Formal Ontology Research Articles

A Formal Ontology for Special Needs Conditions

Ontology deals with the conceptualization of domain concepts and the relationships among them while special needs conditions refer to any condition that requires assistance ranging from disability to giftedness and talentedness. Unified categorization of special needs conditions and the availability of comprehensive facts and knowledge about each category of special needs conditions have become major problems in the field of special education. It is, therefore, necessary to develop an ontology for special needs conditions that will unify the knowledge and fact about special needs conditions to support various decision-making in the special education domain. Hence, this research builds a formal ontology for special needs conditions that unifies the major categories of special needs conditions which are disability and giftedness, and talentedness, and includes various assistive technology that is applicable to each nature of special needs conditions. The knowledge engineering method was used to build the ontology while the language of formalization is Description logic. Web ontology language in protégé was used to implement the work and evaluated with competency questions in form of simple queries. This ontology can be adopted in all special education domains to forestall the problem of information overloading and inconsistency. Keywords: Special needs conditions, Assistive technology, Ontology, Protégé. CISDI Journal Reference Format Adebayo, E.O., Ayorinde, I.T & Akinkunmi, B.O. (2023): A Formal Ontology for Special Needs Conditions. Computing, Information Systems, Development Informatics Journal. Vol 14 No 2, Pp 31-60. Available online at https://www.isteams.net/cisdijournal. dx.doi.org/10.22624/AIMS/CISDI/V14N2P4x

A Formal Ontology for Special Needs Conditions

Ontology deals with the conceptualization of domain concepts and the relationships among them while special needs conditions refer to any condition that requires assistance ranging from disability to giftedness and talentedness. Unified categorization of special needs conditions and the availability of comprehensive facts and knowledge about each category of special needs conditions have become major problems in the field of special education. It is, therefore, necessary to develop an ontology for special needs conditions that will unify the knowledge and fact about special needs conditions to support various decision-making in the special education domain. Hence, this research builds a formal ontology for special needs conditions that unifies the major categories of special needs conditions which are disability and giftedness, and talentedness, and includes various assistive technology that is applicable to each nature of special needs conditions. The knowledge engineering method was used to build the ontology while the language of formalization is Description logic. Web ontology language in protégé was used to implement the work and evaluated with competency questions in form of simple queries. This ontology can be adopted in all special education domains to forestall the problem of information overloading and inconsistency. Keywords: Special needs conditions, Assistive technology, Ontology, Protégé. CISDI Journal Reference Format Adebayo, E.O., Ayorinde, I.T & Akinkunmi, B.O. (2023): A Formal Ontology for Special Needs Conditions. Computing, Information Systems, Development Informatics Journal. Vol 14 No 2, Pp 31-60. Available online at https://www.isteams.net/cisdijournal. dx.doi.org/10.22624/AIMS/CISDI/V14N2P4xx

Evaluation Method of Japanese Teaching Effect Based on Feature Offset Compensation

This study applies big data processing technology and parallel computing methods to assess the teaching effect of Japanese in the Flipped Classroom (FC) and task-based teaching mode. We propose a model for evaluating the Japanese teaching effect in this mode using feature offset compensation. We employ distributed mining of association rules to detect the teaching effect and extract ontology information and association rules related to the distribution of Japanese teaching effect in the FC and task-based teaching mode. Furthermore, we construct a fuzzy decision-making model for evaluating the teaching effect. The joint information entropy characteristic value of the teaching effect deviation is calculated, and the extracted characteristic quantity is classified and identified using feature deviation compensation and the C-means clustering method. Based on the classification and identification results, we achieve accurate evaluation of the Japanese teaching effect in the FC and task-based teaching mode. Our simulation results demonstrate high precision rates and confidence levels in the evaluation of Japanese teaching effect in this mode.

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.

The case for expressing nursing theories using ontologies.

