Hierarchical Relationships Research Articles

QLTI-07. COLLABORATIVE RESEARCH SUITE FOR GENOMIC ANALYSIS, DATA REPORTING AND WORKFLOW CUSTOMISATION

Abstract The genomic analysis pipeline involves multiple stages, including data extraction, transformation, and loading (ETL), exploratory data analysis (EDA), machine learning (ML) modeling, and reporting. The ETL stage involves data profiling and quality control measures to ensure data integrity and consistency. The EDA stage employs statistical and visualization methods to understand data distribution, identify patterns, and detect anomalies. Machine learning modelling involves supervised classification techniques, such as logistic regression and support vector machines (SVMs), to predict tumour classification and develop gene signatures. Unsupervised classification techniques, such as K-means clustering and Hierarchical clustering, identify patterns and relationships in genomic data. Quality metrics evaluate data completeness, accuracy, and consistency, ensuring data reliability and validity. The integration of data science techniques into a comprehensive framework can be achieved with the development of Application Dashboards that provide real-time insights into genomic data across every stage of the analysis pipeline. This enables clinical researchers and bioinformaticians to make informed decisions quickly, improving turn-around time and accelerating the discovery of new insights. In contrast to conventional methods of using stand-alone scripts, these dashboards offer a reusable and scalable solution, allowing for easy modification and adaptation to new datasets and analysis pipelines, thereby reducing development effort and increasing productivity. The dashboards constitute a centralized platform for data visualization, exploration, and analysis, facilitating collaboration and communication among research team members. With customizable and interactive visualizations, users can explore genomic data from multiple dimensions and the integration of multiple sources, including genomic and transcriptomic data. The technologies used to power this Research suite include Streamlit for Web App development, Python for data analysis, Opensearch for data storage and other utility libraries for reporting.

Pragmatic particles as distinguishing features of registers

Abstract The study explores the potential of utilizing particle use data to differentiate between seven Estonian registers: everyday conversation, institutional interaction, printed media (newspapers), prose fiction, academic prose, instant messaging, and internet comments. The objective is to develop a simple yet effective model that enables researchers to comprehend the internal logic behind register differentiation based on particle use. Particles are considered promising differentiators due to their independence from text content. The article outlines the chosen method, the model creation, and the testing process. A key finding reveals that hierarchical relationships between particles within registers prove more reliable indicators than general use frequencies. The method involves establishing correspondences between particle pairs and register pairs, facilitating the measurement of distances between registers. During testing, the model demonstrates high accuracy across registers, encountering some difficulties in categorizing fiction and institutional interaction. Overall, the study confirms the efficacy of the proposed method in distinguishing registers based on particle use, underscoring the significance of particles in linguistic analysis.

Equal Despite Different Entitlements? M.C. Piepers’ Ambiguous Conceptualization of Legal Equality (1898)

In ‘Gelijkstelling van Vreemde Oosterlingen met Europeanen’ (‘Legal Equation of Foreign Asians with Europeans’), published in 1898 in Tijdschrift voor Nederlandsch-Indië, the liberal intellectual M.C. Piepers argued against the legal equation of the resident Japanese subjects in the Netherlands Indies with the Europeans. To make such equation happen, Parliament in the Netherlands discussed a change in the Law in order to alter the provisions with respect to the binary colonial distinction between Europeans and all other people in the Netherlands Indies as laid down in article 109 of the ‘colonial constitution’ (‘regeringsreglement’) of 1854. The change was needed for the 1896/1897 trade-agreement between the Netherlandic Empire and Imperial Japan to be implemented. Piepers warned against this ‘surrender’ to Japanese claims, arguing that the change emerged from a wide-spread misunderstanding of the legal and constitutional regulations with respect to legal entitlements in the Netherlands Indies. This article interprets Piepers’ passionate defense of the judicial status quo concerning equality founded on ‘legal’ definitions of difference, as an avant la lettre constitutional ‘apartheid’ argument. Piepers’ view on Netherlands Indies state formation started from a legal pluralism with subjectively defined groups. We will argue how in these subjectivities resonate three levels of power relations around 1900: a) geopolitical; b) national/state/colonial c) individual; with long-living aftermaths. Piepers final line of defending the status quo is the distinction between the just de jure equality in colonial society and the unjustified de facto hierarchical relationships between the different population groups. This very opposition between just laws and unfair practices in colonial governance obscures how the Eurocentrism of European (liberal) legal thought inevitably resulted in racist practice

