Ontology-based Knowledge Base Research Articles

Risk identification in Japanese consumer product injury data throughout an ontology-based knowledge base

Risk identification and further risk assessment of consumer product injury data are critical tools for accident prevention. However, in Japan, existing risk assessment methods face challenges due to issues such as inconsistent semantic understanding of risk-related vocabulary among Japanese analysts, posing obstacles to the effective identification and assessment of risk information. This study develops an ontology-based knowledge base (JCPRI-KB), which includes an ontology (JCPRI-Onto) and a total of 41 standardised vocabulary sets, for the risk information identification and standardization of consumer product injury data in Japan. A graph database was used to represent and store the risk knowledge of JCPRI-KB. Finally, the JCPRI-KB is applied for a case study of risk identification and further risk assessment of child product injury data. The risk assessment results of this study demonstrate that the JCPRI-KB offers a promising new approach to the field of consumer product safety in Japan, contributing to the enhancement of accuracy and efficiency in risk identification and thus promoting consumer product safety.

Geometric feature extraction in manufacturing based on a knowledge graph

In times of global crises, the resilience of production chains is becoming increasingly important. If a supply chain is interrupted, a cost-effective solution must be established quickly. In the context of Industry 4.0, the concept of smart manufacturing offers a solution for fast and automated decision-making in production planning. The core idea of smart manufacturing is the digitalization of the product life cycle and the linking of individual phases of this cycle. Computer Aided Process Planning (CAPP) plays an important role as the connecting element between design and manufacturing. An important prerequisite for CAPP is the automated analysis of 3D models of components. The aim of this work is the development of an automatic feature recognition (AFR) -method to recognize geometric manufacturing features and their properties from 3D-models and then store them in a knowledge base. In that way, the result of the design can be automatically analysed and compared with manufacturing information afterwards in order to achieve an automated process planning. Geometric and topological information of a 3D model (STEP-AP242 format) generated by CAD systems is extracted by a Python-script developed and stored in an ontology-based knowledge base. The extracted product data is analysed using a Python-script to identify manufacturing features. To provide a comprehensive extensibility of the model, geometric features are defined according to a layered and hierarchical structure.

Personalized Flexible Meal Planning for Individuals With Diet-Related Health Concerns: System Design and Feasibility Validation Study.

Chronic diseases such as heart disease, stroke, diabetes, and hypertension are major global health challenges. Healthy eating can help people with chronic diseases manage their condition and prevent complications. However, making healthy meal plans is not easy, as it requires the consideration of various factors such as health concerns, nutritional requirements, tastes, economic status, and time limits. Therefore, there is a need for effective, affordable, and personalized meal planning that can assist people in choosing food that suits their individual needs and preferences. This study aimed to design an artificial intelligence (AI)-powered meal planner that can generate personalized healthy meal plans based on the user's specific health conditions, personal preferences, and status. We proposed a system that integrates semantic reasoning, fuzzy logic, heuristic search, and multicriteria analysis to produce flexible, optimized meal plans based on the user's health concerns, nutrition needs, as well as food restrictions or constraints, along with other personal preferences. Specifically, we constructed an ontology-based knowledge base to model knowledge about food and nutrition. We defined semantic rules to represent dietary guidelines for different health concerns and built a fuzzy membership of food nutrition based on the experience of experts to handle vague and uncertain nutritional data. We applied a semantic rule-based filtering mechanism to filter out food that violate mandatory health guidelines and constraints, such as allergies and religion. We designed a novel, heuristic search method that identifies the best meals among several candidates and evaluates them based on their fuzzy nutritional score. To select nutritious meals that also satisfy the user's other preferences, we proposed a multicriteria decision-making approach. We implemented a mobile app prototype system and evaluated its effectiveness through a use case study and user study. The results showed that the system generated healthy and personalized meal plans that considered the user's health concerns, optimized nutrition values, respected dietary restrictions and constraints, and met the user's preferences. The users were generally satisfied with the system and its features. We designed an AI-powered meal planner that helps people create healthy and personalized meal plans based on their health conditions, preferences, and status. Our system uses multiple techniques to create optimized meal plans that consider multiple factors that affect food choice. Our evaluation tests confirmed the usability and feasibility of the proposed system. However, some limitations such as the lack of dynamic and real-time updates should be addressed in future studies. This study contributes to the development of AI-powered personalized meal planning systems that can support people's health and nutrition goals.

