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The global pandemic over the past two years has reset societal agendas by identifying both strengths and weaknesses across all sectors. Focusing in particular on global health delivery, the ability of health care facilities to scale requirements and to meet service demands has detected the need for some national services and organisations to modernise their organisational processes and infrastructures. Core to requirements for modernisation is infrastructure to share information, specifically structural standardised approaches for both operational procedures and terminology services. Problems of data sharing (aka interoperability) is a main obstacle when patients are moving across healthcare facilities or travelling across border countries in cases where emergency treatment is needed. Experts in healthcare service delivery suggest that the best possible way to manage individual care is at home, using remote patient monitoring which ultimately reduces cost burden both for the citizen and service provider. Core to this practice will be advancing digitalisation of health care underpinned with safe integration and access to relevant and timely information. To tackle the data interoperability issue and provide a quality driven continuous flow of information from different health care information systems semantic terminology needs to be provided intact. In this paper we propose and present ContSonto a formal ontology for continuity of care based on ISO 13940:2015 ContSy and W3C Semantic Web Standards Language OWL (Web Ontology Language). ContSonto has several benefits including semantic interoperability, data harmonization and data linking. It can be use as a base model for data integration for different healthcare information models to generate knowledge graph to support shared care and decision making.

This paper describes a method by which the Web Ontology Language (OWL) can be used to specify a highly structured health record, following internationally recognised standards such as ISO 13606 and HL7 CDA. The structured record is coded using schemes such as SNOMED, ICD or LOINC, with the coding applied statically, on the basis of the predefined structure, or dynamically, on the basis of data values entered in the health record. The highly structured, coded record can then be linked with external knowledge sources which are themselves coded using the Resource Description Framework. These methods have been used to implement dynamic decision support in the open source cityEHR health records system. The effectiveness of the decision support depends on the scope and quality of the clinical coding and the sophistication of the algorithm used to match the structured record with knowledge sources.

Objective: The Foundational Model of Anatomy (FMA) [Rosse C, Mejino JLV. A reference ontology for bioinformatics: the Foundational Model of Anatomy. J. Biomed. Inform. 2003;36:478-500] is an ontology that represents canonical anatomy at levels ranging from the entire body to biological macromolecules, and has rapidly become the primary reference ontology for human anatomy, and a template for model organisms. Prior to this work, the FMA was developed in a knowledge modeling language known as Protégé Frames. Frames is an intuitive representational language, but is no longer the industry standard. Recognizing the need for an official version of the FMA in the more modern semantic web language OWL2 (hereafter referred to as OWL), the objective of this work was to create a generalizable Frames-to-OWL conversion tool, to use the tool to convert the FMA to OWL, to "clean up" the converted FMA so that it classifies under an EL reasoner, and then to do all further development in OWL., Methods: The conversion tool is a Java application that uses the Protégé knowledge representation API for interacting with the initial Frames ontology, and uses the OWL-API for producing new statements (axioms, etc.) in OWL. The converter is relation centric. The conversion is configurable, on a property-by-property basis, via user-specifiable XML configuration files. The best conversion, for each property, was determined in conjunction with the FMA knowledge author. The convertor is potentially generalizable, which we partially demonstrate by using it to convert our Ontology of Craniofacial Development and Malformation as well as the FMA. Post-conversion cleanup involved using the Explain feature of Protégé to trace classification errors under the ELK reasoner in Protégé, fixing the errors, then re-running the reasoner., Results: We are currently doing all our development in the converted and cleaned-up version of the FMA. The FMA (updated every 3 months) is available via our FMA web page http://si.washington.edu/projects/fma, which also provides access to mailing lists, an issue tracker, a SPARQL endpoint (updated every week), and an online browser. The converted OCDM is available at http://www.si.washington.edu/projects/ocdm. The conversion code is open source, and available at http://purl.org/sig/software/frames2owl. Prior to the post-conversion cleanup 73% of the more than 100,000 classes were unsatisfiable. After correction of six types of errors no classes remained unsatisfiable., Conclusion: Because our FMA conversion captures all or most of the information in the Frames version, is the only complete OWL version that classifies under an EL reasoner, and is maintained by the FMA authors themselves, we propose that this version should be the only official release version of the FMA in OWL, supplanting all other versions. Although several issues remain to be resolved post-conversion, release of a single, standardized version of the FMA in OWL will greatly facilitate its use in informatics research and in the development of a global knowledge base within the semantic web. Because of the fundamental nature of anatomy in both understanding and organizing biomedical information, and because of the importance of the FMA in particular in representing human anatomy, the FMA in OWL should greatly accelerate the development of an anatomically based structural information framework for organizing and linking a large amount of biomedical information., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Electronic commerce and finance are progressively supporting and including decentralized, shared and public ledgers such as the blockchain. This is reshaping traditional commercial activities by advancing them towards Decentralized Finance (DeFi) and Commerce 3.0, thereby supporting the latter's potential to outpace the hurdles of central authority controllers and lawgivers. The quantity and entropy of the information that must be sought and managed to become active participants in such a relentlessly evolving scenario are increasing at a steady pace. For example, that information comprises asset or service description, general rules of the game, and specific technologies involved for decentralization. Moreover, the relevant information ought to be shared among innumerable and heterogeneous stakeholders, such as producers, buyers, digital identity providers, valuation services, and shipment services, to just name a few. A clear semantic representation of such a complex and multifaceted blockchain-based e-Commerce ecosystem would contribute dramatically to make it more usable, namely more automatically accessible to virtually anyone wanting to play the role of a stakeholder, thereby reducing programmers' effort. However, we feel that reaching that goal still requires substantial effort in the tailoring of Semantic Web technologies, hence this article sets out on such a route and advances a stack of OWL 2 ontologies for the semantic description of decentralized e-commerce. The stack includes a number of relevant features, ranging from the applicable stakeholders through the supply chain of the offerings for an asset, up to the Ethereum blockchain, its tokens and smart contracts. Ontologies are defined by taking a behaviouristic approach to represent the various participants as agents in terms of their actions, inspired by the Theory of Agents and the related mentalistic notions. The stack is validated through appropriate metrics and SPARQL queries implementing suitable competency questions, then demonstrated through the representation of a real world use case, namely, the iExec marketplace. [ABSTRACT FROM AUTHOR]