Exploring the Relationship Needs of Service Users During Crisis Interventions: A Qualitative Study

AbstractPeople in crisis sometimes seek professional support, and the relationship between service users and professionals is crucial in overcoming the crisis. To understand the relationship needs of people in crisis, 29 semi-structured interviews with service users were conducted and analyzed using a grounded-theory approach. The findings reveal that people in crisis seek a professional who is there for them, recognizes the crisis as an emergency and a solvable situation, treats them with respect, and offers individual support. Furthermore, there needs to be a general fit between professionals, the support services, and service users for a supportive relationship to be established. However, two main aspects were discovered where service users differ depending on the resources they can access during the crisis: While people with access to many resources seek a rather distant and egalitarian relationship, those with access to fewer resources prefer more intimate and hierarchical relationships with professionals.

Research on construction method of command and control network model based on complex network theory

AbstractThe command and control (C2) network is a complete organizational system that connects operational units at all levels based on command relationships. Its purpose is to ensure that the C2 system can fully perform its command functions, achieving high precision and efficiency in decision‐making. As warfare models evolve rapidly from network‐centric warfare to multi‐domain operations, traditional C2 networks, which utilize a tree structure for connectivity, exhibit only a single hierarchical relationship, making it challenging for different operational units at the same level to interconnect. Furthermore, with the diversification of warfare, the three types of nodes in traditional C2 network models are insufficient to encompass all operational units. In response, this paper proposes a method for edge weighting in C2 networks based on a combination of node attributes and network attributes described by complex network theory. The node attributes mainly include node information transmission capacity, task coordination ability between nodes, node distance, and response time. The network attributes are primarily represented by hierarchy and betweenness centrality. Additionally, the traditional C2 network model's three types of nodes are expanded to five types of nodes. Based on the edge weighting method, internal command edges, inter‐network collaborative edges, and cross‐level command edges are generated within the C2 network. Simulation results demonstrate that the constructed C2 network model's characteristic parameters are superior to those of traditional C2 networks and collaborative C2 networks. This improvement enhances command and coordination abilities, aligns more closely with real‐world scenarios, and effectively improves network command and control efficiency.

Primary clinics achieving regular follow-up of patients: Teamwork aspects among healthcare staff

Abstract Backgrounds Studies have shown that more temporally regular primary care visits, or visits more evenly spaced in time, are associated with improved patient outcomes. However, no study has examined what clinic staff can do to encourage greater temporal regularity (TR). We aimed to understand factors related to healthcare staff dynamics that contribute to higher TR for adults with chronic conditions. Methods The study is part of a two-stage mixed methods project. In the first stage, we ranked clinics according to their TR level. In this qualitative stage, we interviewed staff at 6 clinics with extremely high TR (regular follow-up with primary care physician, or PCP), and 6 clinics with extremely low TR. Interviews were conducted with 15 PCPs, 12 nurses, 15 administrative staff, and 4 pharmacists between July 2022- September 2023. Interviews were audiotaped, transcribed, and analyzed using Atlas qualitative analysis software. Findings Themes emerged regarding best ways to promote regular follow-up of patients with chronic conditions. Strategies included having a defined system to reach out to no-show patients, having routine staff meetings to discuss no-shows, adapting workflow to meet the needs of challenging patients, dealing with bureaucracy on patients’ behalf, informal channels of communication with patients, and consulting social workers more often. Relational aspects among staff members at high TR clinics emphasized flat as opposed to hierarchical relationships, as well as high levels of teamwork. Conclusions Teamwork between staff members in primary care settings can contribute to more proactive care delivery, with greater potential to prevent long-term complications. The findings suggest that a high-functioning multidisciplinary care team that focuses on creating the right sorts of interactions and teamwork among staff members, and uses proactive strategies to help engage patients, can contribute to more temporally regular care and thus improved outcomes. Key messages • Stronger teamwork and flat relationships between staff members in primary care settings can result in proactive medicine and developing strategies that improve patients’ regular follow-ups. • Programs focusing on how medical staff utilize their dynamics/relations to improve health outcomes and how doctors’ role align with the staff, can facilitate regular follow-ups of challenging patients.