Nursing and informatics share a common strength in their use of structured representations of domains, specifically the underlying notion of 'things' (ie, concepts, constructs, or named entities) and the relationships among those things. Accurate representation of nursing knowledge in machine-interpretable formats is a necessary next step for leveraging contemporary technologies. Expressing validated nursing theories in ontologies, and in particular formal ontologies, would serve not only nursing, but also investigators from other domains, clinical information system developers, and the users of advanced technologies such as artificial intelligence that seek to learn from the real-world data and evidence generated by nurses and others. Such efforts will enable sharing knowledge and conceptualizations about phenomena across the domains of nursing and generating, testing, revising, and providing theoretically-based perspectives when leveraging contemporary technologies. Nursing is well situated for this work, leveraging intentional and focused collaborations among nurse informaticists, scientists, and theorists.

A new focused crawler using an improved tabu search algorithm incorporating ontology and host information

A new focused crawler using an improved tabu search algorithm incorporating ontology and host information

A Semantic Representation Method of Building Codes Applied to Compliance Checking

Compliance checking is a very important step in engineering construction. With the development of information technology, automated compliance checking (ACC) has been paid more and more attention by researchers. One of the most important steps in automated compliance checking is the representation of the code information. However, the relationship constraint is often ignored in the code information and spatial geometric relationship is challenging to represent. The general code representation method does not have enough ability to identify the situation that does not meet the checking conditions because it is easy to cause semantic ambiguity in the checking results. This paper proposes a code representation method, and the building code information is represented in five parts. Relationships in the engineering domain and spatial relationships can be represented in constraint mode; different spatial relationship constraint-checking methods are also explicated. Constraint subject and constraint item can distinguish checking conditions and requirements, which supports semantic checking results. The mapping between the building information ontology and the code concepts is established, which can be used to automatically generate reasoning rules for compliance checking. Finally, the proposed method is verified by the representation of the China Metro Design Code and the application of the actual Metro model.

Semantic web-based diagnosis and treatment of vector-borne diseases using SWRL rules

Vector-borne diseases (VBDs) are a kind of infection caused through the transmission of vectors generated by the bites of infected parasites, bacteria, and viruses, such as ticks, mosquitoes, triatomine bugs, blackflies, and sandflies. If these diseases are not properly treated within a reasonable time frame, the mortality rate may rise. In this work, we propose a set of ontologies that will help in the diagnosis and treatment of vector-borne diseases. For developing VBD’s ontology, electronic health records taken from the Indian Health Records website, text data generated from Indian government medical mobile applications, and doctors’ prescribed handwritten notes of patients are used as input. This data is then converted into correct text using Optical Character Recognition (OCR) and a spelling checker after pre-processing. Natural Language Processing (NLP) is applied for entity extraction from text data for making Resource Description Framework (RDF) medical data with the help of the Patient Clinical Data (PCD) ontology. Afterwards, Basic Formal Ontology (BFO), National Vector Borne Disease Control Program (NVBDCP) guidelines, and RDF medical data are used to develop ontologies for VBDs, and Semantic Web Rule Language (SWRL) rules are applied for diagnosis and treatment. The developed ontology helps in the construction of decision support systems (DSS) for the NVBDCP to control these diseases.

Knowledge graph construction based on ship collision accident reports to improve maritime traffic safety

As an important data source, marine accident investigation reports are frequently used for accident analysis. However, it is hard to extract effective information since key knowledge is normally hidden in large blocks of text. In most cases, the collection of accident-related data is done manually. In this paper, a new knowledge graph construction approach to explore ship collision accidents is proposed, aiming to show the correlation among important factors of the accidents. In this research, 241 investigation reports on ship collision accidents from 2018 to 2021 published on the official website of the China Maritime Safety Administration (CMSA) were collected and analyzed. Then, the ship collision accident ontology module is constructed. According to the ontology information in the accident reports, entities were divided into context-based metadata and content-based metadata, which were used for describing different types of data. To extract the information for the accident report with semi-structured data, an information extraction module based on ontology was proposed. In this process, natural language processing (NLP) was used to obtain text information about the ontology. On this basis, the Ship Collision Accident Knowledge Graph (SCAKG) including 910 entity nodes and 1920 relation edges was constructed and stored in the graph database Neo4j. Finally, two case retrievals were conducted using the SCAKG to show the potential utilization of the method. The results show the effectiveness of the proposed approach in terms of discovering the internal relationship of the accident and could be used to expedite the judicial process, which simplifies the process of marine accident investigation.

