HermiT: An OWL 2 Reasoner

An ontology-based text mining dataset for extraction of process-structure-property entities

While large language models learn sound statistical representations of the language and information therein, ontologies are symbolic knowledge representations that can complement the former ideally. Research at this critical intersection relies on datasets that intertwine ontologies and text corpora to enable training and comprehensive benchmarking of neurosymbolic models. We present the MaterioMiner dataset and the linked materials mechanics ontology where ontological concepts from the mechanics of materials domain are associated with textual entities within the literature corpus. Another distinctive feature of the dataset is its eminently fine-grained annotation. Specifically, 179 distinct classes are manually annotated by three raters within four publications, amounting to 2191 entities that were annotated and curated. Conceptual work is presented for the symbolic representation of causal composition-process-microstructure-property relationships. We explore the annotation consistency between the three raters and perform fine-tuning of pre-trained language models to showcase the feasibility of training named entity recognition models. Reusing the dataset can foster training and benchmarking of materials language models, automated ontology construction, and knowledge graph generation from textual data.

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

Homeostasis imbalance process ontology: a study on COVID-19 infectious processes

BACKGROUND

One significant challenge in addressing the coronavirus disease 2019 (COVID-19) pandemic is to grasp a comprehensive picture of its infectious mechanisms. We urgently need a consistent framework to capture the intricacies of its complicated viral infectious processes and diverse symptoms.

RESULTS

We systematized COVID-19 infectious processes through an ontological approach and provided a unified description framework of causal relationships from the early infectious stage to severe clinical manifestations based on the homeostasis imbalance process ontology (HoIP). HoIP covers a broad range of processes in the body, ranging from normal to abnormal. Moreover, our imbalance model enabled us to distinguish viral functional demands from immune defense processes, thereby supporting the development of new drugs, and our research demonstrates how ontological reasoning contributes to the identification of patients at severe risk.

CONCLUSIONS

The HoIP organises knowledge of COVID-19 infectious processes and related entities, such as molecules, drugs, and symptoms, with a consistent descriptive framework. HoIP is expected to harmonise the description of various heterogeneous processes and improve the interoperability of COVID-19 knowledge through the COVID-19 ontology harmonisation working group.

Prototyping an Ontological Framework for Cellular Senescence Mechanisms: A Homeostasis Imbalance Perspective

Although cellular senescence is a key factor in organismal aging, with both positive and negative effects on individuals, its mechanisms remain largely unknown. Thus, integrating knowledge is essential to explain how cellular senescence manifests in tissue damage and age-related diseases. Here, we propose an ontological model that organizes knowledge of cellular senescence in a computer-readable form. We manually annotated and defined cellular senescence processes, molecules, anatomical structures, phenotypes, and other entities based on the Homeostasis Imbalance Process ontology (HOIP). We described the mechanisms as causal relationships of processes and modelled a homeostatic imbalance between stress and stress response in cellular senescence for a unified framework. HOIP was assessed formally, and the relationships between cellular senescence and diseases were inferred for higher-order knowledge processing. We visualized cellular senescence processes to support knowledge utilization. Our study provides a knowledge base to help elucidate mechanisms linking cellular and organismal aging.

PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.

A Semantic Approach to Describe Social and Economic Characteristics That Impact Health Outcomes (Social Determinants of Health): Ontology Development Study

BACKGROUND

Social determinants of health (SDoH) have been described by the World Health Organization as the conditions in which individuals are born, live, work, and age. These conditions can be grouped into 3 interrelated levels known as macrolevel (societal), mesolevel (community), and microlevel (individual) determinants. The scope of SDoH expands beyond the biomedical level, and there remains a need to connect other areas such as economics, public policy, and social factors.

OBJECTIVE

Providing a computable artifact that can link health data to concepts involving the different levels of determinants may improve our understanding of the impact SDoH have on human populations. Modeling SDoH may help to reduce existing gaps in the literature through explicit links between the determinants and biological factors. This in turn can allow researchers and clinicians to make better sense of data and discover new knowledge through the use of semantic links.

METHODS

An experimental ontology was developed to represent knowledge of the social and economic characteristics of SDoH. Information from 27 literature sources was analyzed to gather concepts and encoded using Web Ontology Language, version 2 (OWL2) and Protégé. Four evaluators independently reviewed the ontology axioms using natural language translation. The analyses from the evaluations and selected terminologies from the Basic Formal Ontology were used to create a revised ontology with a broad spectrum of knowledge concepts ranging from the macrolevel to the microlevel determinants.

