Knowledge Storage Research Articles

Development of Knowledge Graph Based on Risk Register to Support Risk Management of Construction Projects

Risk management (RM) in construction projects is a knowledge-intensive process that requires an efficient mechanism for capturing, storing, exploiting, and managing risk-related knowledge. Although some existing studies have proposed various knowledge-based risk management (KBRM) tools. However, not all tools suit all projects, as every project is unique. Besides, each tool is developed based on certain assumptions with methodological drawbacks. Hence, new tools are required in response to the continuous change in the project environment, complexity, and dynamism by adopting new technologies to improve the RM processes. This study proposes a knowledge graph technique to improve risk-related knowledge storage, sharing, and reuse in managing construction project risks. This study constructed a risk knowledge graph (RisKG) based on a project risk register. The proposed RisKG was then used to develop a construction risk dashboard (ConRisk Dashboard) that can be used for quick risk information retrieval to support the RM of construction projects. The effectiveness of the proposed ConRisk Dashboard was verified through its use for the search and retrieval of risk assessment details of a particular risk source associated with the case project used in RisKG. The results indicate that the ConRisk Dashboard can improve the RM processes by saving time spent on project documentation reviews and cost expended on expert workshops at the beginning of every new project. Conclusively, the contribution of this study to the body of knowledge is that it provides a framework for developing knowledge-based risk management systems (KBRMS) and intelligent tools that can facilitate integrated risk management and digital collaboration among project stakeholders to support informed decision-making in construction projects. In addition, this work paves the way for research towards developing and adopting digital tools that can facilitate the digital transformation of RM and CM practices to improve project performance and productivity in the construction industry.

A FUZZY ANALYTICAL HIERARCHY PROCESS FOR EVALUATION OF KNOWLEDGE MANAGEMENT EFFECTIVENESS IN RESEARCH CENTERS

Measuring knowledge management (KM) effectiveness is a very important issue in organizations today. This study aims to develop a method to evaluate the effectiveness of KM under uncertainty in research centers (RCs) in Iran. To develop an evaluation, the relevant literature was reviewed to identify KM effectiveness criteria. Next, the judgments of the experts, specialists, scholars, and professionals in IT and KM systems and senior managers of Iran’s RCs were determined using a pairwise comparison questionnaire. Because linguistic terms are an integral part of human judgments that will influence the results of the research, a fuzzy Analytic Hierarchy Process (AHP) called the Extent Analysis (EA) method was used for data analysis and weight determination. Accordingly, 34 subcriteria extracted from the literature that are important in the evaluation of KM effectiveness were categorized into six main criteria as follows: human resources, leadership and center structure, knowledge creation and acquisition, knowledge storage and security, knowledge sharing, and knowledge utilization and updating. The findings indicate that human resources is the most important criterion based on both the AHP and fuzzy AHP methods. The other five criteria in descending order of importance are knowledge sharing, leadership and center structure, knowledge utilization and updating, knowledge creation and acquisition and knowledge storage and security. Finally, to test the validity and reliability of the proposed framework, we evaluated the effectiveness of KM system in nine of Iran’s RCs.

Focusing on Key Customers and Customer Knowledge Management as Predictors of Customer Satisfaction among Graduating Students of UMTC

This study aimed to determine if focusing on key customers and customer knowledge management were predictors of customer satisfaction among graduating students of UMTC. 300 graduating students of UMTC were the respondents of this research. The statistical tools used were Mean, Pearson-r, and Regression Analysis. Quantitative non-experimental research utilizing correlational technique with regression analysis was the research design of this paper. Results revealed that the level of focusing on key customers among graduating students in terms of needs assessment, customized services, ongoing dialogue and employee empowerment was high. The level of customer knowledge management yielded a descriptive equivalent of high in terms of knowledge creation, knowledge acquisition, knowledge sharing and knowledge storage. For the level of customer satisfaction of graduating students of UMTC, it yielded a high-level result in terms of customer satisfaction, customer loyalty and customer retention. There was a positive significant relationship between focusing on key customers and customer knowledge management to customer satisfaction among graduating students. In addition, both the focusing on key customers and customer knowledge management was a predictor of customer satisfaction among the graduating students of UMTC.