Decentralised ledgers are gaining momentum following the interest of industries and people in smart contracts. Major attention is paid to blockchain applications intended for trading assets that exploit digital cryptographic certificates called tokens. Particularly relevant tokens are the non-fungible tokens (NFTs), namely, unique and non-replicable tokens used to represent the cryptographic counterpart of assets ranging from pieces of art through to licenses and certifications. A relevant consequence of the hard-coded nature of blockchains is the hardness of probing, in particular when advanced searchers involving the capabilities of the smart contracts or the assets digitised by NFTs are required. For this purpose, a formal representation for the operational semantics of smart contracts and of tokens has become particularly urgent, especially in economy and finance, where blockchains become increasingly relevant. Hence, we feel the need to tailor Semantic Web technologies to achieve that semantic representation at least for NFTS. This article reports on an ontology that leverages the Ontology for Agents, Systems, and Integration of Services ("OASIS") towards the semantic representation of smart contracts responsible for managing ERC721-compliant NFTs and running on the Ethereum blockchain. Called Ether-OASIS, the proposed ontology adopts OASIS and tailors its behaviouristic approach to the Ethereum blockchain by conceiving smart contracts as agents running on the blockchain and, consequently, smart contract interactions as agent commitments. Smart contracts are represented in terms of their actions, purposes and tokens that they manage, thus realising a blockchain that is more usable both by users and automated applications. The ontology is evaluated using standard ontological metrics and applied on a case study concerning the minting and transferring of NFTs that digitise batches of wheat. [ABSTRACT FROM AUTHOR]

Ontologies provide a powerful method for representing, reusing, and sharing domain knowledge. They are extensively used in a wide range of disciplines, including artificial intelligence, knowledge engineering, biomedical informatics, and many more. For several reasons, developing domain ontologies is a challenging task. One of these reasons is that it is a complicated and time-consuming process. Multiple ontology development methodologies have already been proposed. However, there is room for improvement in terms of covering more activities during development (such as enrichment) and enhancing others (such as conceptualization). In this research, an enhanced ontology development methodology (ON-ODM) is proposed. Ontology-driven conceptual modeling (ODCM) and natural language processing (NLP) serve as the foundation of the proposed methodology. ODCM is defined as the utilization of ontological ideas from various areas to build engineering artifacts that improve conceptual modeling. NLP refers to the scientific discipline that employs computer techniques to analyze human language. The proposed ON-ODM is applied to build a tourism ontology that will be beneficial for a variety of applications, including e-tourism. The produced ontology is evaluated based on competency questions (CQs) and quality metrics. It is verified that the ontology answers SPARQL queries covering all CQ groups specified by domain experts. Quality metrics are used to compare the produced ontology with four existing tourism ontologies. For instance, according to the metrics related to conciseness, the produced ontology received a first place ranking when compared to the others, whereas it received a second place ranking regarding understandability. These results show that utilizing ODCM and NLP could facilitate and improve the development process, respectively. [ABSTRACT FROM AUTHOR]