RESULTS

The literature search identified several topics of discussion for each determinant level. Publications for the macrolevel determinants centered around health policy, income inequality, welfare, and the environment. Articles relating to the mesolevel determinants discussed work, work conditions, psychosocial factors, socioeconomic position, outcomes, food, poverty, housing, and crime. Finally, sources found for the microlevel determinants examined gender, ethnicity, race, and behavior. Concepts were gathered from the literature and used to produce an ontology consisting of 383 classes, 109 object properties, and 748 logical axioms. A reasoning test revealed no inconsistent axioms.

CONCLUSIONS

This ontology models heterogeneous social and economic concepts to represent aspects of SDoH. The scope of SDoH is expansive, and although the ontology is broad, it is still in its early stages. To our current understanding, this ontology represents the first attempt to concentrate on knowledge concepts that are currently not covered by existing ontologies. Future direction will include further expanding the ontology to link with other biomedical ontologies, including alignment for granular semantics.

Ontologies4Cat: investigating the landscape of ontologies for catalysis research data management

As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent for the domain of catalysis research. Thus, this work provides an approach for systematic collection of ontology metadata with focus on the catalysis research data value chain. By classifying ontologies by subdomains of catalysis research, the approach is offering efficient comparison across ontologies. Furthermore, a workflow and codebase is presented, facilitating representation of the metadata on GitHub. Finally, a method is presented to automatically map the classes contained in the ontologies of the metadata collection against each other, providing further insights on relatedness of the ontologies listed. The presented methodology is designed for its reusability, enabling its adaptation to other ontology collections or domains of knowledge. The ontology metadata taken up for this work and the code developed and described in this work are available in a GitHub repository at: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat .

C3PO: a crop planning and production process ontology and knowledge graph

Vegetable crop farmers diversify their production by growing a range of crops during the season on the same plot. Crop diversification and rotation enables farmers to increase their income and crop yields while enhancing their farm sustainability against climatic events and pest attacks. Farmers must plan their agricultural work per year and over successive years. Planning decisions are made on the basis of their experience regarding previous plans. For the purpose of assisting farmers in planning decisions and monitoring, we developed the Crop Planning and Production Process Ontology (C3PO), i.e., a representation of agricultural knowledge and data for diversified crop production. C3PO is composed of eight modules to capture all crop production dimensions and complexity for representing farming practices and constraints. It encodes agricultural processes and farm plot organization and captures common agricultural knowledge. C3PO introduces a representation of technical itineraries, i.e., sequences of technical farming tasks to grow vegetables, from soil identification and seed selection to harvest and storage. C3PO is the backbone of a knowledge graph which aggregates data from heterogeneous related semantic resources, e.g., organism taxonomies, chemicals, reference crop listings, or development stages. C3PO and its knowledge graph are used by the Elzeard enterprise to develop knowledge-based decision support systems for farmers. This article describes how we built C3PO and its knowledge graph-which are both publicly available-and briefly outlines their applications.

DEVO: an ontology to assist with dermoscopic feature standardization

BACKGROUND

The utilization of dermoscopic analysis is becoming increasingly critical for diagnosing skin diseases by physicians and even artificial intelligence. With the expansion of dermoscopy, its vocabulary has proliferated, but the rapid evolution of the vocabulary of dermoscopy without standardized control is counterproductive. We aimed to develop a domain-specific ontology to formally represent knowledge for certain dermoscopic features.

METHODS

The first phase involved creating a fundamental-level ontology that covers the fundamental aspects and elements in describing visualizations, such as shapes and colors. The second phase involved creating a domain ontology that harnesses the fundamental-level ontology to formalize the definitions of dermoscopic metaphorical terms.

RESULTS

The Dermoscopy Elements of Visuals Ontology (DEVO) contains 1047 classes, 47 object properties, and 16 data properties. It has a better semiotic score compared to similar ontologies of the same domain. Three human annotators also examined the consistency, complexity, and future application of the ontology.

CONCLUSIONS

The proposed ontology was able to harness the definitions of metaphoric terms by decomposing them into their visual elements. Future applications include providing education for trainees and diagnostic support for dermatologists, with the goal of generating responses to queries about dermoscopic features and integrating these features to diagnose skin diseases.

