Semantic Query-enhanced Web Rule Language Research Articles

An ontology-driven model for hospital equipment maintenance management: a case study

Purpose This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.Design/methodology/approach The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.Findings Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.Originality/value An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

An ontology-based probabilistic framework for comprehensive seismic risk evaluation of subway stations by combining Monte Carlo simulation

The development of subways is currently at its peak and underground structures including subway stations are being constructed on a large scale. However, previous research on seismic risk evaluation of underground structures mainly focuses on the analysis of influencing factors and seismic fragility. There is a lack of research on comprehensive automated seismic risk evaluation from a macro perspective based on economic losses, casualties, disaster relief costs and post-earthquake recovery time etc. The emergence of semantic web technologies provides an effective way for multi-objective automated seismic risk evaluation of subway stations. This paper aims to develop a framework for comprehensive seismic risk evaluation of subway stations based on Monte Carlo simulation and ontology theory. In the developed framework, the seismic risk probabilities of subway stations are determined using Monte Carlo simulation. Taking economic losses, casualties, disaster relief costs and post-earthquake recovery time as indicators, an ontology-based model for seismic risk evaluation of subway stations is then developed by combining the knowledge base and the semantic web rules to achieve automated evaluations. A case study is also conducted to demonstrate the practicability of the proposed framework and further validate the semantic web rule language and the semantic query-enhanced web rule language

Developing a Dietary Lifestyle Ontology to Improve the Interoperability of Dietary Data: Proof-of-Concept Study

BackgroundDietary habits offer crucial information on one's health and form a considerable part of the patient-generated health data. Dietary data are collected through various channels and formats; thus, interoperability is a significant challenge to reusing this type of data. The vast scope of dietary concepts and the colloquial expression style add difficulty to standardizing the data. The interoperability issues of dietary data can be addressed through Common Data Elements with metadata annotation to some extent. However, making culture-specific dietary habits and questionnaire-based dietary assessment data interoperable still requires substantial efforts.ObjectiveThe main goal of this study was to address the interoperability challenge of questionnaire-based dietary data from different cultural backgrounds by combining ontological curation and metadata annotation of dietary concepts. Specifically, this study aimed to develop a Dietary Lifestyle Ontology (DILON) and demonstrate the improved interoperability of questionnaire-based dietary data by annotating its main semantics with DILON.MethodsBy analyzing 1158 dietary assessment data elements (367 in Korean and 791 in English), 515 dietary concepts were extracted and used to construct DILON. To demonstrate the utility of DILON in addressing the interoperability challenges of questionnaire-based multicultural dietary data, we developed 10 competency questions that asked to identify data elements sharing the same dietary topics and assessment properties. We instantiated 68 data elements on dietary habits selected from Korean and English questionnaires and annotated them with DILON to answer the competency questions. We translated the competency questions into Semantic Query-Enhanced Web Rule Language and reviewed the query results for accuracy.ResultsDILON was built with 262 concept classes and validated with ontology validation tools. A small overlap (72 concepts) in the concepts extracted from the questionnaires in 2 languages indicates that we need to pay closer attention to representing culture-specific dietary concepts. The Semantic Query-Enhanced Web Rule Language queries reflecting the 10 competency questions yielded correct results.ConclusionsEnsuring the interoperability of dietary lifestyle data is a demanding task due to its vast scope and variations in expression. This study demonstrated that we could improve the interoperability of dietary data generated in different cultural contexts and expressed in various styles by annotating their core semantics with DILON.