Research on Construction and Application of Ocean Circulation Spatial–Temporal Ontology

Due to the absence of a comprehensive knowledge system for modeling ocean circulation, there is ambiguity and diversity in the semantic expression of ocean circulation. This makes it difficult to organize and share relevant spatiotemporal data effectively. This paper addresses the issue of ocean circulation by introducing ontological theory and methodology based on a comprehensive analysis of domain knowledge. Through a comprehensive analysis of the conceptual and relational characteristics of different elements, we define classes, properties, spatiotemporal relationships, and inference conditions with which to formally express concepts and relationships in ocean circulation, and finally complete the construction of ocean circulation ontology. The formal expression of the Equatorial Counter Current is presented as an example with which to validate the effectiveness of ontological construction. Additionally, an ontology-based knowledge base of ocean circulation is proposed. The construction framework is described, and several examples of knowledge base queries are also illustrated. The results demonstrate that this ontology can effectively represent the relevant knowledge within ocean circulation and provide a meaningful reference for investigating knowledge sharing and semantic integration within this field.

Ontology-Based Driving Simulation for Traffic Lights Optimization

Traffic lights optimization is one of the principal components to lessen the traffic flow and travel time in an urban area. The present article seeks to introduce a novel procedure to design the traffic lights in a city using evolutionary-based optimization algorithms in combination with an ontology-based driving behavior simulation framework. Accordingly, an ontology-based knowledge base is introduced to provide a machine-understandable knowledge of roads and intersections, traffic rules, and driving behaviors. Then, a simulation environment is developed to inspect car behavior in real time. To optimize the traffic lights, a sine-based equation was defined for each traffic light, and the total travel time of the vehicles was considered as the cost function in the optimization algorithm. The optimization was performed with 5, 10, 15, 20, 25, and 30 vehicles in the urban areas. Based on the results, in contrast to uncontrolled intersections without traffic lights, optimized traffic lights can significantly contribute to total travel time-saving. To conclude, due to an escalation in the number of vehicles, the significance of optimized traffic lights has encountered an increase, and unoptimized traffic lights could increase total travel time even more than a city deprived of any traffic light.

Ontology knowledge base combined with Bayesian networks for integrated corridor risk warning

With the accelerated urbanization process, the emergence of urban underground integrated pipeline corridors is the trend for cities, especially large and medium-sized cities. However, due to the complexity of the internal system of the integrated corridor, there are various risks in the process of its construction and operation and maintenance, and the risk factors are complex and diverse. In this paper, we introduce ontology technology and knowledge base construction into the risk management of integrated pipeline corridor, build an ontology-based knowledge base of integrated pipeline corridor risk, and construct a Bayesian network based on the established risk knowledge base for risk evaluation of identified risk factors. The combination of ontology knowledge base construction and Bayesian network method of integrated pipeline corridor risk makes the risk identification system completer and more effective, and the method can effectively evaluate the disaster risk level of integrated pipeline corridor operation and maintenance, which can meet the practical needs of integrated pipeline corridor operation and maintenance risk management and disaster prevention and mitigation work.