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Ontology generation is a process of relationship analysis and representation for multiple data categories using automatic or semi-automatic approaches. Thus, the main contribution of this paper is the design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification (BOGMAS) that can overcome existing issues. The BOGMAS model uses a semi-supervised approach for ontology generation from almost any structured or unstructured dataset. This model uses a combination of linear support vector machine, and extra trees stratifies for variance estimation, which makes the model highly efficient, and reduces redundant features from the output ontology. The generated ontology is represented using an incremental OWL (W3C Web Ontology Language) format, which assists in dynamically sizing the ontology depending on incoming data. The performance of the proposed BOGMAS model is evaluated in terms of precision and recall of representation, memory usage, computational complexity, and accuracy of attack detection. It is observed that the proposed model is highly efficient in terms of precision, recall and accuracy performance, but has incrementally higher computational complexity and delay of ontology formation when compared with existing approaches. Due to this incremental increase in delay, the proposed model is observed to be applicable for a wide variety of real-time scenarios, which include but are not limited to, medical ontology generation, sports ontology generation, and internet of things ontology generation with high-security levels.

Terminology and ontology development for semantic annotation: A use case on sepsis and adverse events

Annotations enrich text corpora and provide necessary labels for natural language processing studies. To reason and infer underlying implicit knowledge captured by labels, an ontology is needed to provide a semantically annotated corpus with structured domain knowledge. Utilizing a corpus of adverse event documents annotated for sepsis-related signs and symptoms as a use case, this paper details how a terminology and corresponding ontology were developed. The Annotated Adverse Event NOte TErminology (AAENOTE) represents annotated documents and assists annotators in annotating text. In contrast, the complementary Catheter Infection Indications Ontology (CIIO) is intended for clinician use and captures domain knowledge needed to reason and infer implicit information from data. The approach taken makes ontology development understandable and accessible to domain experts without formal ontology training.

A Proposal for Basic Formal Ontology for Knowledge Management in Building Information Modeling Domain

The construction industry produces enormous amounts of information, relying on building information modeling (BIM). However, due to interoperability issues, valuable information is not being used properly. Ontology offers a solution to this interoperability. A complete knowledge base can be provided by reusing basic formal ontology (BFO). In previous studies, domain ontology was developed without BFO. Domain ontology requires loads of effort to reuse because domain ontology is too detailed. To increase the reuse rate and establish a complete knowledge base, it is necessary to develop BFO. This study has developed the BFO in the BIM domain to advance interoperability. First, unnecessary parts were omitted from the existing BFO development process, the process was simplified, and the base of hierarchy was created by extracting the most basic superclasses of the BFO model from Revit, the software of BIM. Based on that hierarchy, each child class was created, and the BFO model was completed by completing the relation of each class. After completion of the model, reliability, in addition to the completeness of the model, was evaluated through a query. Domain experts can reuse the BFO when defining relations between concepts and entities. The proposed BFO will be the foundation of future ontology developments in the BIM domain. This study facilitates future researchers to enhance interoperability in the BIM domain and make the ontology more complete to improve information sharing.

A hybrid system to understand the relations between assessments and plans in progress notes

Objective:The paper presents a novel solution to the 2022 National NLP Clinical Challenges (n2c2) Track 3, which aims to predict the relations between assessment and plan subsections in progress notes. Methods:Our approach goes beyond standard transformer models and incorporates external information such as medical ontology and order information to comprehend the semantics of progress notes. We fine-tuned transformers to understand the textual data and incorporated medical ontology concepts and their relationships to enhance the model’s accuracy. We also captured order information that regular transformers cannot by taking into account the position of the assessment and plan subsections in progress notes. Results:Our submission earned third place in the challenge phase with a macro-F1 score of 0.811. After refining our pipeline further, we achieved a macro-F1 of 0.826, outperforming the top-performing system during the challenge phase. Conclusion:Our approach, which combines fine-tuned transformers, medical ontology, and order information, outperformed other systems in predicting the relationships between assessment and plan subsections in progress notes. This highlights the importance of incorporating external information beyond textual data in natural language processing (NLP) tasks related to medical documentation. Our work could potentially improve the efficiency and accuracy of progress note analysis.