Bidirectional Transformation of MES Source Code and Ontologies

Future production environments must be flexible and reconfigurable. To achieve this, the devices and services to fulfill the different steps of a production order (PO) should not be selected in the manufacturing execution system (MES), but in an edge component close to the shop floor. To enable this, abstract services in the PO and concrete services provided by the field devices on the shop floor need to refer to a production ontology. The creation of this ontology is a challenge of its own. This research proposes a pragmatic automation of an encoding of a primary and light weight production ontology based on the source code of MES. The transformation procedure of source code to resource, product and generic concepts of the manufacturing plant ontology is described. To this end, the knowledge of OPC UA collaborations are also exploited during the creation of resource ontologies. Due to a fundamental difference between source code implementation (imperative paradigm) and ontology representation (declarative paradigm), the problem of information loss is inevitable. This problem is overcome by formulation of production and business rules that encapsulate the logic of the MES. The foundation of ontology is exploited to formulate these rulesets using OWL based constructs and OWL based rule languages such as Semantic Web Rule Language (SWRL), Semantic Query-Enhanced Web Rule Language (SQWRL) and SPARQL Protocol and RDF Query Language (SPARQL) based on feasibility and requirements of specific rules. Further, these rulesets are either run on the automatically generated ontology at design time with an intention to enrich the knowledge base, or production runtime to validate the pre-defined business rules between the production steps. The generated ontology also acts as basis for automatically generating the OWL-S ontologies for the OPC UA application methods for the purpose of dynamic manufacturing service discovery and orchestration. The generated ontology and an abstract PO hooked with formulated rules are cached to the shop-floor network for consequent production control to enable smart edge production. An implementation is conducted on an industrial use-case demonstrator to evaluate the applicability of the proposed approach.

Covid-19 Ontology Engineering-Knowledge Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

COVID-19 pandemic has rapidly spread across the world since its arrival in December 2019 from Wuhan, China. This pandemic has disrupted the health of the citizens in such a way that the impact is enormous in terms of economy and social aspects. Education, employment, income, well-being of the humankind is affected very crucially by this corona virus. Nations world-wide are struggling to battle this emergency. Intensive studies are being carried out to control this pandemic by researchers all over the world. Medical science has advanced a lot with the application of computer assisted solutions in health care. Ontology based clinical decision support systems (CDSS) assist medical practitioners in the diagnosis and treatment of diseases. They are well known in data sharing, interoperability, knowledge reuse, and decision support. This research article presents the development of ontology for SARS-CoV-2 (COVID-19) to be used in a CDSS, which is proposed in the satellite clinics of Royal Oman Police (ROP), Sultanate of Oman. The key concepts and the concept relationships of COVID-19 is represented using an ontology. Semantic Web Rule Language (SWRL) is used to model the rules related to the initial diagnosis of the patient and Semantic Query Enhanced Web Rule Language (SQWRL) is used to retrieve the data stored in the ontology. The developed ontology successfully classified the patients into one of the different categories as non-suspected, suspected, probable, and confirmed. The reasoning time and the query execution time is found to be optimal.

Design Ontology in a Case Study for Cosimulation in a Model-Based Systems Engineering Tool-Chain

Cosimulation is an important system-level verification approach aimed at integrating multidomain and multi-physics models during complex system development. Currently, the lack of integrating system development process with cosimulations leads to gaps between them, decreasing the effectiveness and efficiency of system development. Model-based systems engineering (MBSE) tool-chains have been proposed to facilitate the integration of complex system development and automated verification using a model-based approach. However, due to the lack of formal and structured specifications, development information sharing is difficult for supporting MBSE facilitating automated cosimulations. In order to formalize cosimulation in an MBSE tool-chain, a scenario-based ontology is developed in this paper, using formal web ontology language (OWL). Ontology refers to a specification expressing the cosimulation implementations as well as the development information represented in the models supporting the MBSE. It is illustrated by a case study of a cosimulation based on Simulink. Protocol and resource description framework (RDF) query language (SPARQL) and semantic query-enhanced web rule language queries are proposed for evaluating the ontology's completeness and logic for supporting cosimulations. The result demonstrates that the scenario-based ontology formalizes the information related to automated cosimulation development and configurations while using the proposed MBSE tool-chain.