HL7-FHIR-Based ContSys Formal Ontology for Enabling Continuity of Care Data Interoperability

The rapid advancement of digital technologies and recent global pandemic-like scenarios have pressed our society to reform and adapt health and social care toward personalizing the home care setting. This transformation assists in avoiding treatment in crowded secondary health care facilities and improves the experience and impact on both healthcare professionals and service users alike. The interoperability challenge through standards-based roadmaps is the lynchpin toward enabling the efficient interconnection between health and social care services. Hence, facilitating safe and trustworthy data workflow from one healthcare system to another is a crucial aspect of the communication process. In this paper, we showcase a methodology as to how we can extract, transform and load data in a semi-automated process using a common semantic standardized data model (CSSDM) to generate a personalized healthcare knowledge graph (KG). CSSDM is based on a formal ontology of ISO 13940:2015 ContSys for conceptual grounding and FHIR-based specification to accommodate structural attributes to generate KG. The goal of CSSDM is to offer an alternative pathway to discuss interoperability by supporting a unique collaboration between a company creating a health information system and a cloud-enabled health service. The resulting pathway of communication provides access to multiple stakeholders for sharing high-quality data and information.

CIRO: COVID-19 infection risk ontology

Public health authorities perform contact tracing for highly contagious agents to identify close contacts with the infected cases. However, during the pandemic caused by coronavirus disease 2019 (COVID-19), this operation was not employed in countries with high patient volumes. Meanwhile, the Japanese government conducted this operation, thereby contributing to the control of infections, at the cost of arduous manual labor by public health officials. To ease the burden of the officials, this study attempted to automate the assessment of each person's infection risk through an ontology, called COVID-19 Infection Risk Ontology (CIRO). This ontology expresses infection risks of COVID-19 formulated by the Japanese government, toward automated assessment of infection risks of individuals, using Resource Description Framework (RDF) and SPARQL (SPARQL Protocol and RDF Query Language) queries. For evaluation, we demonstrated that the knowledge graph built could infer the risks, formulated by the government. Moreover, we conducted reasoning experiments to analyze the computational efficiency. The experiments demonstrated usefulness of the knowledge processing, and identified issues left for deployment.

Ontology-Driven Knowledge Sharing in Alzheimer’s Disease Research

Alzheimer’s disease is a debilitating neurodegenerative condition which is known to be the most common cause of dementia. Despite its rapidly growing prevalence, medicine still lacks a comprehensive definition of the disease. As a result, Alzheimer’s disease remains neither preventable nor curable. In recent years, broad interdisciplinary collaborations in Alzheimer’s disease research are becoming more common. Furthermore, such collaborations have already demonstrated their superiority in addressing the complexity of the disease in innovative ways. However, establishing effective communication and optimal knowledge distribution between researchers and specialists with different expertise and background is not a straightforward task. To address this challenge, we propose the Alzheimer’s disease Ontology for Diagnosis and Preclinical Classification (AD-DPC) as a tool for effective knowledge sharing in interdisciplinary/multidisciplinary teams working on Alzheimer’s disease. It covers six major conceptual groups, namely Alzheimer’s disease pathology, Alzheimer’s disease spectrum, Diagnostic process, Symptoms, Assessments, and Relevant clinical findings. All concepts were annotated with definitions or elucidations and in some cases enriched with synonyms and additional resources. The potential of AD-DPC to support non-medical experts is demonstrated through an evaluation of its usability, applicability and correctness. The results show that the participants in the evaluation process who lack prior medical knowledge can successfully answer Alzheimer’s disease-related questions by interacting with AD-DPC. Furthermore, their perceived level of knowledge in the field increased leading to effective communication with medical experts.

Surface realisation architecture for low-resourced African languages

There has been growing interest in building surface realisation systems to support the automatic generation of text in African languages. Such tools focus on converting abstract representations of meaning to a text. Since African languages are low-resourced, economical use of resources and general maintainability are key considerations. However, there is no existing surface realiser architecture that possesses most of the maintainability characteristics (e.g., modularity, reusability, and analysability) that will lead to maintainable software that can be used for the languages. Moreover, there is no consensus surface realisation architecture created for other languages that can be adapted for the languages in question. In this work, we solve this by creating a novel surface realiser architecture suitable for low-resourced African languages that abides by the features of maintainable software. Its design comes after a granular analysis, classification, and comparison of the architectures used by 77 existing NLG systems. We compare our architecture to existing architectures and show that it supports the most features of a maintainable software product.