Surgical procedure prediction using medical ontological information

Background and Objective: Predicting the duration of surgical procedures is an important step in scheduling operating rooms. Many factors have been shown to influence the duration of a procedure, in this research we aim to use medical ontological information to improve the predictions.Methods: This paper presents two methods for incorporating the medical information about a surgical procedure into the prediction of the duration of the procedure. The first method uses the Systematised Nomenclature of Medicine Clinical Terms to relate different procedures to each other. The second uses simple text fragments. The relationships between types of procedures are included in a regression model for the procedure duration. These methods are applied to data from New Zealand healthcare facilities and the accuracy of the estimations of the durations is compared. In addition a simulation of scheduling the procedures in an operating room is performed.Results: It is shown that both of the methods provide an improvement in the prediction of procedure durations. When compared to a traditional categorical encoding, the ontological information provides an improvement in the continuous ranked probability scores of the prediction of procedure durations from 18.4 min to 17.1 min, and from 25.3 to 21.3 min for types of procedures that are not performed very often.Conclusions: Different methods for encoding medical ontological information in surgery procedure duration predictions are presented, and show an improvement over traditional models. The improvement in duration prediction is shown to improve the efficiency of scheduling in a simple simulation.

Epitope1D: accurate taxonomy-aware B-cell linear epitope prediction.

The ability to identify B-cell epitopes is an essential step in vaccine design, immunodiagnostic tests and antibody production. Several computational approaches have been proposed to identify, from an antigen protein or peptide sequence, which residues are more likely to be part of an epitope, but have limited performance on relatively homogeneous data sets and lack interpretability, limiting biological insights that could otherwise be obtained. To address these limitations, we have developed epitope1D, an explainable machine learning method capable of accurately identifying linear B-cell epitopes, leveraging two new descriptors: a graph-based signature representation of protein sequences, based on our well-established Cutoff Scanning Matrix algorithm and Organism Ontology information. Our model achieved Areas Under the ROC curve of up to 0.935 on cross-validation and blind tests, demonstrating robust performance. A comprehensive comparison to alternative methods using distinct benchmark data sets was also employed, with our model outperforming state-of-the-art tools. epitope1D represents not only a significant advance in predictive performance, but also allows biologically meaningful features to be combined and used for model interpretation. epitope1D has been made available as a user-friendly web server interface and application programming interface at https://biosig.lab.uq.edu.au/epitope1d/.

A strategy for archives metadata representation on CIDOC-CRM and knowledge discovery

This paper presents a strategy for the semantic migration of Portuguese National Archives records into CIDOC-CRM standard, an ontology developed for museums, within the context of the EPISA project. The approach to automatically populate the CIDOC-CRM is based on Mapping Description Rules to semantically translate the archives descriptive information into CIDOC-CRM representation. The compliance of the CIDOC-CRM model recommendations guarantees that the populated CIDOC-CRM ontology of archives descriptive information verifies interoperability, and could be linked and integrated with other populated CIDOC-CRM ontologies. In the information modelling, requirements on the mapping representation, due to the intent of interpreting natural language text to automatically extract information of metadata text fields and to interpret natural language queries, are taken into account. To automatically interpret the Mapping Description Rules, OWL API was used to obtain the set of assertions that represents the information in the target ontology and two datasets are available with some migration examples. The exploration of the knowledge representation is done through some Description Logic queries to highlight the advantages of having this new representation of the National Archives. The evaluation of the resulting representation can be done automatically proving its correctness for the metadata that has a direct representation in CIDOC-CRM.