A Design for Additive Manufacturing Ontology to Support Manufacturability Analysis

Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure the manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for achieving target goals. Furthermore, the wide variety of AM processes, materials, and machines creates challenges in determining manufacturability constraints. Therefore, this study presents a DFAM ontology using the web ontology language (OWL) to semantically model DFAM knowledge and retrieve that knowledge. The goal of the proposed DFAM ontology is to provide a structure for information on part design, AM processes, and AM capability to represent design rules. Furthermore, the manufacturing feature concept is introduced to indicate design features that are considerably constrained by given AM processes. After developing the DFAM ontology, queries based on design rules are represented to explicitly retrieve DFAM knowledge and analyze manufacturability using Semantic Query-enhanced Web Rule Language (SQWRL). The SQWRL rules enable effective reasoning to evaluate design features against manufacturing constraints. The usefulness of the DFAM ontology is demonstrated in a case study where design features of a bracket are selected as manufacturing features based on a rule development process. This study contributes to developing a reusable and upgradable knowledge base that can be used to perform manufacturing analysis.

An Ontology to Integrate Multiple Knowledge Domains of Training-Dietary-Competition in Weightlifting: A Nutritional Approach

This study is a part of weightlifting “Training-Dietary-Competition” (TDC) cycle ontology. The main objective of TDC-cycle is to build a knowledge framework for Olympic weightlifting, bringing together related fields such as training methodology, weightlifting biomechanics, and nutrition while modelling the synergy among them. In so doing, terminology, semantics, and used concepts are unified among athletes, coaches, nutritionists, and researchers to partially obviate the problem of unclear results and paucity of information. The uniqueness of this ontology is its ability to solve the knowledge sharing problem in which the knowledge owned by these experts in each field are not captures, classified or integrated into an information system for decision-making. The whole weightlifting TDC-cycle is semantically modelled by conceiving, designing, and integrating domain and task ontologies with the latter devising reasoning capability toward an automated and tailored weightlifting TDC-cycle. However, this study will focus mainly on the nutrition domain. The intended application of this part of ontology is to provide a useful decision-making platform for a sport nutritionist who gathers and integrate relevant scientific information, equation, and tools necessary when providing nutritional services. The system is constructed by using Web Ontology Language (OWL), Semantic Web Rule Language (SWRL), and Semantic Query-Enhanced Web Rule Language (SQWRL). The use of weightlifting TDC-cycle ontology can be helpful for nutritionists to create a well-planned nutrition program for athletes (especially, in the process of nutrition monitoring to identify energy imbalance in athletes) by reducing time consumption and calculation errors.

Software Experience for an Ontology-Based Approach for the Definition of Alarms in Geographical Sensor Systems

This paper presents a system based on ontologies for the definition of alarms in sensor systems. Although we consider that the ontology and the system are interesting themselves, we have a special impact on their experience with the software tools used and the technology related to the Semantic Web. This approach is intended to illustrate the process for those readers not familiar with this field and who can consider the use of ontologies for their next developments. An ontology has been designed in the OWL language, complemented with Semantic Web Rule Language rules and search elements in the Semantic Query-enhanced Web Rule Language. The set has offered satisfactory results in simulation. Other interesting contributions of this paper are: a survey of the available literature in the field of the use of Semantic Web technologies and ontologies for the detection of events from data obtained from sensors and a study on the tools and vocabularies to be used to create a system that interfaces with ontologies and an educational method—reporting our experience—to help university students understand this topic.

Cloud SLA Terms Analysis Based On Ontology

Services in cloud computing run under specified constraints defined in Service Level Agreement (SLA) which becomes the basic core that guarantee the Quality of Service (QoS). However, the ambiguity in agreement terms makes the consumer face new challenges especially in understanding and analyzing SLA document. These challenges increasingly rise when the consumer uses services from multiple providers; since each one has its own SLA terminology. In this paper, we have significantly automated the process of managing and analyzing cloud SLA using semantic web technologies like OWL (Ontology Web Language), SWRL (Semantic Web Rule Language) and SQWRL (Semantic Query-Enhanced Web Rule Language). We describe the cloud SLA modeling and analyzing approach that assists consumers automatically analyze terms of various SLA documents. A prototype implementation demonstrates the feasibility and the efficiency of our approach.

