Fuzzy Ontology Research Articles

A fuzzy ontology-based case-based reasoning system for stomach dystemperament in Persian medicine.

In Persian medicine, early diagnosis and treatment of stomach dystemperament is crucial for preventing other diseases. However, traditional medicine diagnosis often involves ambiguous and less structured information making it challenging for practitioners. Integrating fuzzy ontology with case-based reasoning (CBR) systems can enhance diagnostic accuracy in this filed. This study aimed to develop and evaluate a fuzzy ontology-based CBR system for diagnosing and treating stomach dystemperament in Persian medicine. This was a mixed-methods research in which a fuzzy ontology-based CBR system was developed based on the fuzzy features, utilizing trapezoidal, triangular, right shoulder and left shoulder membership functions to represent linguistic variables such as hunger level and digestion power. The research phases included identifying relevant terms, concepts, and relationships, developing the fuzzy case-base ontology using the IKARUS-Onto methodology, and subsequently designing and implementing the CBR system. The system performance was evaluated in terms of its sensitivity, specificity, accuracy, precision, and F1-score. Initially, a case-base fuzzy ontology was created. Then, the database was built up using 88 expert-validated medical records. Of these cases, 72% (63 cases) were diagnosed with phlegmatic dystemperament, 18% (16 cases) with cold-dry dystemperament, and 10% (9 cases) had no stomach dystemperament. The CBR system was developed and evaluated using sensitivity, specificity, accuracy, precision, and F1-score which were 97.5%, 87.5%, 96.6%, 98.7%, and 98.1%, respectively. Our fuzzy ontology-based CBR demonstrated high performance in diagnosing stomach dystemperament in Persian medicine. This system shows promise in improving diagnostic accuracy and facilitating the identification of similar cases. While initial results are encouraging, further evaluation in a real clinical environment is recommended to fully assess its practical utility.

Utilization of Fuzzy Ontology for the Meaning of Homonymous and Homophones Ambiguous Sentences

The ambiguous sentences Homonyms and Homophones become a big problem when processed by computers. From these problems, a Novelty was found; the Novelty created a system that was able to recognize ambiguous sentences of Homonyms and Homophones. The process that the system runs for the first time is to test the proximity of the ambiguous sentences entered with the data set; from this process, the ambiguous sentences entered can already be recognized as the meaning of the sentence. The resulting result is how many per cent the level of similarity. Then the results are processed with the fuzzy ontology method. The results of the Fuzzy Ontology are low similarity level, moderate similarity level, and high similarity level. The method used to analyze this research is the confusion matrix, the precision results obtained were 92%, recall was 100%, and accuracy was 96%. In the future, this research can be used to refine translation results in a translation system.

Fuzzy ontology-based approach for liver fibrosis diagnosis

The domain of the digestive system is prone to severe chronic disease in the form of liver cirrhosis, which is currently a leading cause of mortality. This article presents a new intelligent system for predicting the severity of liver fibrosis in patients with chronic viral hepatitis C. The proposed system is based on the inference capabilities of fuzzy ontology and operates on semantic rule-based techniques. A fuzzy decision tree technique was employed to generate the ontology rule base using a dataset of real fibrosis cases from the Mansoura University Hospital, Egypt. These rules were then encoded into a set of fuzzy semantic rules using the fuzzy description logic format. To evaluate the system’s effectiveness, the proposed ontology was then tested on 47 chronic HCV cases, with an attempt made to see if this correctly diagnosed the patients’ conditions. The performance of the proposed system was compared with that of the now-standard Mamdani fuzzy inference system; while the latter achieved an accuracy of 95.7/%, the proposed fuzzy ontology-based system demonstrated higher performance, with 97.8% accuracy. Furthermore, the proposed system also supports semantic interoperability between clinical decision support systems and electronic health record ecosystems. The positive impacts of this system on the correct prediction of liver fibrosis severity thus suggest that it has the potential to assist medical professionals in diagnosing and treating this dangerous disease.

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback.

A Hybrid Artificial Intelligence-Based System for Supporting Eco-Industrialization of Complex Manufacturing Processes

Since the industrialization phase is one of the main phases of the product development cycle, an original hybrid flexible automated system is developed in this paper in order to support production eco-processes designers’ decision making. It is based on three different artificial intelligence tools, namely the fuzzy Ontologies, the cases based reasoning and the rules based reasoning, which have been integrated in one system. Actually, the proposed system is composed of different modules that are well described in details thereafter. In the end of this paper, a case of study is presented in order to illustrate the efficacy of the developed intelligent system.