A study on the construction of knowledge graph of Yunjin video resources under productive conservation

Nanjing Yunjin, a highly representative Chinese silk weaving handicraft, was included in the Representative List of Intangible Cultural Heritage of Humanity in 2009. However, due to modern silk weaving technology advancements, aesthetic style evolution, and inadequate public recognition of Yunjin culture, the art faces a decline in market recognition and practitioners, posing a risk to its preservation. Addressing this issue necessitates product innovation, efficient knowledge storage, management, and utilization, and enhancing public cultural identity for Yunjin. Following the government’s “productive conservation” concept for intangible cultural heritage (ICH) projects in the handicraft category, this study uses Yunjin video resources as the primary data source. It constructs a domain knowledge graph (DKG) using an ontological approach to effectively and systematically preserve Yunjin knowledge. Furthermore, the study leverages Neo4j network topology to reveal intricate and diverse relationships within Yunjin knowledge, uncovering rich cultural connotations. Lastly, Cypher is employed for semantic queries, graph visualization, and domain expert evaluation. Evaluation results indicate that the constructed Yunjin DKG meets quality standards, supporting the development of products that align with market aesthetics while preserving Yunjin’s intrinsic cultural values. This approach fosters a complementary relationship between economic benefits and ICH. Additionally, the Yunjin DKG application presents a technical path for knowledge interconnection, integration, and discovery within ICH projects in the handicraft category.

The epistemic vices of corporations

Vice epistemology studies the qualities of individuals and collectives that undermine the creation, sharing, and storing of knowledge. There is no settled understanding of which epistemic vices exist at the collective level. Yet understanding which collective epistemic vices exist is important, both to facilitate research on the antecedents and effects of collective epistemic vice, and to advance philosophical discussions such as whether some collective epistemic vices are genuinely collective. I propose an empirical approach to identifying epistemic vices in corporations, analyzing a large dataset of online employee reviews. The approach has parallels to the methodology for identifying the big-five personality traits. It surfaces epistemic vices that are attributed to corporations by its own members and reduces the number of vices to the minimum required to describe differences between corporations. This approach yields a new taxonomy of epistemic vices for corporations. While two vices identified have close correlates in the existing literature, four others have not been identified at all or only in aspects. Two of these vices are ‘genuinely’ collective in the sense that they can only be attributed to collectives.

INTELLECTUAL TECHNOLOGIES IN THE MANAGEMENT OF OIL FIELDS

The goal of this effort is to create new strategies and enhance the control over oil-producing wells and an oilfield using artificial intelligence techniques. The advancement of knowledge necessitates the implementation of intelligent technologies with the objective of creating a unified scientific and technological oilfield space. Methods for optimizing operation modes for complex technological systems; expert knowledge-based decision-support systems; unified requirements for intelligent system interfaces and modules, as well as means to represent and store knowledge, including distributed knowledge base architectures; and mathematical and software support are among these. Advanced intelligent technologies and tools built on them may be used to modify goal-attainment procedures. The key characteristics of the applicable artificial intelligence tools that are necessary for implementation at all levels are detailed, along with a suggested three-tiered system of intelligent oil field control that assures optimization of oil output and the operation of equipment. The idea and the suggestions provided in the study can serve as a springboard for more investigation and the construction of actual intelligent wells and intelligent oilfields. Keywords: intelligent oilfield, oil, production, artificial intelligence

Teacher Innovation through Knowledge Management and Personality Strengthening

This study aims to increase teacher innovation by examining knowledge management and personality relationships. The method used is a combination of correlation approaches and Sitorem analysis. There were 271 samples from a population of 1684 elementary school teachers with civil servant status using multistage proportional sampling. Data analysis comprised normality, homogeneity, linearity, and multiple regression tests. The study's results prove a solid or significant relationship between knowledge management and personality and teacher innovation, as indicated by the correlation coefficient ry123 = 0.652. SITOREM analysis shows that based on the order of priority, improvements that need to be increased include knowledge dissemination, awareness, openness, personal consideration, and business and organizational innovation. At the same time, the indicators that are maintained and developed are influence, product innovation, emotional stability, process innovation, inspirational motivation, application of knowledge, knowledge storage, agreeableness, service innovation, knowledge acquisition, intellectual stimulation, extraversion, and knowledge evaluating.

Learning by Co-Designing a Board Game to Make Chain Store Knowledge More Robust

The modern student is used to visual information and needs an engaging, stimulating, and fun method of teaching to make learning enjoyable and memorable. Recently, more and more teachers are changing traditional teaching methods and incorporating the concept of learner-centered teaching into their courses. Students must actively identify gaps in self-knowledge, construct clear learning topics, and then integrate relevant information to explain or solve problems. In order to enhance students’ interest in learning and affect their learning effectiveness, the present study introduces students to problem-oriented and game-based learning methods for solving the development problems of chain board games. Students in the third year already possess basic theoretical knowledge and have achieved relevant learning achievements, such as competition awards, industry/academic experience, class cadre experience, community service, etc. Thus, 125 students from two classes participated in this study via quantitative questionnaires. Data analysis with SPSS data revealed significant differences between learning effectiveness and learning methods, social interaction, and subject engagement; students with good learning outcomes were significantly more likely to organize notes and use methods than those with poor learning outcomes.

Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph

To effectively manage the heterogeneous and discrete knowledge of the bridge maintenance domain, this study adopts knowledge graph technology to build a knowledge base of bridge maintenance, called the bridge maintenance knowledge graph (BMKG). The BMKG uses an ontology as the knowledge organization and representation framework and a graph database as the knowledge storage tool. To facilitate the construction of the BMKG, a hybrid method combining a top-down approach and a bottom-up approach is proposed. Firstly, a bridge maintenance domain ontology (BMDO) is coded with Protégé and represented in Web Ontology Language. Secondly, rule reasoning and ontology reasoning are implemented on the BMDO in Protégé in order to automatically complete missing relations or attribute values. Thirdly, ontology reasoning is adopted to perform consistency check on the BMDO. Lastly, the BMDO model is stored in the Neo4j graph database through data format conversion, thus completing the construction of the BMKG. The BMKG is applied in a typical scenario of bridge maintenance to demonstrate its application value. Results show that the proposed hybrid method can create a knowledge graph that can realize the transformation from discrete data into interconnected knowledge. Knowledge graph offers a novel idea to create a knowledge base in the bridge maintenance domain.

Architecture and Data Knowledge of the Regional Data Center for Intelligent Agriculture

The main task of the National Research Program “Smart crop production”, supported by the Ministry of Education and Science of Bulgaria and approved by the Council of Ministers, is the development of a regional data center to facilitate the work of farmers. The regional data center is part of the implementation of a smart crop production environment called ZEMEL which provides personal assistants supporting the work of farmers. The environment provides intelligent services for crop analysis and prevention and assists farmers in performing basic tasks related to crop production. The objective of the proposed article is to present the implementation of the architecture, infrastructure, and data architecture of a regional data center in the Plovdiv region. In order to clearly present the results of this work, which are the architectural and physical implementations of a regional data center and the storage of dynamic data and background knowledge, a methodology consisting of several steps is followed: the system infrastructure of the data center and the data architecture are discussed; one of the local pieces of infrastructure, implemented in the Institute of Plant Genetic Resources (IPGR) in the town of Sadovo in the Plovdiv region, is presented in detail, including the different types of sensors and their connection to the data center in wheat cultivation; the data repositories are discussed where dynamic data and background knowledge are stored. The paper pays special attention to background knowledge developed as ontologies for winter wheat cultivation. The results are summarized by drawing some conclusions and recommendations for the design of the local infrastructure of the center and the stored data to improve its performance.

Leveraging Prior Knowledge to Support Short-term Memory: Exploring the Role of the Ventromedial Prefrontal Cortex.

It is well established that the ventromedial prefrontal cortex (vmPFC) plays a critical role in memory consolidation and the retrieval of remote long-term memories. Recent evidence suggests that the vmPFC also supports rapid neocortical learning and consolidation over shorter timescales, particularly when novel events align with stored knowledge. One mechanism by which the vmPFC has been proposed to support this learning is by integrating congruent information into existing neocortical knowledge during memory encoding. An important outstanding question is whether the vmPFC also plays a critical role in linking congruent information with existing knowledge before storage in long-term memory. The current study investigated this question by testing whether lesions to the vmPFC disrupt the ability to leverage stored knowledge in support of short-term memory. Specifically, we investigated the visuospatial bootstrapping effect, the phenomenon whereby immediate verbal recall of visually presented stimuli is better when stimuli appear in a familiar visuospatial array that is congruent with prior knowledge compared with an unfamiliar visuospatial array. We found that the overall magnitude of the bootstrapping effect did not differ between patients with vmPFC lesions and controls. However, a reliable bootstrapping effect was not present in the patient group alone. Post hoc analysis of individual patient performance revealed that the bootstrapping effect did not differ from controls in nine patients but was reduced in two patients. Although mixed, these results suggest that vmPFC lesions do not uniformly disrupt the ability to leverage stored knowledge in support of short-term memory.

