then collected into a database system needed for making conclusions. In this system, the certainty factor method will be used using a forward chaining inference engine. The purpose of making this root stem p can be useful for many people and can know clearly about Alzheimer's disease from its symptoms and solutions, which will be displayed in the form of a website using PHP programming with MySQL database.

Bridging Knowledge Management and Capabilities in Innovative Projects: An Integrative Framework

The application of dynamic capabilities and absorptive capacity in innovative projects can use the knowledge processed by knowledge management as a strategic resource. Thus, this article aims to investigate how the cross-fertilization among dynamic capabilities, absorptive capacity, and knowledge management can benefit the development of innovative projects. The research approach is a literature review. We identified core codes and their relationships. We propose an integrative framework, detailing three propositions. It also presents unsolved questions and new trends for future research agendas. The results can provide valuable contributions for project managers, pointing out how project capabilities can enhance innovative projects.

Predictive and mediation model for decision-making in the context of dynamic capabilities and knowledge management

PurposeDrawing on resource and capability theory, this study aimed to analyze the relationship between the dynamic capabilities (DC), the knowledge management (KM) process (KMP) and results in customers and people. More specifically, the study argues that the KM process mediates the relationship between DC and the results outlined above. In addition, a predictive analysis is carried out that demonstrates the relevance of the KM process in the model.Design/methodology/approachThe study sample is made up of 118 Spanish organizations that have some kind of recognition of excellence awarded by the European Foundation for Quality Management (EFQM). Partial least squares methodology is used to validate the research model, the hypothesis testing and the predictive analysis.FindingsThe results show that organizations which leverage the DC through the KMP improve customer and people outcomes. Moreover, the predictive power is higher when the KMPmediates the relationship between the DC and the results.Originality/valueThere is no consensus in the literature on the relationship between DC, KM and performance. Moreover, there are also not enough papers that study KM or DC through the dimensions that define these constructs or variables. Given this need, this work considers the KMP according to the stages of knowledge creation, storage, transfer and application. Similarly, DC is dimensioned in sensing, learning, integrating and coordinating capabilities. These, as reconfigurators of knowledge assets, influence the KMP. Accordingly, the empirical model connects these knowledge domains and analyses their link to outcomes.

Enabling Context-based AI in Chatbots for conveying Personalized Interdisciplinary Knowledge to Users

Adaptable chatbots have revolutionized user interactions by dynamically tailoring responses to users' knowledge and explainability preferences in interdisciplinary domains, such as AI in education and medicine. While strides have been made in model explainability, little attention has been paid to making models contextually aware and responsive to users' background knowledge. This study investigated interdisciplinary knowledge learning principles in a different domain, where the chatbot's contextual understanding is enhanced through dynamic knowledge graphs that capture users' past interactions to deliver up-to-date and relevant responses. By incorporating explainability features, chatbot responses become enriched, enabling users to understand the reasoning behind answers. This study proposed a model that showed superior chatbot performance and accurately addressed most queries, outperforming competitor chatbots DBpedia, ODC, and ODA. A 0.7 F-measure showcased its excellence, attributed to dynamic knowledge graphs and explainability checks. This study envisions a new era of conversational AI that not only meets user needs but also fosters a deeper understanding of AI decision-making, making it indispensable in delivering personalized, informed, and contextually aware interactions.

Blockchain-based ontology driven reference framework for security risk management

Security risk management (SRM) is crucial for protecting valuable assets from malicious harm. While blockchain technology has been proposed to mitigate security threats in traditional applications, it is not a perfect solution, and its security threats must be managed. This paper addresses the research problem of having no unified and formal knowledge models to support the SRM of traditional applications using blockchain and the SRM of blockchain-based applications. In accordance with this, we present a blockchain-based reference model (BbRM) and an ontology driven reference framework (OntReF) for the SRM of traditional and blockchain-based applications. The BbRM consolidates security threats of traditional and blockchain-based applications, structured following the SRM domain model and offers guidance for creating the OntReF using the domain model. OntReF is grounded on unified foundational ontology (UFO) and provides semantic interoperability and supporting the dynamic knowledge representation and instantiation of information security knowledge for the SRM. Our evaluation approaches demonstrate that OntReF is practical to use.