Toward a standard formal semantic representation of the model card report

BACKGROUND

Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health's Bridge2AI initiative to address the FAIR challenges with artificial intelligence-based machine learning models for biomedical research. We present our early undertaking in developing an ontology for capturing the conceptual-level information embedded in model card reports.

RESULTS

Sourcing from existing ontologies and developing the core framework, we generated the Model Card Report Ontology. Our development efforts yielded an OWL2-based artifact that represents and formalizes model card report information. The current release of this ontology utilizes standard concepts and properties from OBO Foundry ontologies. Also, the software reasoner indicated no logical inconsistencies with the ontology. With sample model cards of machine learning models for bioinformatics research (HIV social networks and adverse outcome prediction for stent implantation), we showed the coverage and usefulness of our model in transforming static model card reports to a computable format for machine-based processing.

CONCLUSIONS

The benefit of our work is that it utilizes expansive and standard terminologies and scientific rigor promoted by biomedical ontologists, as well as, generating an avenue to make model cards machine-readable using semantic web technology. Our future goal is to assess the veracity of our model and later expand the model to include additional concepts to address terminological gaps. We discuss tools and software that will utilize our ontology for potential application services.

Towards ontological interoperability of cognitive IoT agents based on natural language processing¶

The interoperability of devices from distinct brands on the Internet of Things (IoT) domain is still an open issue. The main reason is that pioneer companies always deliberately neglected to deploy devices able to interoperate with competitors products. The key factors that may invert such a trend derive, on one hand, from the abstraction of communication protocols that facilitates the migration from vertical to horizontal paradigms and, on the other hand, from the introduction of common and shared ontologies encoding devices specifications. The Semantic Web, with all its layers, can be considered the main framework for delivering ontologies, and by virtue of its features, it is surely the ideal means for providing shared knowledge. In this paper we present a framework that instantiates cognitive agents operating in IoT context, endowed with meta-reasoning in the Semantic Web. The framework, called SW-Caspar, is also provided with a module that performs semi-automatic ontology learning from sentences expressed in natural language; such a learning process generates a conceptual space reflecting the domain of discourse with an instance of a novel foundational ontology called Linguistic Oriented Davidsonian Ontology (LODO), whose main feature is to increase the deepness of reasoning without compromising linguistic-related features. LODO is inspired by the First-Order Logic Davidsonian notation and is serialized in OWL 2. Well-known examples derived from the theory of logical reasoning and a case-study applied to automation on health scenarios are also provided.

Automated Rational Design of Metal-Organic Polyhedra

Metal-organic polyhedra (MOPs) are hybrid organic-inorganic nanomolecules, whose rational design depends on harmonious consideration of chemical complementarity and spatial compatibility between two or more types of chemical building units (CBUs). In this work, we apply knowledge engineering technology to automate the derivation of MOP formulations based on existing knowledge. For this purpose we have (i) curated relevant MOP and CBU data; (ii) developed an assembly model concept that embeds rules in the MOP construction; (iii) developed an OntoMOPs ontology that defines MOPs and their key properties; (iv) input agents that populate The World Avatar (TWA) knowledge graph; and (v) input agents that, using information from TWA, derive a list of new constructible MOPs. Our result provides rapid and automated instantiation of MOPs in TWA and unveils the immediate chemical space of known MOPs, thus shedding light on new MOP targets for future investigations.

Bridging the gap between expressivity and efficiency in stream reasoning: a structural caching approach for IoT streams

In today's data landscape, data streams are well represented. This is mainly due to the rise of data-intensive domains such as the Internet of Things (IoT), Smart Industries, Pervasive Health, and Social Media. To extract meaningful insights from these streams, they should be processed in real time, while solving an integration problem as these streams need to be combined with more static data and their domain knowledge. Ontologies are ideal for modeling this domain knowledge and facilitate the integration of heterogeneous data within data-intensive domains such as the IoT. Expressive reasoning techniques, such as OWL2 DL reasoning, are needed to completely interpret the domain knowledge and for the extraction of meaningful decisions. Expressive reasoning techniques have mainly focused on static data environments, as it tends to become slow with growing datasets. There is thus a mismatch between expressive reasoning and the real-time requirements of data-intensive domains. In this paper, we take a first step towards bridging the gap between expressivity and efficiency while reasoning over high-velocity IoT data streams for the task of event enrichment. We present a structural caching technique that eliminates reoccurring reasoning steps by exploiting the characteristics of most IoT streams, i.e., streams typically produce events that are similar in structure and size. Our caching technique speeds up reasoning time up to thousands of times for fully fledged OWL2 DL reasoners and even tenths and hundreds of times for less expressive OWL2 RL and OWL2 EL reasoners.