IrGO: Iranian traditional medicine General Ontology and knowledge base

BackgroundIranian traditional medicine, also known as Persian Medicine, is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified language system has been proposed for the concepts of this medicine up to the present time. Considering the extensive terminology in the numerous textbooks written by the scholars over centuries, comprehending the totality of concepts is obviously a very challenging task. To resolve this issue, overcome the obstacles, and code the concepts in a reusable manner, constructing an ontology of the concepts of Iranian traditional medicine seems a necessity.Construction and contentMakhzan al-Advieh, an encyclopedia of materia medica compiled by Mohammad Hossein Aghili Khorasani, was selected as the resource to create an ontology of the concepts used to describe medicinal substances. The steps followed to accomplish this task included (1) compiling the list of classes via examination of textbooks, and text mining the resource followed by manual review to ensure comprehensiveness of extracted terms; (2) arranging the classes in a taxonomy; (3) determining object and data properties; (4) specifying annotation properties including ID, labels (English and Persian), alternative terms, and definitions (English and Persian); (5) ontology evaluation. The ontology was created using Protégé with adherence to the principles of ontology development provided by the Open Biological and Biomedical Ontology (OBO) foundry.Utility and discussionThe ontology was finalized with inclusion of 3521 classes, 15 properties, and 20,903 axioms in the Iranian traditional medicine General Ontology (IrGO) database, freely available at http://ir-go.net/. An indented list and an interactive graph view using WebVOWL were used to visualize the ontology. All classes were linked to their instances in UNaProd database to create a knowledge base of ITM materia medica.ConclusionWe constructed an ontology-based knowledge base of ITM concepts in the domain of materia medica to help offer a shared and common understanding of this concept, enable reuse of the knowledge, and make the assumptions explicit. This ontology will aid Persian medicine practitioners in clinical decision-making to select drugs. Extending IrGO will bridge the gap between traditional and conventional schools of medicine, helping guide future research in the process of drug discovery.

SWRL reasoning on ontology-based clinical dengue knowledge base

Dengue is a widespread mosquito-borne viral illness that may lead to death if not treated timely and properly. The aim of this study is to propose a semantic rule-based modelling and reasoning approach directed towards formalising dengue disease definition in conjunction with operational definitions (semantics) that support clinical and diagnostic reasoning. The operational definitions are incorporated using Semantic Web Rule Language (SWRL) as logical rules that enhance the expressive capability of the knowledge base. A dengue knowledge base has been designed which is extended with International Classification of Diseases (ICD) ontology for associating dengue fever with ICD code. The knowledge base created can be reasoned upon for diagnostic classification that can discover dengue symptoms and predict the possibility of patients to suffer from the disease apart from offering interoperability. 153 real patient cases are classified successfully against the operational definitions incorporated by SWRL rules.

The application of an amended FCA method on knowledge acquisition and representation for interpreting meteorological services

In the process of building an ontology-based knowledge base, developers need to transform the knowledge within the domain into a system conceptual framework that can be processed. This process is usually divided into two parts: knowledge acquisition and knowledge representation. If the way of acquiring knowledge is arbitrary, it is easy to form a concept that does not have consensus. When generating a conceptual hierarchy, it also needs to face the transformation of level confirmation and expression. Based on formal concept analysis (for knowledge acquisition) and description logic (for knowledge representation), this paper explores the use, problems and inconsistencies of these two aspects, and proposes an amended method. In addition, a method of exploring how to develop an unknown concept is proposed. This paper provides an ontology case based on the application amended method for the meteorological service field to solve the problem that the hidden knowledge in the meteorological service field is difficult to find and the concept processing is inaccurate, and the Formal Concept Analysis and implementation process of the meteorological service field are expounded. The method after the correction is to collect abstract and objective concept formation factors, and finally name the specific and subjective concepts. The formation of the concept of ontology is more in line with the cognitive development process.

The ontology-based approach to data storage systems technical diagnostics

Monitoring and diagnosing the state of data storage systems, as well as assessing reliability and troubleshooting, require a formalized health model. A comparative analysis of existing knowledge representation methods has shown that an ontological approach is well suited for this task. This paper introduces a machine-represented data storage reliability ontology with an expert health model as baseline data. Classes of the ontology include the key terms of the reliability domain. Stated requirements for data interpretation tools allow further processing of the ontology-based knowledge base. Described ontology-based diagnostic systems have shown their applicability in the case of data storage systems in the construction industry.