Fuzzy ontology datatype learning using Datil

Semantic Web technologies (especially, ontologies) are very important nowadays in intelligent applications to represent the important knowledge in an application domain. Shortly after the birth of ontologies, the inability to adequately handle knowledge affected by imprecision or vagueness, which is inherent to many real-world scenarios, was observed. Fuzzy ontologies were proposed and developed as a solution. However, the problem of building fuzzy ontologies has received little attention in the literature. In fact, there are few examples of publicly available fuzzy ontologies, suggesting that new techniques helping users to build fuzzy ontologies are needed.In this paper, we develop advanced strategies to learn fuzzy datatypes for fuzzy ontologies. Our approach uses real-world data and unsupervised clustering algorithms to compute the fuzzy datatype definitions in an automatic way. Our learning algorithm is implemented in the Datil system, a tool supporting different input formats and devices, including a mobile version. Our approach could be orthogonal to other existing approaches learning fuzzy subclass axioms.

Research on knowledge representation and modeling of health management based on fuzzy ontology

The long-term maintenance of good condition for equipment is the basis of carrying out combat missions under the high technology and fast pace of modern war. However, the knowledge in the health management field at present has the characteristics of distribution, multi-source, heterogeneity and uncertainty, which seriously affects the efficiency of knowledge sharing and reuse. In order to improve the utilization of health management knowledge, an ontology-based knowledge representation method is proposed to describe knowledge in a unified and standardized way, and the classical ontology is extended to express the uncertain knowledge in the field of health management. In addition, to improve the maintenance and knowledge updating efficiency, a global ontology model and a hierarchy, time and activity (HTA) ontology model are constructed. This paper takes the guidance subsystem of a missile as an example to illustrate the process of knowledge modeling. The results show this method realizes knowledge sharing in the health management field and can provide decision support for health management of equipment.

Can Semantics Uncover Hidden Relations between Neurodegenerative Diseases and Artistic Behaviors?

Semantics play a crucial role in organizing domain knowledge, schematizing it, and modeling it into classes of objects and relationships between them. Knowledge graphs (KGs) use semantic models to integrate and represent different types of data. This study aimed to systematically review related work on the topics of ontologies for neurodegenerative diseases (NDs), ontology-based expert systems for NDs, and the artistic behavior of ND patients. The utilization of ontologies allows for a more comprehensive understanding of the progression and etiology of NDs, the structure and function of the brain, and the artistic expression associated with these diseases. The data collected from ND patients highlights the presence of cases where artistic expression can be linked to the disease. By developing fuzzy ontologies for NDs and incorporating them into expert systems, early detection and monitoring can be supported. Through our systematic review, we identify and discuss open issues and challenges in understanding the relationship between ND patients and their artistic behavior. We also conclude that ontology-based expert systems hold immense potential in uncovering hidden correlations between these two. Further research in this area has the potential to address key research questions and provide deeper insights.

Integration of Fuzzy Ontologies and Neural Networks in the Detection of Time Series Anomalies

This paper explores an approach to solving the problem of detecting time series anomalies, taking into account the specifics of the subject area. We propose a method based on the integration of a neural network with long short-term memory (LSTM) and Fuzzy OWL (Fuzzy Web Ontology Language) ontology. A LSTM network is used for the mathematical search for anomalies in the first stage. The fuzzy ontology filters the detection results and draws an inference for decision making in the second stage. The ontology contains a formalized representation of objects in the subject area and inference rules that select only those anomaly values that correspond to this subject area. In the article, we propose the architecture of a software system that implements this approach. Computational experiments were carried out on free data of technical characteristics of drilling rigs. The experiments showed high efficiency, but not the maximum efficiency of the proposed approach. In the future, we plan to select a more efficient neural network architecture for mathematical anomaly detection. We also plan to develop an algorithm for automatically filling the rules of inference into the ontology when analyzing text sources.

A semantic-based approach for hepatitis C virus prediction and diagnosis using a fuzzy ontology and a fuzzy Bayesian network

Medical decisions, especially when diagnosing Hepatitis C, are challenging to make as they often have to be based on uncertain and fuzzy information. In most cases, that puts doctors in complex yet uncertain decision-making situations. Therefore, it would be more suitable for doctors to use a semantically intelligent system that mimics the doctor’s thinking and enables fast Hepatitis C diagnosis. Fuzzy ontologies have been used to remedy the shortcomings of classical ontologies by using fuzzy logic, which allows dealing with fuzzy knowledge in ontologies. Moreover, Fuzzy Bayesian networks are well-known and widely used to represent and analyze uncertain medical data. This paper presents a system that combines fuzzy ontologies and Bayesian networks to diagnose Hepatitis C. The system uses a fuzzy ontology to represent sequences of uncertain and fuzzy data about patients and some features relevant to Hepatitis C diagnosis, enabling more reusable and interpretable datasets. In addition, we propose a novel semantic diagnosis process based on a fuzzy Bayesian network as an inference engine. We conducted an experimental study on 615 real cases to validate the proposed system. The experimentation allowed us to compare the results of existing machine learning algorithms for the Hepatitis C diagnosis with the results of our proposed system. Our solution shows promising results and proves effective for fast medical assistance.