A knowledge representation for unit manufacturing processes

A unit manufacturing process is a fundamental operation unit in the manufacturing process of a mechanical product. It requires extensive knowledge of various processes to select a suitable unit process in process planning and manufacturing services. In the face of a large number of unit processes, it is very necessary to research and develop the knowledge representation and information processing approach for unit processes. In this paper, the basic unit manufacturing process concepts and relationships between them are analyzed in detail. The semantic Web technology is introduced into the knowledge modeling for unit processes, and a new ontology-based knowledge representation model is presented. A formalization for taxonomic semantics and descriptions of manufacturing capability knowledge is presented. A rule base that consists of a set of rules using Semantic Web Rule Language (SWRL) is built, which is used to enable reasoning on taxonomic semantics and manufacturing knowledge of unit processes. A knowledge retrieval approach that integrates Web Ontology Language (OWL) ontologies and SWRL rules is proposed. Some knowledge retrieval examples employing Semantic Query-enhanced Web Rule Language (SQWRL) are given. A knowledge prototype system for unit processes is developed. This research is a new attempt to construct an open, scalable, and shared manufacturing process knowledge system.

Migration From PLC to IEC 61499 Using Semantic Web Technologies

This paper proposes a new methodology of migration from IEC 61131-3 PLCs to IEC 61499 function blocks. The aim of this migration process is to recreate IEC 61131-3 applications in IEC 61499 implementations with equivalent execution behavior. The formal model of the IEC 61131-3 standard for migration and cyclical execution model is defined. This method also creates a foundation for correct-by-design development tools and automatic migration between the IEC 61131-3 and IEC 61499 standard. Formal migration rules based on ontology mappings, restoring execution model including tasks and programs scheduling and variables mapping with different access levels, are provided. A transformation engine for importing PLC code, mapping from PLC ontology model to function block model and code generation is implemented based on the ontological knowledge base and semantic query-enhanced web rule language. The migration approach is demonstrated on a simple airport baggage handling system.

IDENTIFYING DEAD FEATURES AND THEIR CAUSES IN PRODUCT LINE MODELS: AN ONTOLOGICAL APPROACH

Feature Models (FMs) are a notation to represent differences and commonalities between products derived from a product line. However, product line modelers could unintentionally incorporate dead features in FMs. A dead feature is a type of defect, which implies that one or more features are not present in any product of the product line. Some authors have used ontologies in product lines, but they have not exploited ontology reasoning to identify and explain causes for defects in FMs in natural language. In this paper, we propose an ontology that represents FMs in OWL (Web Ontology Language). Then, we use SQWRL (Semantic Query-enhanced Web Rule Language) to identify dead features in a FM and identify and explain certain causes of this defect in natural language. Our preliminary empirical evaluation confirms the benefits of our approach.

Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoring

This paper presents an ontology-based annotation system and BI-RADS (Breast Imaging Reporting and Data System) score reasoning with Semantic Web technologies in mammography. The annotation system is based on the Mammography Annotation Ontology (MAO) where the BI-RADS score reasoning works. However, ontologies are based on crisp logic and they cannot handle uncertainty. Consequently, we propose a Bayesian-based approach to model uncertainty in mammography ontology and make reasoning possible using BI-RADS scores with SQWRL (Semantic Query-enhanced Web Rule Language). First, we give general information about our system and present details of mammography annotation ontology, its main concepts and relationships. Then, we express uncertainty in mammography and present approaches to handle uncertainty issues. System is evaluated with a manually annotated dataset DEMS (Dokuz Eylul University Mammography Set) and DDSM (Digital Database for Screening Mammography). We give the result of experimentations in terms of accuracy, sensitivity, precision and uncertainty level measures.