Investigating the relationship between knowledge management practices and organizational learning practices in the universities’ environment

<span lang="EN-US">The concept of knowledge management (KM) and organizational learning (OL) has been embraced by organizations to complement each other. Higher education institutions have embraced KM and OL as a means to improve organizational efficiency. This research explores the link between KM and OL. The target population included all the 432 academicians and administrators from 35 public universities in Iraq. The sampling was selected using a stratified random sampling technique. The correlation among the components of KM and OL was tested as well as the effect of KM components on OL. The findings were derived using smart partial least square. The findings showed that there is significant correlation between components of KM and components of OL. The regression analysis showed also that the effect of KM and its components; knowledge creation, knowledge sharing, knowledge storage, knowledge application and knowledge acquisition on OL are significant. These findings provide insights to universities management on strategies to implement KM practices that can align with OL practices to assure dynamic lifelong mechanisms for the basic daily activities such as teaching, learning, researching, and supervision.</span>

Context-driven ontology-based risk identification for onshore wind farm projects: A domain-specific approach

Risk identification is a knowledge-based process that requires the time-consuming and laborious identification of project-specific risk factors. Current practices for risk identification in construction rely heavily on an expert’s subjective knowledge of the current project and of similar historical projects to determine if a risk may affect the project under study. When quantitative risk-related data are available, they are often stored across multiple sources and in different types of documents complicating data sharing and reuse. The present study introduces an ontology-based approach for construction risk identification that maps and automates the representation of project context and risk information, thereby enhancing the storage, sharing, and reuse of knowledge for the purpose of risk identification. The study also presents a novel wind farm construction project risk ontology that has been validated by a group of industry experts. The resulting ontology-based risk identification approach is able to accommodate project context in the risk identification process and, through implementation of the proposed approach, has identified risk factors that affect the construction of onshore wind farm projects.

The Fragmented Mind

<i>The Fragmented Mind</i>

Healthy Food Recommender System for Obesity Using Ontology and Semantic Web Rule Language

Today's lifestyle and eating patterns tend to be irregular due to busyness. People prefer eating foods that are fast and easy to obtain, but often lack knowledge of the nutritional content in them. These eating patterns lead to unbalanced nutrition and can cause various health problems and diseases, such as overweight and obesity. Due to a lack of information, people often turn to drugs instead of learning about healthy diets, making it difficult for them to determine what menu to choose or what type of food to consume. While there have been many studies to recommend healthy food based on user preferences, there is currently no recommender system that includes serving size and budget for each daily food recommendation that is implemented in a chatbot framework. This study proposes using ontology and the Semantic Web Rule Language (SWRL) to store knowledge in the ontology and then process it using SWRL to produce food recommendations based on user preferences. From a sample of user data which obtained 170 recommended meal menus. System performance is pretty good. Based on the validation results from nutritionists, the precision value was 0.852941, the recall was 1, and F-score of 0.920634 So that a healthy food recommendation system can be used to help the user follows a diet that meets his nutritional needs and is within his budget needed

Extraction and analysis of risk factors from Chinese chemical accident reports

Accidents in chemical production usually result in fatal injury, economic loss and negative social impact. Chemical accident reports which record past accident information, contain a large amount of expert knowledge. However, manually finding out the key factors causing accidents needs reading and analyzing of numerous accident reports, which is time-consuming and labor intensive. Herein, in this paper, a semi-automatic method based on natural language process (NLP) technology is developed to construct a knowledge graph of chemical accidents. Firstly, we build a named entity recognition (NER) model using SoftLexicon (simplify the usage of lexicon) + BERT-Transformer-CRF (conditional random field) to automatically extract the accident information and risk factors. The risk factors leading to accident in chemical accident reports are divided into five categories: human, machine, material, management, and environment. Through analysis of the extraction results of different chemical industries and different accident types, corresponding accident prevention suggestions are given. Secondly, based on the definition of classes and hierarchies of information in chemical accident reports, the seven-step method developed at Stanford University is used to construct the ontology-based chemical accident knowledge description model. Finally, the ontology knowledge description model is imported into the graph database Neo4j, and the knowledge graph is constructed to realize the structured storage of chemical accident knowledge. In the case of information extraction from 290 Chinese chemical accident reports, SoftLexicon + BERT-Transformer-CRF shows the best extraction performance among nine experimental models. Demonstrating that the method developed in the current work can be a promising tool in obtaining the factors causing accidents, which contributes to intelligent accident analysis and auxiliary accident prevention.