An ontology-based approach to the analysis of the acid-base state of patients at operative measures

Researchers working in various domains are focusing on extracting information from data sets by data mining techniques. However, data mining is a complicated task, including multiple complex processes, so that it is unfriendly to non-computer researchers. Due to the lack of experience, they cannot design suitable workflows that lead to satisfactory results. This article proposes an ontology-based approach to help users choose appropriate data mining techniques for analyzing domain data. By merging with domain ontology and extracting the corresponding sub-ontology based on the task requirements, an ontology oriented to a specific domain is generated that can be used for algorithm selection. Users can query for suitable algorithms according to the current data characteristics and task requirements step by step. We build a workflow to analyze the Acid-Base State of patients at operative measures based on the proposed approach and obtain appropriate conclusions.

Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis

In this paper, we demonstrate the potential of a knowledge-driven framework to improve the efficiency and effectiveness of care through remote and intelligent assessment. More specifically, we present a rule-based approach to detect health related problems from wearable lifestyle sensor data that add clinical value to take informed decisions on follow-up and intervention. We use OWL 2 ontologies as the underlying knowledge representation formalism for modelling contextual information and high-level concepts and relations among them. The conceptual model of our framework is defined on top of existing modelling standards, such as SOSA and WADM, promoting the creation of interoperable knowledge graphs. On top of the symbolic knowledge graphs, we define a rule-based framework for infusing expert knowledge in the form of SHACL constraints and rules to recognise patterns, anomalies and situations of interest based on the predefined and stored rules and conditions. A dashboard visualizes both sensor data and detected events to facilitate clinical supervision and decision making. Preliminary results on the performance and scalability are presented, while a focus group of clinicians involved in an exploratory research study revealed their preferences and perspectives to shape future clinical research using the framework.

An Alignment-Based Implementation of a Holistic Ontology Integration Method

Despite the intense research activity in the last two decades, ontology integration still presents a number of challenging issues. As ontologies are continuously growing in number, complexity and size and are adopted within open distributed systems such as the Semantic Web, integration becomes a central problem and has to be addressed in a context of increasing scale and heterogeneity. In this paper, we describe a holistic alignment-based method for customized ontology integration. The holistic approach proposes additional challenges as multiple ontologies are jointly integrated at once, in contrast to most common approaches that perform an incremental pairwise ontology integration. By applying consolidated techniques for ontology matching, we investigate the impact on the resulting ontology. The proposed method takes multiple ontologies as well as pairwise alignments and returns a refactored/non-refactored integrated ontology that faithfully preserves the original knowledge of the input ontologies and alignments. We have tested the method on large biomedical ontologies from the LargeBio OAEI track. Results show effectiveness, and overall, a decreased integration cost over multiple ontologies.•

OIAR and AROM are two implementations of the proposed method.

•

OIAR creates a bridge ontology, and AROM creates a fully merged ontology.

•

The implementation includes the option of ontology refactoring.

EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of Non-Used Information Buried in Routine Clinical Practice

Non-used clinical information has negative implications on healthcare quality. Clinicians pay priority attention to clinical information relevant to their specialties during routine clinical practices but may be insensitive or less concerned about information showing disease risks beyond their specialties, resulting in delayed and missed diagnoses or improper management. In this study, we introduced an electronic health record (EHR)-oriented knowledge graph system to efficiently utilize non-used information buried in EHRs. EHR data were transformed into a semantic patient-centralized information model under the ontology structure of a knowledge graph. The knowledge graph then creates an EHR data trajectory and performs reasoning through semantic rules to identify important clinical findings within EHR data. A graphical reasoning pathway illustrates the reasoning footage and explains the clinical significance for clinicians to better understand the neglected information. An application study was performed to evaluate unconsidered chronic kidney disease (CKD) reminding for non-nephrology clinicians to identify important neglected information. The study covered 71,679 patients in non-nephrology departments. The system identified 2,774 patients meeting CKD diagnosis criteria and 10,377 patients requiring high attention. A follow-up study of 5,439 patients showed that 82.1% of patients who met the diagnosis criteria and 61.4% of patients requiring high attention were confirmed to be CKD positive during follow-up research. The application demonstrated that the proposed approach is feasible and effective in clinical information utilization. Additionally, it's valuable as an explainable artificial intelligence to provide interpretable recommendations for specialist physicians to understand the importance of non-used data and make comprehensive decisions.