Ontology-based mammography annotation and Case-based Retrieval of breast masses

This paper describes ontology-based annotation of mammography and a Case-based Retrieval approach for breast masses from digital mammography archive. We first present our Mammography Annotation Ontology focusing on its main concepts and relationships, as well as the annotation tool. Then, we propose a model for similarity calculation between breast masses based on their high, mid and low-level features. We use Semantic Query-enhanced Web Rule Language (SQWRL) to process retrieval of similar masses from annotated mammography collection in OWL. We give both retrieving process and results we obtained from experimentations, in detail.

A Policy-Based Team Collaboration

This paper presents a policy-based coordination model for team collaboration. Team collaboration requires an agreement that utilizes a negotiation protocol to find candidate teams and to decide on a collaboration partner. The decision relies on policies that are rules governing team situations in an organization. Contexts and rules allow reasoning about team situations. The authors describe a policy-based negotiation protocol. It introduces an ontology-based whiteboard component that uses the Semantic Web technologies such as Web Ontology Language (OWL), Semantic Web Rule Language (SWRL), and Semantic Query-enhanced Web Rule Language (SQWRL). The negotiation protocol facilitates whiteboards as a computational foundation for awareness of situations and policies, and it assists with the final decision using a measure based on the combination of rule-based queries and functions.

Translation ITIL Service Operation to Ontological Artifacts

This paper presents a Translation ITIL (Information Technology Infrastructure Library) Service Operation to Ontological Artifacts. The model is used to solve an unplanned interruption to an IT service or reduction in the quality of an IT service operation and the impact of incidents that cannot be prevented. The focus of the research is on IT service operation in organization , based on Incident Management and Problem Management of ITIL Framework. The research uses OWL design Ontological Artifacts and SQWRL (Semantic Query-enhanced Web Rule Language) which is a SWRLbased query language that can be used to query OWL ontologies, using Protege software as a tool. To implement Ontological Artifacts and Visualization of Process Activities we use Protege to design Business Cases. The Business Cases are presented in 4 types: Incident Management for Procurement , Problem Management for Procurement , Incident Management for Communication Network Failure and Problem Management for Communication Network Failure. The goal of this paper is to produce an OWL of those Business Cases. We organize a group of 5 domain experts to verify and validate. The mean value of evaluation is 4.60 (maximum=5) or 92%. This model has been implemented in the Computer Center’s Directorate of Communications and Electronics, Royal Thai Air Force.

An ontology-based approach to enhance querying capabilities of general practice medicine for better management of hypertension

Hypertension is one of the most prevalent chronic conditions and is directly correlated to deadly risks; yet, despite the availability of effective treatment, there is still clear room for improving patient outcomes. Use of relational databases is widespread for storing patient data, but formulating queries to identify patients whose clinical management can be improved is challenging due to the temporal nature of chronic illness and the mismatch in levels of abstraction between key management concepts and coded clinical data. The objective of this work is to develop a sharable and extensible analysis tool that can be used to identify hypertensive patients who satisfy any of a set of evidence-based criteria for quality improvement potential. We developed an ontology driven framework to enhance and facilitate some important temporal querying requirements in general practice medicine, focusing on prescribing for hypertension. The Web Ontology Language has been used to develop the ontology and the specific queries have been written in Semantic Query-enhanced Web Rule Language. We have used production electronic medical record (EMR) data from a General Medical Practice in New Zealand to populate the ontology. A unified patient management ontology consisting of a disease management ontology, a patient data ontology, and a plan violation taxonomy has been created and populated with EMR data. We have queried this ontology to determine patient cohorts that satisfy any of eight quality audit criteria, thereby identifying patients whose clinical management can be improved. A prescription timeline visualisation tool has also been developed to aid a clinician in understanding a patient's antihypertensive prescribing patterns, as well as visually validating the query results. The presented framework shows potential to provide answers to clinically relevant queries with complex temporal relationships. The framework can be used to successfully identify hypertensive patients who need to be followed-up/recalled.