COVID-19 Patients’ Health Monitoring System using Fuzzy Ontology and Internet of Things

COVID-19 is a very dangerous pandemic attacking the respiratory organs of humans. It ischaracterized by its contagious speed, especially with its last versions. Effectiveness of confrontation resides in astrategy based on the speed of intervention, early detection, and appropriate and quick treatment. However, thisstrategy requires more effort and enormous human, material, and financial resources. The latter situation requiresa much more efficient solution based on using new technologies. Through the integration of the internet of thingsin healthcare, the quality of the latter will be improved. This integration requires suitable architecture representthe foundation for the system that handles the data generated by this technology. To concretize this strategy, wepropose an approach based on IoT architecture inspired by the organization of the Algerian health structure forfighting COVID-19. The architecture allows the organization to manage resources and ensure adequate resources.The approach also consists of a semantic web-based system to handle the heterogeneity of data sources and exchangeit with different applications. The system is based on a proposed fuzzy ontology that helps to treat vague and imprecisedata that characterize the medical domain. The fuzzy ontology is developed by reusing a standard IoT ontology forallowing the sharing and reuse of IoT data. It also uses the NEWS2 score system for defining membership functions.We conducted an experimental study, and the results of the proposed approach were compared with those obtainedby physician assessment. The results show that the approach is effective, and even if COVID-19 disappears soonaccording to the World Health Organization indications, the proposed approach will still be valid for any otherepidemic that may occur in the future or for any other disease

Reviving stagnated debates in Group Decision Making environments with high number of alternatives

In group decision-making, experts try to obtain a consensus to determine how to order a series of alternatives. A consensus is a decision that reflects the opinions of every group member. Consensus requires discussion and deliberation between the group members. During the process, it is normal that the discussion process to stagnate. In this article, we present a new method of group decision-making that solves the stagnation of the process by including new information. The timing of this is determined by a process stagnation analysis. Fuzzy Ontologies allow experts to work in environments with large numbers of alternatives. The method takes into account the experts’ rankings of alternatives to determine which criteria the experts seem to like the most. When introducing new information into the debate, experts are given the possibility to explore alternatives that are very different from those already seen or to look for alternatives that meet the criteria that are preferred the most.

Sugeno-Type Fuzzy Ontology PI Controller for Proportional Electrohydraulic System

In this paper, an ontology representation of the Sougeno-type PI Fuzzy Logic Controller (FLC) is presented. The proposed controller is an extension of the Fuzzy Ontology Controller (FOC), where the new fuzzy annotations relevant to Sougeno-type fuzzy control are added to the ontology. The design of the fuzzy ontology is carried out in Protégé and it is hosted on a remote server in the Virtuoso database. Simulation results for the proportional electrohydraulic system are presented and compared to the PI and the SMC controller. The simulation results show that the proposed concept works, and achieves similar results to the PI controller. Experimental results are carried out on a proportional electrohydraulic system. Results show good tracking capabilities despite slow network communication between the server and experimental setup.

Introducing disruption on stagnated Group Decision Making processes using Fuzzy Ontologies

In Group Decision Making processes, experts debate about how to rank a set of alternatives. It is usual that, at a certain point of the discussion, the debate gets stuck. In this paper, a novel Group Decision Making method for environments with a high number of alternatives is presented. Fuzzy Ontologies are used in order to represent the alternatives and their characteristics. Moreover, a novel stagnation analysis is used in order to determine if the debate gets stuck. If it does, the method modifies the alternatives set in order to introduce new options and remove the least popular ones. This way, the debate can revive since that the new alternatives provide different points of view. The presented method helps experts to conduct long and thorough debates in order for them to be able to make effective and reliable decisions.

Using type-2 fuzzy ontology to improve semantic interoperability for healthcare and diagnosis of depression

Ontology enhances semantic interoperability through integrating health data from heterogeneous sources and sharing information in a meaningful way. In the field of smart health services, semantic interoperability means the exchange and interpretation of data without ambiguity and uncertainty. However, existing classical ontologies are not able to represent vague and uncertain knowledge, especially in contexts of mental health disorders which are associated with varying degrees of uncertainty and inaccuracy of diagnosis, and in this case, the treatment is a complex and common mental process necessitating to share information accurately and unambiguously. Type-2 fuzzy set theory can offer a fruitful solution in order to control uncertainty or express ambiguous concepts in a dynamic and complex environment such as healthcare systems. Herein, a semantic framework for healthcare, and also monitoring mental health disorders using type-2 fuzzy set theory based on the Internet of Thing (IoT) is suggested, in which all depression-related concepts are semantically annotated to share detailed information with the treatment staff. This framework not only paved the way to increasing the accuracy of medical diagnosis and decision-making but also provides the possibility of inference and semantic reasoning using the languages of SPARQL query and DL query.