Establishing a dynamic and static knowledge model of the manufacturing cell management system: An active push approach

The workshop operation management (WOM) system contains a large amount of multi-source heterogeneous data, but these data often lack effective correlation, posing challenges in extracting valuable information for achieving unified system modelling at the knowledge level. Due to the lack of knowledge active support in the actual WOM system, managers could only passively rely on their own past experiences. To bridge this gap, the knowledge active push method for optimal operation management of the manufacturing cell (OOMMC) is studied based on knowledge graph (KG). Firstly, a dynamic knowledge model is established according to the knowledge application scenario of manufacturing cell, in which the Local Tangent Space Alignment (LTSA) + k-means algorithm is used for active abnormal warning. Secondly, a static knowledge model based on user needs using feature vocabulary and naive Bayesian classifier is established, where managers can obtain knowledge from the KG based on professional noun keywords or simple query questions. These two models complement and promote each other: the dynamic knowledge model using a service-based collaborative filtering method to push the nearest knowledges in the KG to users, effectively inspiring them to retrieve more relevant knowledge. While the static knowledge model evaluates the effectiveness of previously pushed knowledge by setting the knowledge demand index and providing feedback to the system to ensure accuracy of knowledge push. Finally, an active push simulation system is designed and developed to verify the feasibility and effectiveness of the proposed architecture and key enabling technologies.

Mapping of Soil Dynamic Response of Palu City Based on Horizontal to Vertical Spectral Ratio (HVSR) Method

Palu is one of the earthquake prone coastal cities in Indonesia. The 28 September 2018 earthquake (Mw 7.5) destroyed buildings in Palu due to ground shaking amplification, liquefaction, and tsunami. This phenomenon implies the importance of soil dynamic properties knowledge as a basis for proper building design to reduce such an earthquake. This paper presents the results of microtremor measurements conducted at 110 locations in Palu to estimate the dynamic properties of alluvial and marine deposits for earthquake mitigation. The microtremor data were processed using the HVSR method to estimate dominant frequency and amplification factors. Based on these two parameters, the seismic vulnerability index value can be used to estimate the dynamic characteristic variations of the area. The results show that alluvial and marine sediments in Palu have dominant frequency values from 0.54 to 11.72 Hz, amplification from 1.64 to 6.15, and seismic vulnerability index of 1.49 to 56. Sediments with low dominant frequency and high amplification factors are mostly found in the western and eastern Palu and Mantikulore districts, which are associated with thick sediment layers. The areas with the highest amplification factor and seismic vulnerability index are Petobo and Balaroa, which experienced severe damage during the 2018 earthquake.

Re-conceptualizing absorptive capacity: The importance of teams as a meso-level context

Absorptive capacity has received considerable scholarly attention due to its ability to explain heterogeneous degrees of learning from complex and dynamic knowledge environments. Extant absorptive capacity frameworks and models unmistakably take firms as the focal unit and level of analysis. Mounting empirical evidence, in contrast, shows the growing importance of teams in acquiring and utilizing external knowledge. Teams are not just scale models of firms; they have unique attributes and function as an active context of organizational learning. However, previous research does not consider such idiosyncrasies when conceptualizing absorptive capacity. We focus on absorptive capacity within the meso-level context of teams and problematize several entrenched assumptions behind existing models. We then propose a re-conceptualization of absorptive capacity with four new dimensions that collectively address these assumptions and pay systematic attention to the distinctive characteristics of teams as active learning contexts.