Research on the evaluation method for safety cognitive ability of workers in high-risk construction positions

PurposeSafety cognitive ability is a key factor influencing unsafe behavior. However, the existing achievements have not yet involved the division of the hierarchical relationship of factors influencing safety cognition and lack a quantitative evaluation system of safety cognitive ability. The purpose of this paper is to find out the deficiencies in the safety cognition of workers in high-risk construction positions and to provide practical suggestions for improving their safety cognitive ability and reducing unsafe behavior.Design/methodology/approachBased on the iceberg model, the factors influencing the safety cognitive ability of workers in high-risk construction positions and their hierarchical relationship were determined, and an evaluation index system consisting of four primary indicators and 20 secondary indicators was constructed. The game theory algorithm was used to optimize the subjective and objective weights of the indicators calculated by the sequential analysis method (G1) and the entropy weighting method (EWM) to obtain the optimal combination weight value. The Matlab software was used for cloud mapping and similarity calculation to determine the safety cognitive ability level of the object to be evaluated.FindingsThe research results indicate that the comprehensive level of safety cognitive ability of scaffolders in this construction project is at “Level III”, the fundamental factors and compliance factors are at “Level IV”, the auxiliary factors and driving factors are at “Level III”. This conclusion aligns with the situation learned from the previous field investigation, which validates the feasibility and scientificity of the proposed evaluation method.Research limitations/implicationsConsidering that the safety cognitive ability of construction workers is constantly changing, this study has not yet delved into the specific impacts of various influencing factors on the level of safety cognitive ability. Future research can utilize simulation software, such as MATLAB and Vensim, to construct dynamic simulation models that accurately simulate the changing rules of construction workers’ safety cognitive ability under the influence of different factors.Practical implicationsThis research broadens the application scope of the iceberg model, enriches the analysis model of the factors influencing the safety cognitive ability of workers in high-risk construction positions and provides a novel perspective for similar research. The safety cognitive ability evaluation method proposed in this paper can not only accurately evaluate the safety cognitive ability level of workers in high-risk positions such as scaffolders but also provide practical suggestions for improving the safety cognitive ability of workers, which is of great significance to improve the safety management level and reduce unsafe behavior in the construction field.Originality/valueThis research fills the research gap of workers in high-risk construction positions and the quantification of safety cognitive ability. The iceberg model is used to realize the hierarchical division of the factors influencing safety cognitive ability. Additionally, an evaluation method for the safety cognitive ability of workers in high-risk construction positions based on the game theory combination weighting method and cloud model is proposed, which realizes the quantitative evaluation of safety cognitive ability.

Investigating the impediments to carbon emission mitigation in prefabricated construction: a stakeholder perspective

ABSTRACT Prefabricated construction is a promising global technology for reducing carbon emissions (CEs) in the construction sector. This study aims to identify critical factors affecting the life cycle of prefabricated buildings, understand their interrelationships, and develop targeted carbon reduction measures. A mixed-methods approach is employed, incorporating bibliometric analysis to identify key factors and the Delphi method to refine these factors through iterative surveys with diverse stakeholders. A 24-member scoring panel and a 3-member review panel consisting of experienced corporate practitioners were selected for the study. The DEMATEL-ISM approach was utilized to analyze causal and hierarchical relationships, revealing both direct and indirect impacts on CEs. The findings indicate that factors such as energy efficiency and behavior change significantly affect CEs in prefabricated buildings. The study categorizes 15 key factors, including energy efficiency, environmental properties, behavioral change, and water and solid waste management. Furthermore, it underscores the importance of building design, energy-saving awareness and habits, and policies and regulations in reducing carbon emissions in prefabricated construction. These insights are valuable for advancing sustainable building research, analyzing stakeholder attitudes, and informing engineering project studies.