An interval type‐2 fuzzy ontological model: Predicting water quality from sensory data

SUMMARYWith the advent of break‐through sensing technology, performing data capturing and analysis for knowledge engineering has become more opportunistic. The task of efficiently analyzing sensor based data for effective decision making poses a significant challenge. Conventional prediction and recommender systems lack comprehensive analysis of all parameters and aspects, thus compromising prediction results. At the decision‐making level, traditional knowledge driven prediction systems deploy classical ontology for knowledge representation and analysis. However, classical ontologies are not considered as powerful tools due to their inability to handle vagueness in data for real‐world applications. On the contrary, fuzzy ontology deals with the issue of hazy and uncertain data for effective analysis to give promising results. This work presents interval type 2 fuzzy ontological knowledge model that predicts water quality of sensor based water samples and providing solutions with respect to the corresponding quality state. The proposed knowledge model constitutes of two newly developed ontologies: water sensor observations ontology (crisp ontology to model sensor observational data) and water quality ontology (interval type 2 fuzzy ontology for modeling the water quality prediction process). The inference mechanism is based on interval type‐2 fuzzy partitioning and computation. Besides water quality prediction and providing solutions, the proposed model handles the issue of interoperability and exchange of consensual knowledge among multiple disciplines. The proposed knowledge model is validated with real‐life water sensor based parameterized data captured from various geographically dispersed monitoring stations with approximately 50,000 samples at each station.

Product online review analysis using fuzzy ontology

AbstractThis paper analyses the online text comments in the online trading platform and identifies the abnormal and contradictory comments. Firstly, the abnormal contradiction detection and analysis model of online text reviews is constructed based on fuzzy ontology to identify the abnormal and contradictory online text reviews. Secondly, the fuzzy emotional ontology and emotional tendency analysis method identify abnormal and contradictory online text reviews. The online text review anomaly detection and analysis model constructed enriches the process and method of online review anomaly identification.

A Rule-Based Heuristic Methodology for Su-Field Analysis in Industrial Engineering Design

Industrial engineering design is a crucial issue in manufacturing. To meet the competitive global market, manufacturers are continuously seeking solutions to design industrial products and systems inventively. Su-Field analysis, which is one of the TRIZ analysis tools for inventive design problems, has been used to effectively improve the performance of industrial systems. However, the inventive standards used for engineering design are summarized and classified according to a large number of patents in different fields. They are built on a highly abstract basis and are independent of specific application fields, making their use require much more technical knowledge than other TRIZ tools. To facilitate the use of invention standards, in particular to capture the uncertainty or imprecision described in the standards, this paper proposes a rule-based heuristic approach. First, Su-Field analysis ontology and fuzzy analysis ontology are constructed to represent precise and fuzzy knowledge in the process of solving inventive problems respectively. Then, SWRL (Semantic Web Rule Language) reasoning and fuzzy reasoning are executed to generate heuristic conceptual solutions. Finally, we develop a software prototype and elaborate the resolution of “Auguste Piccard’s Stratostat ” in the prototype.

Type-2 fuzzy ontology-based semantic knowledge for indoor air quality assessment

Semantic web technology plays an increasing role in performing smart home applied programs and it has led to improve semantic interoperability among different systems. However, classical ontologies fail to illustrate ambiguous, incomplete, and uncertain knowledge often available in the real world. On the other hand, the air quality assessment carried out to determine “the degree of pollution” lacks accurately specified boundaries; therefore, the conventional approach based on classic ontology cannot extract real-valued memberships and consequently fails to support ambiguous, incomplete, and uncertain knowledge. Integrating semantic web of things technology (SWOT) and type-2 fuzzy logic improves the capability of semantic reasoning to retrieve query information. Annotation of sensor-generated data and the ability to infer and represent knowledge based on type-2 fuzzy logic are extremely essential when the available data are ambiguous and uncertain. Hence, in this paper, we have provided a framework to build an IoT-based home air quality assessment system by using type-2 fuzzy ontology so that smart home systems can make a decision and control appropriately based on predefined rules by employing the provided semantic reasoning.

Fuzzy ontology as a basis for recommendation Systems for Traveler’s preference

Fuzzy ontology as a basis for recommendation Systems for Traveler’s preference