OWL2Vec*: embedding of OWL ontologies

Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology Language) ontologies, which contain richer semantic information than plain knowledge graphs, and have been widely adopted in domains such as bioinformatics. In this paper, we propose a random walk and word embedding based ontology embedding method named , which encodes the semantics of an OWL ontology by taking into account its graph structure, lexical information and logical constructors. Our empirical evaluation with three real world datasets suggests that benefits from these three different aspects of an ontology in class membership prediction and class subsumption prediction tasks. Furthermore, often significantly outperforms the state-of-the-art methods in our experiments.

Patient-Provider Communication Training Models for Interactive Speech Devices

Patient-provider communication plays a major role in healthcare with its main goal being to improve the patient's health and build a trustworthy relationship between the patient and the doctor. Provider's efficiency and effectiveness in communication can be improved through training in order to meet the essential elements of communication that are relevant during medical encounters. We surmised that speech-enabled conversational agents could be used as a training tool. In this study, we propose designing an ontology-based interaction model that can direct software agents to train dental and medical students. We transformed sample scenario scripts into a formalized ontology training model that links utterances of the user and the machine that expresses patient-provider communication. We created two instance-based models from the ontology to test the operational execution of the model using a prototype software engine. The assessment revealed that the dialogue engine was able to handle about 62% of the dialogue links. Future direction of this work will focus on further enhancing and capturing the features of patient-provider communication, and eventual deployment for pilot testing.

Conversational ontology operator: patient-centric vaccine dialogue management engine for spoken conversational agents

BACKGROUND

Previously, we introduced our Patient Health Information Dialogue Ontology (PHIDO) that manages the dialogue and contextual information of the session between an agent and a health consumer. In this study, we take the next step and introduce the Conversational Ontology Operator (COO), the software engine harnessing PHIDO. We also developed a question-answering subsystem called Frankenstein Ontology Question-Answering for User-centric Systems (FOQUS) to support the dialogue interaction.

METHODS

We tested both the dialogue engine and the question-answering system using application-based competency questions and questions furnished from our previous Wizard of OZ simulation trials.

RESULTS

Our results revealed that the dialogue engine is able to perform the core tasks of communicating health information and conversational flow. Inter-rater agreement and accuracy scores among four reviewers indicated perceived, acceptable responses to the questions asked by participants from the simulation studies, yet the composition of the responses was deemed mediocre by our evaluators.

CONCLUSIONS

Overall, we present some preliminary evidence of a functioning ontology-based system to manage dialogue and consumer questions. Future plans for this work will involve deploying this system in a speech-enabled agent to assess its usage with potential health consumer users.

Ontology-based computer aid for the automation of HAZOP studies

Hazard and Operability (HAZOP) studies are conducted to identify and assess potential hazards which originate from processes, equipment, and process plants. These studies are human-centered processes that are time and labor-intensive. Also, extensive expertise and experience in the field of process safety engineering are required. There have been several attempts by different research groups to (semi-)automate HAZOP studies in the past. Within this research, a knowledge-based framework for the automatic generation of HAZOP worksheets was developed. Compared to other approaches, the focus is on representing semantic relationships between HAZOP relevant concepts under consideration of the degree of abstraction. In the course of this, expert knowledge from the process and plant safety (PPS) domain is embedded within the ontological model. Based on that, a reasoning algorithm based on semantic reasoners is developed to identify hazards and operability issues in a HAZOP similar manner. An advantage of the proposed method is that by modeling causal relationships between HAZOP concepts, automatically generated but meaningless scenarios can be avoided. The results of the enhanced causation model are high quality extended HAZOP worksheets. The developed methodology is applied within a case study that involves a hexane storage tank. The quality and quantity of the automatically generated results agree with the original worksheets. Thus the ontology-based reasoning algorithm is well-suited to identify hazardous scenarios and operability issues. Node-based analyses involving multiple process units can also be carried out by a slight adjustment of the method. The presented method can help to support HAZOP study participants and non-experts in conducting HAZOP studies.