Virtual nature makes knowledge beautiful

This is a perspective that presents a viewpoint on immersive informal learning applications built as digital twins of the natural world. Such applications provide multimodal, interactive, immersive, embodied, and sensory experiences unique and different from typical game art environments because they are geospatial visualizations of data and information derived from ecological field plot studies, geographical information systems, drone images, and botanically correct 3D plant models visualized in real-time interactive game engines. Since they are constructed from geometric objects, they can programmatically self-express semantic data and connect to knowledge stores on the Internet to create a web of knowledge for both exploration of a virtual environment as a natural landscape and for exploration of connected knowledge stores for informal learning at the moment of curiosity. This design is exceptionally powerful for informal learning as it supports the innate human desire to understand the world. This paper summarizes the construction methods used for creating three digital twins of natural environments and the informal learning applications created and distributed, namely, augmented reality (AR), virtual reality (VR), and virtual field trips. Informal learning outcomes and emotional reactions are evaluated using mixed-methods research studies to understand the impact of design factors. Visual fidelity and navigational freedom are combined to increase learning outcomes and many effective and emotional outcomes as well. Access to facts and story increase learning outcomes, and applications evaluated as beautiful are correlated with emotional reactions of awe and wonder, and awe and wonder are correlated with higher learning gains. Beauty is correlated with other system-wide subjective evaluations largely accepted as important to create a context conducive to support learning outcomes, such as calmness, excitement, and curiosity, a desire to share, and a desire to create. This paper summarizes the highlights of the author’s prior work to give the reader a perspective on the body of work.

Examining Industry 4.0 through the lens of human resource and knowledge management: Implications for SMEs

Abstract The change caused by Industry 4.0 (I4.0) has fundamentally changed our lives and operations of organisations. The objective of the article is to reveal whether I4.0 implementation supports human workforces, knowledge sharing and knowledge storage in case of Hungarian small and medium-sized enterprises (SMEs). The data were obtained from 122 completed questionnaires out of 152 requests. The analysis is based on quantitative methodology, relationship analyses (Cramer’s V) were used. The study was conducted in 2021 with the participation of Hungarian SMEs’ leaders. Based on the results it can be concluded that the implementation of I4.0 technologies supports human workforces in SMEs. In addition, our study reveals that collaborative technologies and social media were implemented to support knowledge sharing in SMEs. At the same time, knowledge storage is not the primary purpose of implementing I4.0 technologies in SMEs. From a theoretical perspective, it contributes to enriching the empirical evidence on implementing I4.0 technologies and their impact on supporting human resources. From a practical point of view, the research identifies those I4.0 technologies that support SMEs in knowledge sharing. Collaborative technologies and social media can be implemented in order to advance the fields of Collaboration, Communication and Connecting. The originality of our research is given by the fact that, to the best of our knowledge, this is the first study that examines the aspects of I4.0 technologies in terms of human resource and knowledge sharing and storage in Hungarian SMEs.

Co-Occurring Autism Spectrum and Borderline Personality Disorder: An Emerging Clinical Challenge Seeking Informed Interventions.

Co-Occurring Autism Spectrum and Borderline Personality Disorder: An Emerging Clinical Challenge Seeking Informed Interventions.

Classification of Confidential Images Using Neural Hash

Humanity generates considerable information using its devices – smartphones, laptops, and tablets. Users upload images to different platforms, such as social networks, messengers, web services and other applications, which greatly endanger their personal information. User privacy has been exploited on the Internet for a long time. Interested parties lure potential customers into a trap of offers and services using such information as age, weight, nationality, religion and preferences. The sensitive information that may be contained in personal images is sometimes not recognized by their users as dangerous to share and, therefore, can easily be shared online by the owner without a second thought.This article inspects a neural hash algorithm for solving image classification tasks of confidential information and evaluates it via basic metrics. The main idea of the algorithm is to find similar images that will serve as an example for defining classes. The algorithm uses hash codes, ensuring users’ privacy. The evaluation of the algorithm is based on “The Visual Privacy (VISPR) Dataset”. The main components of the algorithm are a neural network that generates vectors of extracted features for images and an indexed set of images (hash tables) that store knowledge about a particular domain.The critical aspect of the algorithm involves collisions of hash codes for similar images due to the similarity of their vectors of extracted features. The resulting hash codes can be identical or differ by a specific value of Hamming distance. Multiple hash tables with different hash functions are used to increase the recall or precision of the results. The effect of imperfect taxonomy was analyzed, which led to further filtration of abstract classes and increasing overall scores.Also, the article investigates the “pseudo-adaptivity” of the algorithm - the ability to classify new classes and add new cases to existing classes that were not included in the training stages. Such ability may be crucial for domains with many image instances or classes.