Smart Logistics Facing Industry 5.0: Research on Key Enablers and Strategic Roadmap

While Industry 4.0 has played a significant role in advancing smart logistics, it has yet to provide adequate solutions for widespread concerns such as human centricity, sustainability, and resilience. The emergence of Industry 5.0 addresses and complements these shortcomings of Industry 4.0. However, there is currently a notable gap in the research regarding how Industry 5.0 can drive the transformation of smart logistics. To address this gap, this study develops a strategic roadmap that offers a solution to this issue. The research is initiated by conducting a comprehensive literature review with a focus on content, identifying 13 key enablers crucial for realizing smart logistics in Industry 5.0. Subsequently, this study establishes the hierarchical relationship among these key enablers through the application of the Fuzzy Interpretative Structural Model (FISM). Following this, the study employs the Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) to compute the driving force and dependence of each enabler. The results underscore the significant roles of “Active support from the government” and “Human-centric manufacturing and logistics” as the most critical enablers for Industry 5.0. The strategic roadmap, informed by expert opinions, provides valuable insights for policymakers and implementers while explaining the methods and strategies needed to drive Industry 5.0 transformation in smart logistics. Furthermore, it determines the impact relationship between enablers and the optimal development order, facilitating their synergistic alignment. Ultimately, this strategic roadmap serves as an actionable guide for the logistics industry, steering it toward achieving smart logistics and fortifying competitiveness in the industry 5.0 era.

Multimodal transport route selection: An integrated fuzzy hierarchy risk assessment and multiple criteria decision-making approach

Selecting multimodal transport routes is a complex problem due to the presence of various, often conflicting decision-making attributes, such as transportation cost and time, diverse risks with hierarchical relationships both within and across groups, decision-makers’ preferences, and numerous possible options. To address this problem, a novel decision-making approach is proposed, integrating the best-worst method (BWM), fuzzy hierarchy risk assessment (FHRA), and additive ratio assessment (ARAS). This approach decomposes the route decision-making problem into a hierarchy structure and determines the optimal solution through three distinct phases. In the first phase, BWM assigns relative weights to various criteria within the hierarchical structure, ensuring that the decision-makers’ preferences are accurately reflected. In the second phase, FHRA evaluates qualitative risks from the lowest to the highest hierarchy levels. This process involves calculating risk magnitudes at the lowest hierarchy level and progressively recalculating them as they ascend to the highest level of the hierarchy. The multimodal transport cost-model provides transportation cost and time. In the final phase, ARAS synthesizes all the decision-making data to rank all possible multimodal alternative routes based on utility scores. The realistic case study validates the practical applicability and effectiveness of the proposed approach. The findings indicated that the proposed methodology effectively identify the most appropriate multimodal transport route as the best compromise choice. This study contributes new knowledge regarding a new hybrid decision-making framework that combines multiple criteria decision-making methods with fuzzy hierarchy risk analysis, thereby enhancing the design of transportation plans to align with organizational goals and customer requirements.

Factors influencing the turnover intention for disease control and prevention workers in Northeast China: an empirical analysis based on logistic-ISM model.

This study aimed to determine the current turnover intention among workers at Centers for Disease Control and Prevention (CDCs) in Northeast China and to investigate the factors contributing to this phenomenon. The cross-sectional study was conducted in May 2023 across four northeastern provinces of China. The study included a total of 11,912 valid participants who were CDC workers selected using a stratified cluster sampling method. The study assessed demographics, turnover intention, work resources, work perceptions, and psychological support through online questionnaires. The binary logistic regression analysis identified the factors associated with turnover intention for CDC workers, while the Interpretative Structural Modelling (ISM) revealed the hierarchical relationship between the influencing factors. The study found that 28.8% of the respondents reported high turnover intention. The binary logistic regression suggested that the risk factors of turnover intention among employees included gender, age, education, and several work-related, organizational, and psychological factors. The work-related factors were daily working hours, job burnout, and role overload. The organizational factor was unit support for training, unit's infrastructure conditions, leadership style, remuneration package and performance appraisal and promotion system. The psychological support factors were family support, professional identity, and organizational commitment. The ISM analysis results showed that the primary factors influencing CDC workers' intention to leave were internally connected and grouped into four categories according to their level of impact: surface, transition, deep, and essential factors. Notably, the essential factors were professional identity and organizational commitment. Nearly one-third of the respondents expressed a strong desire to resign from their employment. Turnover intention among CDC workers was subject to diverse influences. Early identification, detection, and targeted multidisciplinary interventions should be introduced to address the array of factors that affect staff, with particular emphasis on boosting the professional identity and organizational commitment of CDC workers.

