Knowledge Representation Method Research Articles

Kernel similarity-based multigranulation three-way decision approach to hypertension risk assessment with multi-source and multi-level structure data

Early intervention and prevention of chronic diseases is a fundamental way to reduce disease incidence. Due to the complex pathogenic factors of disease, the assessment of disease risk requires consideration of the influence of multiple factors. In clinical practice, disease risk assessment may face data with multi-source, multi-type and multiple indicators of examination items. Mining useful information and rules in this complex information is the foundation for further decisions. However, the existing knowledge representation methods cannot comprehensively and accurately describe these characteristic of data. Therefore, we introduce the concept of multiple hybrid attribute information systems (MHAISs). Then, we defined the binary relations over MHAIS by fusing different types of kernel functions. On this basis, we construct a variable precision multigranulation kernel rough set (VPMGKRS), and propose a multigranulation three-way decision method over MHAIS. In addition, considering the individual difference between decision objects and the diversity of data characteristic among sources, we calculate the loss functions under different granular with the help of conditional probabilities, and then obtain the thresholds for the three-way decisions. Finally, we use 712 clinical random samples to conduct a simulation analysis applying the proposed method to assess the risk of hypertension. The experiment result confirmed the applicability and validity of the model.

Kernel Fisher Dictionary Transfer Learning

Dictionary learning is an efficient knowledge representation method that can learn the essential features of data. Traditional dictionary learning methods are difficult to obtain nonlinear information when processing large-scale and high-dimensional datasets. While most dictionary learning algorithms are based on the assumption that the training data and test data have the same feature distribution, which is not always true in practical applications. To address the above problems, we propose the Kernel Fisher Dictionary Transfer Learning (KFDTL) algorithm. First, we map each sample to high-dimensional space through kernel mapping and use any dictionary learning algorithm to learn the essential features. Then, the feature-based transfer learning method is performed to predict the labels of the target samples. This method includes three main contributions: (1) KFDTL constructs a discriminative Fisher embedding model to make the same class samples have similar coding coefficients; (2) Based on the relationship between profiles and atoms, KFDTL constructs an adaptive model that adapts source domain samples to target domain samples; (3) The kernel method is used to efficiently solve nonlinear problems. Experiments on a large number of public image datasets have proved the effectiveness of the proposed method. The source code of the proposed method is available at https://github.com/zzfan3/KFDTL .

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.

A confidence-aware and path-enhanced convolutional neural network embedding framework on noisy knowledge graph

In recent years, Knowledge Graphs (KGs) have been widely used in applications such as search engines and Q&A systems. During construction, errors were inevitably introduced and KGs may contain many incorrect facts. However, most KG representation learning methods assume that all triple facts are correct, making KG noise detection a challenge. Although some knowledge representation methods are based on a confidence-aware framework, they are usually limited by the translation assumption in TransE and confidence calculation methods. To address the above problems, we propose a more accurate and effective confidence-aware and path-enhanced convolutional neural network knowledge embedding framework (CPConvKE). It makes use of structural, entity type, and rule information for knowledge graph noise detection and knowledge representation learning simultaneously. Specifically, for representation learning, we introduce a gating-based path embedding method to filter out noise while learning entity and relation embeddings. For the confidence evaluation, we propose a triple confidence estimator that uses entity type and rule information as prior probability and defines posterior probability based on the embedding. We evaluate the effectiveness of our model on the knowledge graph noise detection, completion, and triple classification tasks. The experimental results show that our confidence-aware model achieves significant and consistent improvements on all tasks, which confirms the ability of CPConvKE to detect noise and learn clean embeddings in a noisy knowledge graph.

A semantic data-driven knowledge base construction method to assist designers in design inspiration based on traditional motifs

Directing at the problem of inaccurate expression of tacit knowledge and low heuristic of retrieval results in the design inspiration drawing process of designers based on traditional motifs, a semantic data representation method of traditional motifs knowledge is put forward, and an effective knowledge base construction and knowledge retrieval method for traditional motifs are described. First, the connotation of traditional motifs is deeply explored based on the theory of symbol semantics, realizing the interpretation and transfer of the semantic features in traditional motifs. Second, shape grammar is adopted for fractal traditional motifs, the hierarchical structure of traditional motifs is built, and the parametric coding of semantic features of traditional motifs is completed, providing the semantic data representation method of traditional motifs knowledge. Third, based on the clustering algorithm, the construction method of the traditional motifs knowledge base is explained, and a traditional motifs knowledge retrieval method taking into account the user's perceptual preference is proposed. Finally, setting the traditional Chinese motifs as an example, with a tea set as the designed product, the construction process of a small traditional motif knowledge base is shown in detail, proving the feasibility of this paper’s theory. Compared with the traditional theory, the semantic data representation method of traditional pattern knowledge is greatly advantageous in semantic differences elimination and computational efficiency.

