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Knowledge representation and reasoning (KR&R) has been successfully implemented in many fields to enable computers to solve complex problems with AI methods. However, its application to biomedicine has been lagging in part due to the daunting complexity of molecular and cellular pathways that govern human physiology and pathology. In this article, we describe concrete uses of Scalable PrecisiOn Medicine Knowledge Engine (SPOKE), an open knowledge network that connects curated information from thirty-seven specialized and human-curated databases into a single property graph, with 3 million nodes and 15 million edges to date. Applications discussed in this article include drug discovery, COVID-19 research and chronic disease diagnosis, and management.

MotivationKnowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information.ResultsIn this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a 'parent table' of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts.Availability and implementationThe SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Knowledge representation and reasoning (KR&R) has been successfully implemented in many fields to enable computers to solve complex problems with AI methods. However, its application to biomedicine has been lagging in part due to the daunting complexity of molecular and cellular pathways that govern human physiology and pathology. In this article we describe concrete uses of SPOKE, an open knowledge network that connects curated information from 37 specialized and human-curated databases into a single property graph, with 3 million nodes and 15 million edges to date. Applications discussed in this article include drug discovery, COVID-19 research and chronic disease diagnosis and management.

Objective: Although social and environmental factors are central to provider-patient interactions, the data that reflect these factors can be incomplete, vague, and subjective. We sought to create a conceptual framework to describe and classify data about presence, the domain of interpersonal connection in medicine.Methods: Our top-down approach for ontology development based on the concept of "relationality" included the following: 1) a broad survey of the social sciences literature and a systematic literature review of >20 000 articles around interpersonal connection in medicine, 2) relational ethnography of clinical encounters (n = 5 pilot, 27 full), and 3) interviews about relational work with 40 medical and nonmedical professionals. We formalized the model using the Web Ontology Language in the Protégé ontology editor. We iteratively evaluated and refined the Presence Ontology through manual expert review and automated annotation of literature.Results and Discussion: The Presence Ontology facilitates the naming and classification of concepts that would otherwise be vague. Our model categorizes contributors to healthcare encounters and factors such as communication, emotions, tools, and environment. Ontology evaluation indicated that cognitive models (both patients' explanatory models and providers' caregiving approaches) influenced encounters and were subsequently incorporated. We show how ethnographic methods based in relationality can aid the representation of experiential concepts (eg, empathy, trust). Our ontology could support investigative methods to improve healthcare processes for both patients and healthcare providers, including annotation of videotaped encounters, development of clinical instruments to measure presence, or implementation of electronic health record-based reminders for providers.Conclusion: The Presence Ontology provides a model for using ethnographic approaches to classify interpersonal data. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to analyze the scientific cooperation network of ontology researchers using social network indicators. It also investigates the correlation between centrality indicators and the productivity and efficiency of researchers in this field. Methodology: The present study is an applied research project that utilizes scientometric techniques and indicators. The social network analysis method was employed to illustrate and analyze the scientific cooperation network in the field of ontology. In order to retrieve the outputs related to the field of ontology, a search was conducted for the term "Ontolog*" in the title and subject fields of the Web of Science database from 1990 to July 2, 2021. The search strategy used is TI=(Ontolog*) OR TS=(Ontolog*). In the next step, the recovered findings were limited to research conducted in the fields of computer science, information science, and librarianship. This is because ontology is a subject that is also used in other fields such as philosophy, religious studies, biology, and so on. Despite the fact that the concept of ontology in this research is "a tool that specifies the concepts of the related field, their characteristics, and the relationships between concepts and characteristics, thereby increasing the semantic interaction between documents and sources to process complex, advanced, and text-sensitive questions." Among the retrieved data, original articles, conference articles, review articles, and editorials with more credibility were selected. In the end, 29,611 research articles were obtained. The retrieved records were entered into VOSviewer version 16.6.1 software for matrix design and visual map design. To standardize the names, Gephi software was used. The matrix designed in Gephi software was then imported, and micro-indexes of social network analysis were calculated. These measures included degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. The calculation of macro indicators for the co-authorship network in the field of ontology outputs was also performed using the UCINET software. To test the research hypotheses, SPSS software Version 24 was utilized. Due to the non-normal distribution of the data, a non-parametric test (Spearman's correlation) was used to test the hypotheses. Findings: Examination of the four measures of centrality, which include degree centrality, closeness centrality, betweenness centrality, and special vector centrality, revealed that Pascal Hitzler from Kansas State University, USA; Stefano Borgo from the Italian National Research Council; Jeff Z. Pan from the University of Edinburgh, Scotland; Stefan Schulz from the Medical University of Graz, Austria; Barry Smith from the University of Buffalo, USA; Nicola Guarino from the National Research Council of Italy; Ian Horrock from the University of Oxford, England; Bernardo Cuenca Grau from the University of Oxford, England; Heiner Stuckenschmidt from the University of Mannheim, Germany; and Jerome Euzenat from the Diderot University of Paris, France are the most influential researchers in the co-authorship network in the field of ontology. The analysis of the scientific cooperation network in the field of ontology using macro indicators of social network analysis revealed that the network is not cohesive. This is evident from the density value, which is less than one, and the high clustering coefficient of the network. Of course, the flow of information in this network is fast, based on the network's diameter and average distance. Additionally, the findings revealed a significant and positive correlation between centrality measures (such as degree, closeness, betweenness, and special vector centrality) and both the number of scientific productions and the number of citations. However, it is worth noting that the relationship between closeness centrality and scientific productions was found to be not significant. Conclusion: The results showed that the cooperation between researchers who know each other has led to a decrease in the diameter of the network and the average distance in the web of ontology co-authorship. As a result, the information flow in this network has intensified, overcoming the weaknesses of low density and high clustering coefficient. According to the results, when authors collaborate with colleagues they know in the field of ontology, the network diameter and average distance decrease, resulting in increased information flow in the ontology co-authorship network. This holds true even when the density and clustering coefficient are high. [ABSTRACT FROM AUTHOR]