Energy Transition Policy of the Mexican Electricity Sector: A Representation in Terms of the Theory of Change

Energy transition in the electricity sector is central to limiting global warming to 1.5 °C. Countries around the world rely on official planning documents, such as strategies, special programs, and action plans, to guide these efforts. However, there are few systematic approaches and tools that can help policy makers determine comprehensiveness, coherence, consistency, and coordination when designing these planning documents. This represents a risk to meet energy transition and climate change goals if the relationships of policies and actions included in these documents are not addressed properly. To this end, this paper proposes an approach to model hierarchical relationships using the theory of change, while causal relationships are allocated using policy makers’ experience (yet can also be exogenously allocated using other models). As a case study, this work investigates Mexico’s energy transition policy of the electricity sector over the 2013–2018 period. The results show that Mexico’s official planning documents lack a good design since most policies and actions are not articulated in the right sequence and privilege clean centralized and distributed generation and energy efficiency, while others related to the modernization of electrical grids and climate and environmental policies are barely included.

One size does not fit all: deciphering the interdependence between barriers hindering SMEs’ involvement in public sector procurement

PurposeDespite their significant economic impact, small and medium-sized enterprises (SMEs) remain underrepresented in public procurement. While previous research has identified the barriers facing SMEs in public procurement markets, a knowledge gap still exists on how these barriers influence one another in a specific context.Design/methodology/approachThis study developed a hierarchical structural model that reveals the contextual interrelationships between 14 notable barriers to SME involvement in public procurement, based on evidence from the publicly funded healthcare sector in the UK. The data were analyzed using interpretive structural modeling (ISM) and matrix impact of cross-multiplication applied to categorization (MICMAC).FindingsThe results systematically classify barriers based on their hierarchical relationships, offering a robust theoretical foundation for understanding their nature and interconnections.Practical implicationsThe findings can help scholars, government administrators and politicians in government-funded healthcare systems determine the most influential barriers to SME involvement and their causes, enabling them to build viable remedies.Originality/valueThis is the first empirical study to examine the relationship between the barriers to SMEs' public health procurement, using evidence from the public health procurement context.

Knowledge Graph Embedding for Hierarchical Entities Based on Auto-Embedding Size

Knowledge graph embedding represents entities and relations as low-dimensional continuous vectors. Recently, researchers have attempted to leverage the potential semantic connections between entities with hierarchical relationships in the knowledge graph. However, existing knowledge embedding methods that model entity hierarchical structures face the following challenges: (1) Setting the same embedding dimension for entities at different hierarchical levels can lead to overfitting issues and the waste of storage and computational resources; (2) Manually searching the embedding dimension in discrete space makes it easy to miss the optimal parameters and increases training costs. Therefore, we propose a knowledge embedding method, HEAES, that automatically learns the embedding dimensions for entities based on their hierarchical structure. Specifically, we first propose a new modeling method to capture the hierarchical relationships of entities, and we then train the model on a triple classification task. Then, we adaptively learn the pruning threshold to trim the embedding vectors, automatically learning different embedding dimensions for entities at different hierarchical levels. Experiments on the YAGO26K and DB111K datasets verify that introducing entity hierarchical information helps boost the model performance.

Promoting a culture of respect in cardiothoracic surgery: navigating challenges in understanding unethical behaviour