With the increase in the number of IoT farming datasets, it has become so difficult to identify the right data for IoT agriculture applications. Therefore, a meaningful structure is needed to well understand, interpret and index IoT farming datasets. This paper proposes a new IoT farming ontology that allows the organization, the understanding, and the classification of IoT agriculture datasets knowledge as well as meta-data storage. For this, we have developed a new IoT agriculture taxonomy that helps to identify an IoT agriculture application based on the combination of various IoT agriculture sensors. The evaluation of the semantic IoT agriculture datasets classification, based on the background knowledge provided by the proposed ontology, was achieved using Machine Learning algorithms, including Logistic Regression, Decision Tree Classifier, K-Neighbors Classifier, Linear Discriminant Analysis, Gaussian NB, SVM, and Random Forest Regressor. The obtained results clearly show the effectiveness of the proposed ontology to classify IoT agriculture datasets with high performances and accuracy (0.98), (0.99) using Decision tree classifier and SVM respectively.

Semantic representation is a key enabler for several application domains, and the multi-agent systems realm makes no exception. Among the methods for semantically representing agents, one has been essentially achieved by taking a behaviouristic vision, through which one can describe how they operate and engage with their peers. The approach essentially aims at defining the operational capabilities of agents through the mental states related with the achievement of tasks. The OASIS ontology — An Ontology for Agent, Systems, and Integration of Services, presented in 2019 — pursues the behaviouristic approach to deliver a semantic representation system and a communication protocol for agents and their commitments. This paper reports on the main modelling choices concerning the representation of agents in OASIS 2, the latest major upgrade of OASIS, and the achievement reached by the ontology since it was first introduced, in particular in the context of ontologies for blockchains. [ABSTRACT FROM AUTHOR]

Semantic representation is a key enabler for several application domains, and the multi-agent systems realm makes no exception. Among the methods for semantically representing agents, one has been essentially achieved by taking a behaviouristic vision, through which one can describe how they operate and engage with their peers. The approach essentially aims at defining the operational capabilities of agents through the mental states related with the achievement of tasks. The OASIS ontology — An Ontology for Agent, Systems, and Integration of Services, presented in 2019 — pursues the behaviouristic approach to deliver a semantic representation system and a communication protocol for agents and their commitments. This paper reports on the main modelling choices concerning the representation of agents in OASIS 2, the latest major upgrade of OASIS, and the achievement reached by the ontology since it was first introduced, in particular in the context of ontologies for blockchains. [ABSTRACT FROM AUTHOR]

Machine learning has been implemented as a part of many software systems to support data-driven decisions and recommendations. The prominent machine learning technique is the artificial neural network, which lacks the explanation of how it produces the output. However, many application domains require algorithmic decision making to be transparent so explainability in these systems has been an important challenge. This paper proposes an automated framework that elicits the contributing rules describing how the neural network model makes decisions. The explainability of contributing rules can be measured and it is able to address issues in the training dataset. With the ontology representation of contributing rules, an individual decision can be automatically explained through ontology reasoning. We have developed a tool that supports applying our framework in practice. The evaluation has been conducted to assess the effectiveness of our framework using open datasets from different domains. The results prove that our framework performs well to explain the neural network models, as it can achieve the average accuracy of 81% to explain the subject models. Also, our framework takes significantly less time to process than the other technique. [ABSTRACT FROM AUTHOR]

This paper details RDF/OWL storage and management in two popular Relational Database Management Systems (RDBMSs): Oracle and Virtuoso. Popularity, sustainability, and conformance with the SPARQL language are the main reasons for choosing these systems. This work combines empirical and analytical studies guided by a comparative framework developed and motivated in the paper. Several dimensions are considered, including RDF data type preservation, SPARQL query and update processing, reasoning capabilities, custom inferences, blank node management, and other functional and non-functional features. Furthermore, a review of the performance assessments reported in the literature has been conducted. This study's results identify the advantages and shortcomings of these RDBMSs for storing and managing RDF/OWL. They can help improve these systems or serve as a guide in choosing an appropriate system to use in a project context. Opportunities for further research efforts are also suggested. [ABSTRACT FROM AUTHOR]