Identifying Principles for the Construction of an Ontology-Based Knowledge Base: A Case Study Approach.

BackgroundOntologies are key enabling technologies for the Semantic Web. The Web Ontology Language (OWL) is a semantic markup language for publishing and sharing ontologies.ObjectiveThe supply of customizable, computable, and formally represented molecular genetics information and health information, via electronic health record (EHR) interfaces, can play a critical role in achieving precision medicine. In this study, we used cystic fibrosis as an example to build an Ontology-based Knowledge Base prototype on Cystic Fibrobis (OntoKBCF) to supply such information via an EHR prototype. In addition, we elaborate on the construction and representation principles, approaches, applications, and representation challenges that we faced in the construction of OntoKBCF. The principles and approaches can be referenced and applied in constructing other ontology-based domain knowledge bases.MethodsFirst, we defined the scope of OntoKBCF according to possible clinical information needs about cystic fibrosis on both a molecular level and a clinical phenotype level. We then selected the knowledge sources to be represented in OntoKBCF. We utilized top-to-bottom content analysis and bottom-up construction to build OntoKBCF. Protégé-OWL was used to construct OntoKBCF. The construction principles included (1) to use existing basic terms as much as possible; (2) to use intersection and combination in representations; (3) to represent as many different types of facts as possible; and (4) to provide 2-5 examples for each type. HermiT 1.3.8.413 within Protégé-5.1.0 was used to check the consistency of OntoKBCF.ResultsOntoKBCF was constructed successfully, with the inclusion of 408 classes, 35 properties, and 113 equivalent classes. OntoKBCF includes both atomic concepts (such as amino acid) and complex concepts (such as “adolescent female cystic fibrosis patient”) and their descriptions. We demonstrated that OntoKBCF could make customizable molecular and health information available automatically and usable via an EHR prototype. The main challenges include the provision of a more comprehensive account of different patient groups as well as the representation of uncertain knowledge, ambiguous concepts, and negative statements and more complicated and detailed molecular mechanisms or pathway information about cystic fibrosis.ConclusionsAlthough cystic fibrosis is just one example, based on the current structure of OntoKBCF, it should be relatively straightforward to extend the prototype to cover different topics. Moreover, the principles underpinning its development could be reused for building alternative human monogenetic diseases knowledge bases.

Failure Detection and Prevention for Cyber-Physical Systems Using Ontology-Based Knowledge Base

Cyber-physical systems have emerged as a new engineering paradigm, which combine the cyber and physical world with comprehensive computational and analytical tools to solve complex tasks. In cyber-physical systems, components are developed to detect failures, prevent failures, or mitigate the failures of a system. Sensors gather real-time data as an input to the system for further processing. Therefore, the whole cyber-physical system depends on sensors to accomplish their tasks and the failure of one sensor may lead to the failure of the whole system. To address this issue, we present an approach that utilizes the Failure Modes, Effects, and Criticality Analysis, which is a prominent hazard analysis technique to increase the understanding of risk and failure prevention. In our approach, we transform the Failure Modes, Effects, and Criticality Analysis model into a UML(Unified Modeling Language) class diagram, and then a knowledge base is constructed based on the derived UML class diagram. Finally, the UML class diagram is used to build an ontology. The proposed approach employs a 5C architecture for smart industries for its systematic application. Lastly, we use a smart home case study to validate our approach.