Unprofessional behaviour in the workplace, such as bullying, harassment, and discrimination, remains common in the cardiothoracic surgery unit. Despite its prevalence, there is little consensus on what constitutes unprofessional behaviour in the workplace of a cardiothoracic surgeon. In this review, we aim to narrow down the definition of unprofessional behaviour among cardiothoracic surgeons, identify its prevalence and the factors contributing to it, as well as explore potential preventive and support measures. We searched the MEDLINE and Web of Science databases (2000-2022), focusing on publications within psychology, medicine, general surgery, and cardiothoracic surgery for inclusion in a focused review. The current review identified 89 papers that highlight the complexity of defining unprofessional behaviour and its prevalence in cardiothoracic surgery units. Psychosocial professional challenges, hierarchical relationships within the medical team, and a high-stress environment may prompt unprofessional behaviour, which can manifest in both overt and covert actions. Such behaviour negatively mpacts medical services for patients, the psycho-emotional state of employees, and the overall functioning of the medical institution. The most effective support measures are characterized by being proactive, anticipating potential problems before they occur. Unprofessional behaviour should be considered an organizational concern, not merely an issue between individual team members. This perspective is essential for fostering a healthy work environment. The leaders of cardiothoracic surgery, given their high status and authority, play a pivotal role in this regard; their focus on inclusiveness of employees with lower status or responsibility is crucial for promoting a culture of psychosocial safety. This culture should be characterized by trust, honesty, and mutual respect, ensuring that every team member feels valued and respected. By prioritizing these values, leaders can minimize the risk of unprofessional behaviour, ultimately leading to an improvement in the quality of medical services for patients, an improvement in the psycho-emotional state of employees and the functioning of the organization as a whole.

SmartISM 2.0: A Roadmap and System to Implement Fuzzy ISM and Fuzzy MICMAC

Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) to classify variables in four clusters, although the manual application of the technique is complex and prone to error. In one of the previous works, a novel concept of reduced conical matrix was introduced, and the SmartISM software was developed for the user-friendly implementation of ISM and MICMAC. The web-based SmartISM software has been used more than 48,123 times in 87 countries to generate ISM models and MICMAC diagrams. This work attempts to identify existing approaches to fuzzy ISM and fuzzy MICMAC and upscale the SmartISM to incorporate fuzzy approaches. The fuzzy set theory proposed by Zadeh 1965 and Goguen 1969 helps the decision makers to provide their input with the consideration of vagueness in the real environment. The systematic review of 32 studies identified five significant approaches that have used different linguistic scales, fuzzy numbers, and defuzzification methods. Further, the approaches have differences in either using single or double defuzzification, and the aggregation of inputs of decision makers either before or after defuzzification, as well as the incorporation of transitivity either before or after defuzzification. A roadmap was devised to aggregate and generalize different approaches. Further, two of the identified approaches have been implemented in SmartISM 2.0 and the results have been reported. Finally, the comparative analysis of different approaches using SmartISM 2.0 in the area of digital transformation shows that, with a wide flexibility of fuzzy scales, the results converge and improve the confidence in the final model. The roadmap and SmartISM 2.0 will help in the implementation of fuzzy ISM and fuzzy MICMAC in a more robust and informed way.

Identification of Postn+ periosteal progenitor cells with bone regenerative potential.

Bone contains multiple pools of skeletal stem/progenitor cells (SSPCs), and SSPCs in periosteal compartments are known to exhibit higher regenerative potential than those in BM and endosteal compartments. However, the in vivo identity and hierarchical relationships of periosteal SSPCs (P-SSPCs) remain unclear due to a lack of reliable markers to distinguish BM SSPCs and P-SSPCs. Here, we found that periosteal mesenchymal progenitor cells (P-MPs) in periosteum can be identified based on Postn-CreERT2 expression. Postn-expressing periosteal subpopulation produces osteolineage descendants that fuel bones to maintain homeostasis and support regeneration. Notably, Postn+ P-MPs are likely derived from Gli1+ skeletal stem cells (SSCs). Ablation of Postn+ cells results in impairments in homeostatic cortical bone architecture and defects in fracture repair. Genetic deletion of Igf1r in Postn+ cells dampens bone fracture healing. In summary, our study provides a mechanistic understanding of bone regeneration through the regulation of region-specific Postn+ P-MPs.

Social Workers' Experiences of Working With Support Workers in Services for Adults With Intellectual Disabilities in Hong Kong.

Services for adults with intellectual disabilities in Hong Kong have a hierarchical staffing structure. Professional social workers, who performed as mid-level employees, work with frontline support workers to deliver services to clients. This qualitative study explored social workers' experiences of working with support workers through in-depth interviews with 13 participants. The study revealed that social workers perceived a significant variation between themselves and support workers in terms of working motivation, values and attitudes towards clients with intellectual disabilities. Despite the hierarchical relationships, social workers and support workers share the power of service decision-making. Social workers used firm and gentle approaches to facilitate changes in practices to uphold service values. These findings were compared with Western literature on this topic, indicating similarities and differences. The organisations need to facilitate an effective collaboration between social workers and support workers to enhance the quality of services for adults with intellectual disabilities in Hong Kong.