Semantic digital technologies are as useful as they are difficult to implement. During its existence, the concept of the Semantic Web has still found very limited application and extremely slow development. The reasons for this are serious differences in the structure of hypertext and the Semantic Web as a superstructure above it, and the rules that circumscribed the development of the internet. Semantic search engines still do not implement semantic search in its pure form: various data organization schemes and data visualization formats make it a little easier to analyze facts, add them to databases, but do not make knowledge bases out of the latter, do not allow working with facts as with knowledge. In addition, the user interfaces of such systems tend to be either very complex or counter-intuitive. Nevertheless, there is every reason to believe that with the use of modern technologies, the ideas of semantization of the web and its content can already be improved and gradually implemented: for this it is necessary to get rid of some false expectations of semantic technologies, to direct efforts towards the development of web native formats of semantic markup and try to take advantage of the increasingly common voice input of household devices. [ABSTRACT FROM AUTHOR]

Temporal data (TD) in Semantic Web are affected by different types of imperfections principally conflict. In the literature, most of the proposed approaches deal with perfect TD. However, to our knowledge, there is no approach to dealing with conflicting TD. In this paper, we propose an approach to represent and reason about quantitative conflicting TD (i.e., time intervals and points) and associated qualitative relations (e.g., "before"). This approach is based on evidence theory and it is three folds. (i) For the representation, the mass function of the conflicting temporal data is estimated, through the believability measures estimated based on our previous DBE_ALZ approach. Then, an ontology-based representation for the handled TD associated with the obtained mass function is proposed. (ii) For the reasoning, our approach relies on the Allen's interval relations. First, we extend this algebra to reason about conflicting temporal relations. The resulting interval relations preserve the properties of the original algebra. Second, we adapt the proposed relations to define new ones relating a time interval and a time point, and two time points. All the proposed relations can be used for temporal reasoning through transitivity tables. (iii) Based on (i) and (ii), we propose a new evidential ontology named "BeliefTimeOnto". We implement a prototype to ease the interaction with the proposed ontology. We conduct two case studies: the first is about temporal data entered by Alzheimer's patients in the context of a memory prosthesis and the second is about data entered the context of Collective Memory application. The evaluation proves the usefulness of the proposed approach as all the inferences are well established and the precision results are interesting. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmias are not life-threatening straight away but can cause serious heart-related complications if not medically handled appropriately. An electrocardiogram (ECG) captures the heart's electric activity and has widespread usage due to its easy deployment and non-invasive aspect. Arrhythmia classification through manual analysis of electrocardiogram (ECG) is troublesome, tedious, and prone to human errors that can lead to serious repercussions. Hence, it's a more effective alternative to deploy computational techniques to automatically perform the classification. Traditional techniques are data-driven and require an immense amount of data to train and then perform identification. This paper presents an ontology-driven knowledge model to automatically diagnose arrhythmias based on the patient's sensor-based ECG data. The proposed approach models the arrhythmia domain knowledge and the conceptual relationships relevant to classification of heartbeat into corresponding cardiac arrhythmias and to facilitate decision making with respect to the patients. The newly developed arrhythmia ontology consists of three different modules, each semantically annotating a different aspect of the arrhythmia detection process. A SWRL (Semantic Web Rule Language) based ontology classifier performs classification of patient's ECG data into corresponding cardiac arrhythmia types. The constructed knowledge base is ontologically aligned with some benchmarked top-level ontologies that promotes the semantic interoperability across multiple domains. The resultant ontological model is validated with a real-world ECG dataset and compared with the existing approaches showing higher precision rate and comparable performance. The developed model establishes a standardized ontology, that promotes the exchange and shareability of consensual domain knowledge about arrhythmic conditions, supports information retrieval and knowledge discovery. [ABSTRACT FROM AUTHOR]

With the popularization of mobile Internet and the continuous improvement of automation, the huge amount of data and information that WWW (World Wide Web) can contain is in an exponential explosive growth trend. OWL (Web Ontology Language) is the official WWW Ontology Language recommended by W3C and is extended by RDF and RDFS. It has strong semantic representation and description ability and is widely used in Semantic Web. However, because most real-life instances have temporal and spatial attributes, the original OWL language, which lacks the ability to express temporal and spatial attributes, no longer has good semantic representation ability. To solve the problem, we fuse the spatiotemporal attributes of the existing OWL conceptual triples and propose the methods for representing spatiotemporal data. Then we define spatiotemporal OWL class relationships and spatiotemporal extended OWL class relationships which mainly based on the fusion of spatiotemporal attributes. To better explain the definitions, we give some examples to illustrate the use of definitions in instances. Our definitions fuse spatiotemporal attributes into each element of the triples, and solve the problem that the previous definitions ignore that each element in triples has spatiotemporal attribute. These definitions make the relationship between classes more comprehensive, and make the model more applicable to spatiotemporal data. [ABSTRACT FROM AUTHOR]