Ontology as mapping of material world

This paper reviews currently available approaches and methods of automated population and enrichment of ontologies or ontology-based knowledge bases by structured data stored in various heterogeneous sources. Advantages and disadvantages of each approach are pointed out. The results of the analysis allow concluding that the existing methods are not effective enough to solve practical problems. A new method suggested does not have any of the specified disadvantages. The suggested method allows integrating data from different types of sources and considering the distributed nature of data and the necessity of authentication during accessing network resources. The method also suggests a solution to the problem of integrated data conflict resolution so that it will reduce the complexity of populating and enriching ontologies using the collected data arrays, irrespective of the format in which data are stored or represented.

ProbOnto: ontology and knowledge base of probability distributions.

Motivation: Probability distributions play a central role in mathematical and statistical modelling. The encoding, annotation and exchange of such models could be greatly simplified by a resource providing a common reference for the definition of probability distributions. Although some resources exist, no suitably detailed and complex ontology exists nor any database allowing programmatic access.Results: ProbOnto, is an ontology-based knowledge base of probability distributions, featuring more than 80 uni- and multivariate distributions with their defining functions, characteristics, relationships and re-parameterization formulas. It can be used for model annotation and facilitates the encoding of distribution-based models, related functions and quantities.Availability and Implementation: http://probonto.orgContact: mjswat@ebi.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

AR2SPARQL: An Arabic Natural Language Interface for the Semantic Web

With the growing interest in supporting the Arabic language on the Semantic Web (SW), there is an emerging need to enable Arab users to query ontologies and RDF stores without being challenged with the formal logic of the SW. In the domain of English language, several efforts provided Natural Language (NL) interfaces to enable ordinary users to query ontologies using NL queries. However, none of these efforts were designed to support the Arabic language which has different morphological and semantic structures. As a step towards supporting Arabic Question Answering (QA) on the SW, this work presents AR2SPARQL, a NL interface that takes questions expressed in Arabic and returns answers drawn from an ontology-based knowledge base. The core of AR2SPARQL is the approach we propose to translate Arabic questions into triples which are matched against RDF data to retrieve an answer. The system uses both linguistic and semantic features to resolve ambiguity when matching words to the ontology content. To overcome the limited support for Arabic Natural Language Processing (NLP), the system does not make intensive use of sophisticated linguistic methods. Instead, it relies more on the knowledge defined in the ontology and the grammar rules we define to capture the structures of Arabic questions and to construct an adequate RDF representations. AR2SPARQL has been tested with two different datasets and results have shown that it achieves a good retrieval performance in terms of precision and recall.

MorphoCol: An ontology-based knowledgebase for the characterisation of clinically significant bacterial colony morphologies

BackgroundOne of the major concerns of the biomedical community is the increasing prevalence of antimicrobial resistant microorganisms. Recent findings show that the diversification of colony morphology may be indicative of the expression of virulence factors and increased resistance to antibiotic therapeutics. To transform these findings, and upcoming results, into a valuable clinical decision making tool, colony morphology characterisation should be standardised. Notably, it is important to establish the minimum experimental information necessary to contextualise the environment that originated the colony morphology, and describe the main morphological features associated unambiguously. ResultsThis paper presents MorphoCol, a new ontology-based tool for the standardised, consistent and machine-interpretable description of the morphology of colonies formed by human pathogenic bacteria. The Colony Morphology Ontology (CMO) is the first controlled vocabulary addressing the specificities of the morphology of clinically significant bacteria, whereas the MorphoCol publicly Web-accessible knowledgebase is an end-user means to search and compare CMO annotated colony morphotypes. Its ultimate aim is to help correlate the morphological alterations manifested by colony-forming bacteria during infection with their response to the antimicrobial treatments administered. ConclusionsMorphoCol is the first tool to address bacterial colony morphotyping systematically and deliver a free of charge resource to the community. Hopefully, it may introduce interesting features of analysis on pathogenic behaviour and play a significant role in clinical decision making. Database URLhttp://morphocol.org.