Preface for LLMs4OL 2024: The 1st Large Language Models for Ontology Learning Challenge at the 23rd ISWC

We are pleased to present the proceedings of the “1st Large Language Models for Ontology Learning Challenge (LLMs4OL 2024)”, held at the 23rd International Semantic Web Conference (ISWC). This challenge marks a significant a dvancement in utilizing Large Language Models (LLMs) for Ontology Learning (OL)—a key Semantic Web component that facilitates the automatic extraction of structured knowledge from unstructured data. The challenge features three main tasks: Term Typing (identifying categories for terms), Taxonomy Discovery (uncovering hierarchical relationships), and Non-Taxonomic Relation Extraction (identifying other meaningful relationships between terms). Each task is designed to test different facets of ontology construction and to encourage the exploration of innovative techniques. This challenge seeks to foster collaboration, inspire innovation, and expand the capabilities of LLMs in OL. The proceedings include a collection of innovative solutions and insights from global participants, highlighting the crucial role of LLMs in enhancing the web with structured knowledge. We believe the outcomes of this challenge will propel further advancements in OL and its applications on the semantic web. We would like to extend our gratitude to all the participants for their invaluable contributions, which their solutions and dedication have greatly enriched this challenge. Our sincere thanks also go to the conference organizers and committee for their efforts in hosting this event. We are deeply appreciative of the reviewers for their evaluations and feedback. Their reviews have been instrumental in enhancing the quality of the submissions. We would like to specifically acknowledge: Dr. Amin Keshavarzi (Postdoctoral Researcher, L3S & TIB, Germany) Mostafa Rahgouy (Lead Project Coordinator, Auburn University, USA) Sahar Tahmasebi (Doctoral Researcher, TIB, Germany) Milad Molazadeh Oskuee (Lead NLP Researcher, Iran) Aida Usmanova (Doctoral Researcher, Leuphana Universit ¨ at L¨uneburg, Germany) Emetis Niazmand (Doctoral Researcher, TIB, Germany) Lastly, we would like to express our gratitude to ”Xenia Felice van Edig” and ”Karolina Linerová” from the TIB Open Publishing, for their support in bringing these proceedings online. We also would like to acknowledge that the 1st LLMs4OL Challenge @ ISWC 2024 jointly supported by the NFDI4DataScience initiative (DFG, German Research Foundation, Grant ID: 460234259) and the SCINEXT project (BMBF, German Federal Ministry of Education and Research, Grant ID: 01lS22070).

HeteroKGRep: Heterogeneous Knowledge Graph based Drug Repositioning

The process of developing new drugs is both time-consuming and costly, often taking over a decade and billions of dollars to obtain regulatory approval. Additionally, the complexity of patent protection for novel compounds presents challenges for pharmaceutical innovation. Drug repositioning offers an alternative strategy to uncover new therapeutic uses for existing medicines. Previous repositioning models have been limited by their reliance on homogeneous data sources, failing to leverage the rich information available in heterogeneous biomedical knowledge graphs. We propose HeteroKGRep, a novel drug repositioning model that utilizes heterogeneous graphs to address these limitations. HeteroKGRep is a multi-step framework that first generates a similarity graph from hierarchical concept relations. It then applies SMOTE over-sampling to address class imbalance before generating node sequences using a heterogeneous graph neural network. Drug and disease embeddings are extracted from the network and used for prediction. We evaluated HeteroKGRep on a graph containing biomedical concepts and relations from ontologies, pathways and literature. It achieved state-of-the-art performance with 99% accuracy, 95% AUC ROC and 94% average precision on predicting repurposing opportunities. Compared to existing homogeneous approaches, HeteroKGRep leverages diverse knowledge sources to enrich representation learning. Based on heterogeneous graphs, HeteroKGRep can discover new drug-disease associations, leveraging de novo drug development. This work establishes a promising new paradigm for knowledge-guided drug repositioning using multimodal biomedical data.