The Holy Quran, due to it is full of many inspiring stories and multiple lessons that need to understand it requires additional attention when it comes to searching issues and information retrieval. Many works were carried out in the Holy Quran field, but some of these dealt with a part of the Quran or covered it in general, and some of them did not support semantic research techniques and the possibility of understanding the Quranic knowledge by the people and computers. As for others, techniques of data analysis, processing, and ontology were adopted, which led to directed these to linguistic aspects more than semantic. Another weakness in the previous works, they have adopted the method manually entering ontology, which is costly and time-consuming. In this paper, we constructed the ontology of Quranic stories. This ontology depended in its construction on the MappingMaster domain-specific language (MappingMaster DSL) technology, through which concepts and individuals can be created and linked automatically to the ontology from Excel sheets. The conceptual structure was built using the object role modeling (ORM) modeling language. SPARQL query language used to test and evaluate the propsed ontology by asking many competency questions and as a result, the ontology answered all these questions well. [ABSTRACT FROM AUTHOR]

In this paper we present an ontological approach to build a knowledge-base on climate change related facts. Our knowledge space, developed in Ontology Web Language (OWL), enables knowledge within the Semantic Web. It allows a dynamic classification and a semantic characterization of the different events that can be related to each other, as well as to external sources of information. The resulting ontology is structured as a timeline which aims to describe the climate change story from multiple perspectives, including scientific, social, political and technological ones. We have created an instance of the ontology as an attempt to tell the climate change story so far. Such a population is based on the collection of factual information and critical literature review, with a focus on relevant theories, happenings, social and political initiatives.

UML class diagrams are used in order to demonstrate the model information systems. Most of the former systems were previously framed by applying UML class diagrams. Besides, the current trend has a growing demand of reusing data from older information systems, which is very beneficial for the implementation of semantic knowledge. The upgradation and conversion of UML class diagrams into ontology aim to reuse older systems; therefore, cost reduction is essential. There have been many studies for transformation from UML class diagrams into ontology; however, they have not fully proposed the transformation rules. This paper will suggest the addition of rules for transforming UML class diagrams to ontologies, such as attribute transformations which have data types as classes, the structured attributes, the association with association class, the recursive association, the aggregation with a qualification.

Featured Application: This article proposes a semantic web framework and application able to perform semantic analysis to create a common understanding about objects across varied robots, enabling robots to learn from each other's experience. Advanced service robots are not, as of yet, widely adopted, partly due to the effectiveness of robots' object recognition capabilities, the issue of object heterogeneity, a lack of knowledge sharing, and the difficulty of knowledge management. To encourage more widespread adoption of service robots, we propose an ontology-based framework for cooperative robot learning that takes steps toward solving these problems. We present a use case of the framework in which multiple service robots offload compute-intensive machine vision tasks to cloud infrastructure. The framework enables heterogeneous 3D object recognition with the use of ontologies. The main contribution of our proposal is that we use the Unified Robot Description Format (URDF) to represent robots, and we propose the use of a new Robotic Object Description (ROD) ontology to represent the world of objects known by the collective. We use the WordNet database to provide a common understanding of objects across various robotic applications. With this framework, we aim to give a widely distributed group of robots the ability to cooperatively learn to recognize a variety of 3D objects. Different robots and different robotic applications could share knowledge and benefit from the experience of others via our framework. The framework was validated and then evaluated using a proof-of-concept, including a Web application integrated with the ROD ontology and the WordNet API for semantic analysis. The evaluation demonstrates the feasibility of using an ontology-based framework and using the Ontology Web Language (OWL) to provide improved knowledge management while enabling cooperative learning between multiple robots. [ABSTRACT FROM AUTHOR]

Semantic web and its technologies have been eyed in many fields. They have the capacity to organize and link data over the web in a consistent and coherent way. Semantic web technologies consist of RDF schema, OWL, and rule and query languages like SPARQL and these technologies will help the various domains to resolve their problems. This review paper starts with analyzing the nature of semantic web and its requirements. We have considered all 10 domains which are closely related to semantic web and its technologies. For a better understanding of the paper, we have separated it into three major contributions. First, we analyze semantic web and those domains that increase the growth of the semantic web. Second, we discuss all domains where semantic web technologies play a vital role. Third, we emphasize those domains that go hand in hand with semantic web technologies. This review paper will be utilized as an unbiased direction for the researchers. [ABSTRACT FROM AUTHOR]