Ontological approach to the knowledge systematization of a toxic process and toxic course representation framework for early drug risk management

Various types of drug toxicity can halt the development of a drug. Because drugs are xenobiotics, they inherently have the potential to cause injury. Clarifying the mechanisms of toxicity to evaluate and manage drug safety during drug development is extremely important. However, toxicity mechanisms, especially hepatotoxic mechanisms, are very complex. The significant exposure of liver cells to drugs can cause dysfunction, cell injury, and organ failure in the liver. To clarify potential risks in drug safety management, it is necessary to systematize knowledge from a consistent viewpoint. In this study, we adopt an ontological approach. Ontology provides a controlled vocabulary for sharing and reusing of various data with a computer-friendly manner. We focus on toxic processes, especially hepatotoxic processes, and construct the toxic process ontology (TXPO). The TXPO systematizes knowledge concerning hepatotoxic courses with consistency and no ambiguity. In our application study, we developed a toxic process interpretable knowledge system (TOXPILOT) to bridge the gaps between basic science and medicine for drug safety management. Using semantic web technology, TOXPILOT supports the interpretation of toxicity mechanisms and provides visualizations of toxic courses with useful information based on ontology. Our system will contribute to various applications for drug safety evaluation and management.

Ontologies and Data Management: A Brief Survey

Information systems have to deal with an increasing amount of data that is heterogeneous, unstructured, or incomplete. In order to align and complete data, systems may rely on taxonomies and background knowledge that are provided in the form of an ontology. This survey gives an overview of research work on the use of ontologies for accessing incomplete and/or heterogeneous data.

BiPOm: a rule-based ontology to represent and infer molecule knowledge from a biological process-centered viewpoint

Background

Managing and organizing biological knowledge remains a major challenge, due to the complexity of living systems. Recently, systemic representations have been promising in tackling such a challenge at the whole-cell scale. In such representations, the cell is considered as a system composed of interlocked subsystems. The need is now to define a relevant formalization of the systemic description of cellular processes.

Results

We introduce BiPOm (Biological interlocked Process Ontology for metabolism) an ontology to represent metabolic processes as interlocked subsystems using a limited number of classes and properties. We explicitly formalized the relations between the enzyme, its activity, the substrates and the products of the reaction, as well as the active state of all involved molecules. We further showed that the information of molecules such as molecular types or molecular properties can be deduced by automatic reasoning using logical rules. The information necessary to populate BiPOm can be extracted from existing databases or existing bio-ontologies.

Conclusion

BiPOm provides a formal rule-based knowledge representation to relate all cellular components together by considering the cellular system as a whole. It relies on a paradigm shift where the anchorage of knowledge is rerouted from the molecule to the biological process.

Availability

BiPOm can be downloaded at https://github.com/SysBioInra/SysOnto

Knowledge-Based Biomedical Data Science

Knowledge-based biomedical data science involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.

GeoLink Data Set: A Complex Alignment Benchmark from Real-world Ontology

Ontology alignment has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships. In this paper, we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark. The data set consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions. The alignment includes 1:1, 1:n, and m:n equivalence and subsumption correspondences, and is available in both Expressive and Declarative Ontology Alignment Language (EDOAL) and rule syntax. The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies. This allows it to be used by extensional alignment systems or those that require training data. This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative (OAEI) complex track to help researchers test their automated alignment systems and algorithms. This paper also analyzes the challenges inherent in effectively generating, detecting, and evaluating complex ontology alignments and provides a road map for future work on this topic.

The Implementation of Credit Risk Scorecard Using Ontology Design Patterns and BCBS 239

Nowadays information and communication technologies are playing a decisive role in helping the financial institutions to deal with the management of credit risk. There have been significant advances in scorecard model for credit risk management. Practitioners and policy makers have invested in implementing and exploring a variety of new models individually. Coordinating and sharing information groups, however, achieved less progress. One of several causes of the 2008 financial crisis was in data architecture and information technology infrastructure. To remedy this problem the Basel Committee on Banking Supervision (BCBS) outlined a set of principles called BCBS 239. Using Ontology Design Patterns (ODPs) and BCBS 239, credit risk scorecard and applicant ontologies are proposed to improve the decision making process in credit loan. Both ontologies were validated, distributed in Ontology Web Language (OWL) files and checked in the test cases using SPARQL. Thus, making their (re)usability and expandability easier in financial institutions. These ontologies will also make sharing data more effective and less costly.