Making knowledge matter: Understanding and improving knowledge-integration in Dutch marine spatial planning policy

Lacking knowledge concerning the North-Sea's socio-ecological system causes the rapid increase of Offshore Wind Farm (OWF) development to occur under serious uncertainty. Optimising OWF development while minimizing negative effects on the socio-ecological system of the North-Sea thus requires holistic knowledge and its appropriate use in policy. Together with the pressure for OWF development, the subsequently rapid development of an array of scientific knowledge results in a dynamic and multidisciplinary knowledge situation, challenging the integration of knowledge, particularly between research and policy. To ensure policy is based on the latest holistic understanding, knowledge requires integration in strategic Marine Spatial Planning (MSP) policy. While the need for better knowledge processes is widely mentioned as a challenge for MSP, knowledge-integration in MSP remains understudied. This paper responds by studying the role of OWF-related knowledge in MSP while formulating a framework of conditions for knowledge-integration between research and policy in MSP policy and OWF development. These conditions are formulated based on knowledge management and MSP literature and subsequently applied in semi-structured interviews, document research, consortium observations, and focus groups. The case study used for acquiring data is knowledge-integration in Dutch MSP and OWF development. The Dutch maritime area is intensively used and faces important tensions over space due to conflicting stakeholder interests. In addition, while being an early adopter of MSP, the Netherlands also faces serious challenges due to high political pressure promoting Offshore Wind Farm development and a related need for maritime knowledge production. Together with the array of knowledge programs aiming to inform Dutch MSP, this provides a useful case to analyse knowledge-integration in MSP.The framework developed in this paper includes conditions for knowledge-integration ranging from the acceptance of differences between researchers and policymakers, to the need to employ interface managers who can develop a structural and effective knowledge-integration process. Fundamental differences between researchers and policymakers, such as differing timeframes, and differing views on uncertainty were found to form significant risks for knowledge-integration, as such influencing conditions. Additionally, the dynamic, long-term, area specific, and multidisciplinary context of MSP and OWF development was found to significantly influence the knowledge-integration process, underlining the importance of context for knowledge processes.

A knowledge-augmented heterogeneous graph convolutional network for aspect-level multimodal sentiment analysis

Aspect-level multimodal sentiment analysis has also become a new challenge in the field of sentiment analysis. Although there has been significant progress in the task based on image–text data, existing works do not fully deal with the implicit sentiment expression in data. In addition, they do not fully exploit the important information from external knowledge and image tags. To address these problems, we propose a knowledge-augmented heterogeneous graph convolutional network (KAHGCN). First, we propose a dynamic knowledge selection algorithm to select the most relevant external knowledge, thereby enhancing KAHGCN’s ability of understanding the implicit sentiment expression in review texts. Second, we propose a graph construction strategy to construct a heterogeneous graph that aggregates review text, image tags and external knowledge. Third, we propose a multilayer heterogeneous graph convolutional network to strengthen the interaction between information from external knowledge, review texts and image tags. Experimental results on two datasets demonstrate the effectiveness of the KAHGCN.

GLOBAL COLLABORATIONS IN BREEDING CROPS FOR CLIMATE RESILIENCE

This paper explores the critical role of international cooperation in addressing the challenges imposed by a changing climate on global agriculture. As climate change threatens food security, this paper investigates the power of unity in transcending geographical and disciplinary boundaries to foster collaborative initiatives. The urgency of developing crops resilient to climate fluctuations is underscored, emphasizing the diverse challenges different regions face and the need for adaptable solutions. Global collaborations catalyze progress, creating a dynamic shared knowledge and resources ecosystem. The abstract outlines the significance of breaking down barriers, be they geographical or cultural, to facilitate a comprehensive approach to breeding crops capable of withstanding a spectrum of climatic conditions. Knowledge exchange and capacity building emerge as key components of successful global collaborations, ensuring communities worldwide have the skills and resources needed for local implementation. The abstract highlights the transformative potential of united efforts, providing a glimpse into a future where international cooperation stands as a cornerstone in pursuing resilient global agriculture amidst the challenges posed by climate change.