An effective MBSE approach for constructing industrial robot digital twin system

Recently, the rapid development of digital twin (DT) technology has been regarded significant in Cyber-physical systems (CPS) promotion. Scholars are focusing on the theoretical architecture and implementing applications, in order to establish a high-fidelity, dynamic, and full-lifecycle DT model and achieve a deep fusion of real and virtual. As a typical complex system with multi-disciplines, multi-physics, and multi-domain characteristics, industrial robot (IR) involves various processes and elements from the two other levels of the system: components and production lines. Their complex relationships lead to a huge challenge to build a comprehensive DT model. Current researchers usually concentrates on single-layer services because of limited construction methodology, which results in enormous isolated models, and leads to low reusable system blocks, finite scalability, and high costs of design, adjustment, upgrade, and maintenance. To address these issues, a standardized methodology and a hierarchical, modular, and generic architecture are proposed to depict comprehensive and variable industrial robot digital twin (IRDT). Firstly, the ontology information model is presented by analyzing variable factors systematically. Then, model-based system engineering (MBSE) based methodology is introduced, including construction process and variants management. After modeling process of three levels (problem domain, solution main, and implementation domain) and four viewpoints (requirement, structure, behavior, and parameter), a generic architecture of IRDT is constructed and a feature-based variants management method is described. Besides, a six-axis IRDTS is implemented to illustrate the mapping of logical architecture and physical system as a multi-level elements and processes representation example. And the steps of numerical evaluations consist of system delay and derivation. Finally, results show the effectiveness and the potential of the proposed theoretical methodology for constructing IRDTS and other industrial applications.

Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana.

Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model, (ii) a Domain2vec encoding-based multiple layer perceptron (MLP) model, and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared to existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and datasets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.

End-to-end interpretable disease-gene association prediction.

Identifying disease-gene associations is a fundamental and critical biomedical task towards understanding molecular mechanisms, the diagnosis and treatment of diseases. It is time-consuming and expensive to experimentally verify causal links between diseases and genes. Recently, deep learning methods have achieved tremendous success in identifying candidate genes for genetic diseases. The gene prediction problem can be modeled as a link prediction problem based on the features of nodes and edges of the gene-disease graph. However, most existing researches either build homogeneous networks based on one single data source or heterogeneous networks based on multi-source data, and artificially define meta-paths, so as to learn the network representation of diseases and genes. The former cannot make use of abundant multi-source heterogeneous information, while the latter needs domain knowledge and experience when defining meta-paths, and the accuracy of the model largely depends on the definition of meta-paths. To address the aforementioned challenges above bottlenecks, we propose an end-to-end disease-gene association prediction model with parallel graph transformer network (DGP-PGTN), which deeply integrates the heterogeneous information of diseases, genes, ontologies and phenotypes. DGP-PGTN can automatically and comprehensively capture the multiple latent interactions between diseases and genes, discover the causal relationship between them and is fully interpretable at the same time. We conduct comprehensive experiments and show that DGP-PGTN outperforms the state-of-the-art methods significantly on the task of disease-gene association prediction. Furthermore, DGP-PGTN can automatically learn the implicit relationship between diseases and genes without manually defining meta paths.

THE MACHINE IN THE GHOST: TRANSHUMANISM AND THE ONTOLOGY OF INFORMATION

AbstractAn ontology of information belies our common intuitions about reality today and animates and governs both explicit scholarly study in philosophy and the sciences as well as the ideologies that are growing out of them. Transhumanism is one such technoscientific ideology that holds to a very specific ontology of information which need not be the only one on offer. This article argues that the transhumanist ontology of information exhibits gnostic and docetic religious overtones in it and that it devalues physical existence. At the same time, despite claiming a rejection of supernature, hypothetical transhumanist practices (such as mind‐uploading) posit the infosphere as a kind of supernatural realm that is often set in opposition to the natural world. This article presents a critique of transhumanist conceptions of information and offers an alternative ontology of information that more adequately accounts for the distinction between the natural and supernatural as well as the integrity of the physical world.