Ontology-centered environmental information delivery for personalized decision support

Data on observed and forecasted environmental conditions, such as weather, air quality and pollen, are offered in a great variety in the web and serve as basis for decisions taken by a wide range of the population. However, the value of these data is limited because their quality varies largely and because the burden of their interpretation in the light of a specific context and in the light of the specific needs of a user is left to the user herself. To remove this burden from the user, we propose an environmental Decision Support System (DSS) model with an ontology-based knowledge base as its integrative core. The availability of an ontological knowledge representation allows us to encode in a uniform format all knowledge that is involved (environmental background knowledge, the characteristic features of the profile of the user, the formal description of the user request, measured or forecasted environmental data, etc.) and apply advanced reasoning techniques on it. The result is an advanced DSS that provides high quality environmental information for personalized decision support.

A semantic query-based approach for management decision-making

Decision-making is one of the critical activities of management in business. However, decision support systems that support management decision-making activities lack the semantics involved in responding to semantic queries involving reasoning. We consider an ontology-based knowledge base that covers linked data about organizations, people and activities in a supply chain. In this paper, we explore how to effectively answer semantic queries to support management decision-making, where custom rules are employed for answering qualitative queries. We explore the ontological data representation and a similarity measure for data integration. We present a hybrid reasoning algorithm for answering qualitative queries. This algorithm adapts the reasoner such that, when proving a goal, it does a simple retrieval when it encounters trusted items, and backward-chaining over untrusted items. We provide a case study and evaluate the hybrid reasoning algorithm on scalability, query processing time and support for management decision-making.

A multi-technique approach to bridge electronic case report form design and data standard adoption

Background and objectiveThe importance of data standards when integrating clinical research data has been recognized. The common data element (CDE) is a consensus-based data element for data harmonization and sharing between clinical researchers, it can support data standards adoption and mapping. However, the lack of a suitable methodology has become a barrier to data standard adoption. Our aim was to demonstrate an approach that allowed clinical researchers to design electronic case report forms (eCRFs) that complied with the data standard. MethodsWe used a multi-technique approach, including information retrieval, natural language processing and an ontology-based knowledgebase to facilitate data standard adoption using the eCRF design. The approach took research questions as query texts with the aim of retrieving and associating relevant CDEs with the research questions. ResultsThe approach was implemented using a CDE-based eCRF builder, which was evaluated using CDE- related questions from CRFs used in the Parkinson Disease Biomarker Program, as well as CDE-unrelated questions from a technique support website. Our approach had a precision of 0.84, a recall of 0.80, a F-measure of 0.82 and an error of 0.31. Using the 303 testing CDE-related questions, our approach responded and provided suggested CDEs for 88.8% (269/303) of the study questions with a 90.3% accuracy (243/269). The reason for any missed and failed responses was also analyzed. ConclusionThis study demonstrates an approach that helps to cross the barrier that inhibits data standard adoption in eCRF building and our evaluation reveals the approach has satisfactory performance. Our CDE-based form builder provides an alternative perspective regarding data standard compliant eCRF design.

On a Novel Cognitive Knowledge Base (CKB) for Cognitive Robots and Machine Learning

A cognitive knowledge base (CKB) is a novel structure of intelligent knowledge base that represents and manipulates knowledge as a dynamic concept network mimicking human knowledge processing. The essence of CKB is the denotational mathematical model of formal concept that is dynamically associated to other concepts in a CKB beyond conventional rule-based or ontology-based knowledge bases. This paper presents a formal CKB and autonomous knowledge manipulation system based on recent advances in neuroinformatics, concept algebra, semantic algebra, and cognitive computing. An item knowledge in CKB is represented by a formal concept, while the entire knowledge base is embodied by a dynamic concept network. The CKB system is manipulated by algorithms of knowledge acquisition and retrieval on the basis of concept algebra. CKB serves as a kernel of cognitive learning engines for cognitive robots and machine learning systems. CKB plays a central role not only in explaining the mechanisms of human knowledge acquisition and learning, but also in the development of cognitive robots, cognitive learning engines, and knowledge-based systems.