The design and operation of modern software systems exhibit a shift towards virtualization, containerization and service-based orchestration. Performance capacity engineering and resource utilization tuning become priority requirements in such environments. Measurement-based performance evaluation is the cornerstone of capacity engineering and designing for performance. Moreover, the increasing complexity of systems necessitates rigorous performance analysis approaches. However, empirical performance analysis lacks sophisticated model-based support similar to the functional design of the system. The paper proposes an ontology-based approach for facilitating and guiding the empirical evaluation throughout its various steps. Hyperledger Fabric (HLF), an open-source blockchain platform by the Linux Foundation, is modelled and evaluated as a pilot example of the approach, using the standard TPC-C performance benchmark workload. [ABSTRACT FROM AUTHOR]

Metareasoning suffers from the heterogeneity problem, in which different researchers build diverse metareasoning models for intelligent systems with comparable functionality but differing contexts, ambiguous terminology, and occasionally contradicting features and descriptions. This article presents an ontology-driven knowledge representation for metareasoning in intelligent systems. The proposed ontology, called IM-Onto, provides a visual means of sharing a common understanding of the structure and relationships between terms and concepts. A rigorous research method was followed to ensure that the two main requirements of the ontology (integrity based on relevant knowledge and acceptance by researchers and practitioners) were met. The high accuracy rate indicates that most of the knowledge elements in the ontology are useful information for the integration of multiple types of metareasoning problems in intelligent systems. [ABSTRACT FROM AUTHOR]

The end-to-end model has a wide range of applications in the fields of image recognition, natural language processing and speech recognition. The main benefit of this model is that the structure is a black box, and industrial users need only amend the inputs and outputs to obtain better performance for various applications. Nevertheless, in some contexts (e.g., medical-related tasks), the results of model generation require interpretability; furthermore, it is difficult to obtain the rich relationships of the hierarchical structure of the input, for the kind of data that are common in real life. In order to deal with these issues, this paper proposes an ontology-based deep belief network model to extend neural networks, using an approach that can be applied to hierarchical data. Multi-parameter data with hierarchical semantics are used in an experiment, and the results show that this model has the ability to handle hierarchical data and provide a certain level of interpretability. [ABSTRACT FROM AUTHOR]

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