CoRiMaS—An Ontological Approach to Cooperative Risk Management in Seaports

For today’s global value chains, seaports and their operations are indispensable components. In many cases, the cargo handling takes place in close proximity to residential and/or environmentally sensitive areas. Furthermore, seaports are often not operated by a single organization, but need to be considered as communities of sometimes hundreds of internal and external stakeholders. Due to their close cooperation in the cargo handling process, risk management should be a common approach among the internal stakeholders as well in order to effectively mitigate and respond to emerging risks. However, empirical research has revealed that risk management is often limited to the organization itself, which indicates a clear lack of cooperation. Primary reasons in this regard are missing knowledge about the relations and responsibilities within the port and differing terminologies. Therefore, we propose an ontology (CoRiMaS) that implements a developed reference model for risk management that explicitly aims at seaports with a cooperative approach to risk management. CoRiMaS has been designed looking at the Semantic Web and at the Linked Data model to provide a common interoperable vocabulary in the target domain. The key concepts of our ontology comprise the hazard, stakeholder, seaport, cooperation aspect, and risk management process. We validated our ontology by applying it in a case study format to the Port of Hamburg (Germany). The CoRiMaS ontology can be widely applied to foster cooperation within and among seaports. We believe that such an ontological approach has the potential to improve current risk management practices and, thereby, to increase the resilience of operations, as well as the protection of sensitive surrounding areas.

SBOL Visual 2 Ontology

Standardizing the visual representation of genetic parts and circuits is essential for unambiguously creating and interpreting genetic designs. To this end, an increasing number of tools are adopting well-defined glyphs from the Synthetic Biology Open Language (SBOL) Visual standard to represent various genetic parts and their relationships. However, the implementation and maintenance of the relationships between biological elements or concepts and their associated glyphs has up to now been left up to tool developers. We address this need with the SBOL Visual 2 Ontology, a machine-accessible resource that provides rules for mapping from genetic parts, molecules, and interactions between them, to agreed SBOL Visual glyphs. This resource, together with a web service, can be used as a library to simplify the development of visualization tools, as a stand-alone resource to computationally search for suitable glyphs, and to help facilitate integration with existing biological ontologies and standards in synthetic biology.

A Logical Model of Homology for Comparative Biology

There is a growing body of research on the evolution of anatomy in a wide variety of organisms. Discoveries in this field could be greatly accelerated by computational methods and resources that enable these findings to be compared across different studies and different organisms and linked with the genes responsible for anatomical modifications. Homology is a key concept in comparative anatomy; two important types are historical homology (the similarity of organisms due to common ancestry) and serial homology (the similarity of repeated structures within an organism). We explored how to most effectively represent historical and serial homology across anatomical structures to facilitate computational reasoning. We assembled a collection of homology assertions from the literature with a set of taxon phenotypes for the skeletal elements of vertebrate fins and limbs from the Phenoscape Knowledgebase. Using seven competency questions, we evaluated the reasoning ramifications of two logical models: the Reciprocal Existential Axioms (REA) homology model and the Ancestral Value Axioms (AVA) homology model. The AVA model returned all user-expected results in addition to the search term and any of its subclasses. The AVA model also returns any superclass of the query term in which a homology relationship has been asserted. The REA model returned the user-expected results for five out of seven queries. We identify some challenges of implementing complete homology queries due to limitations of OWL reasoning. This work lays the foundation for homology reasoning to be incorporated into other ontology-based tools, such as those that enable synthetic supermatrix construction and candidate gene discovery. [Homology; ontology; anatomy; morphology; evolution; knowledgebase; phenoscape.].

Formal Ontologies in Information Systems Development: A Systematic Review

Computational ontologies are machine-processable structures which represent particular domains of interest. They integrate knowledge which can be used by humans or machines for decision making and problem solving. The main aim of this systematic review is to investigate the role of formal ontologies in information systems development, i.e., how these graphs-based structures can be beneficial during the analysis and design of the information systems. Specific online databases were used to identify studies focused on the interconnections between ontologies and systems engineering. One-hundred eighty-seven studies were found during the first phase of the investigation. Twenty-seven studies were examined after the elimination of duplicate and irrelevant documents. Mind mapping was substantially helpful in organising the basic ideas and in identifying five thematic groups that show the main roles of formal ontologies in information systems development. Formal ontologies are mainly used in the interoperability of information systems, human resource management, domain knowledge representation, the involvement of semantics in unified modelling language (UML)-based modelling, and the management of programming code and documentation. We explain the main ideas in the reviewed studies and suggest possible extensions to this research.