A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems

Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role in guiding power equipment operation and decision-making. Due to the complex and weak relations from multi-source heterogeneous data, the use of KGs has become popular in research to represent potential information in power knowledge reasoning. In this review, we first summarize the key technologies of knowledge graph representation and learning. Then, based on the complexity and real-time changes of power system operation and maintenance, we present multiple data processing, knowledge representation learning, and the graph construction process. In three typical power operation and fault decision application scenarios, we investigate current algorithms in power KG acquisition, representation embedding, and knowledge completion to illustrate accurate and exhaustive recommendations. Thus, using KGs to provide reference solutions and decision guidance has a significant role in improving the efficiency of power system operations. Finally, we summarize the achievements and difficulties of current research and give an outlook for future, promising roles of KG in power systems.

Онтологический подход к разработке единой базы знаний мультимодальных перевозок

Purpose: In the frames of ongoing digital transformation of transport complex in a whole and railway transport in particular, to give brief analytical characterization of being developed digital platforms and services. To consider promising technologies that are either at launch stage or on the way to the peak of overpriced expectations as to technological hype cycle of Gartner Research Company. To give an example of unified knowledge base as a basis of digital platform for multimodal transportation. Methods: Methods for visual structuring of information were used, in particular, mental maps (intellect-maps). To represent subject area ontology, a semantic network was used as a method for knowledge representation. Results: The pursued analysis of being developed and existing digital platforms and services has shown that besides data, there should lie at their basis more complex informational units — an active knowledge. This requires the method of data integration from disparate sources, their integration and circulation as within one industry as well as between various industries in order of knowledge generation and spread. In view of work specifics of transport various kinds, participating in multimodal transportations, the subject area, uniting all kinds of transport, should be chosen for this. The application of "cargo transportation" subject area has been justified by virtue of which, it is necessary to create management system of cargo flows on railway transport. Ontology fragment, describing "cargo transportation" domain for railway transport, has been worked out. Practical significance: The necessity of applying active knowledge to create autonomous intellectual productions on transport is shown. The use of knowledge bases, established on ontologies, will allow to raise the level of interaction of transport various kinds in "narrow" places during cargo transshipment as well as to expand the range of services, provided to customers.

Automating Safety and Security Co-design through Semantically Rich Architecture Patterns

During the design of safety-critical systems, safety and security engineers make use of architecture patterns, such as Watchdog and Firewall, to address identified failures and threats. Often, however, the deployment of safety architecture patterns has consequences on security; e.g., the deployment of a safety architecture pattern may lead to new threats. The other way around may also be possible; i.e., the deployment of a security architecture pattern may lead to new failures. Safety and security co-design is, therefore, required to understand such consequences and tradeoffs in order to reach appropriate system designs. Currently, architecture pattern descriptions, including their consequences, are described using natural language. Therefore, their deployment in system design is carried out manually by experts and thus is time-consuming and prone to human error, especially given the high system complexity. We propose the use of semantically rich architecture patterns to enable automated support for safety and security co-design by using Knowledge Representation and Reasoning (KRR) methods. Based on our domain-specific language, we specify reasoning principles as logic specifications written as answer-set programs. KRR engines enable the automation of safety and security co-engineering activities, including the automated recommendation of which architecture patterns can address failures or threats, and consequences of deploying such patterns. We demonstrate our approach on an example taken from the ISO 21434 standard.

A fuzzy set theory-based fast fault diagnosis approach for rotators of induction motors.

Induction motors have been widely used in industry, agriculture, transportation, national defense engineering, etc. Defects of the motors will not only cause the abnormal operation of production equipment but also cause the motor to run in a state of low energy efficiency before evolving into a fault shutdown. The former may lead to the suspension of the production process, while the latter may lead to additional energy loss. This paper studies a fuzzy rule-based expert system for this purpose and focuses on the analysis of many knowledge representation methods and reasoning techniques. The rotator fault of induction motors is analyzed and diagnosed by using this knowledge, and the diagnosis result is displayed. The simulation model can effectively simulate the broken rotator fault by changing the resistance value of the equivalent rotor winding. And the influence of the broken rotor bar fault on the motors is described, which provides a basis for the fault characteristics analysis. The simulation results show that the proposed method can realize fast fault diagnosis for rotators of induction motors.

Dynamic knowledge modeling and fusion method for custom apparel production process based on knowledge graph

Compared with ordinary mass-produced apparel products, custom apparel products will generate more data at each stage of their life cycle. Such data is in a highly dynamic state, while the relationship between the data is more complex. However, the current use of traditional relational data to store the whole life cycle data of custom apparel products has several problems of high redundancy, weak correlation, discrete distribution, and certain limitation of storage capacity. Therefore, based on knowledge graph, a dynamic knowledge modeling and fusion method is proposed for the production process of custom apparel. Firstly, an ontology-based knowledge modeling method is designed for custom apparel, which defined three types of ontology modeling methods for the process, resources, and features. On this basis, a knowledge graph construction method based on bi-directional fusion for the custom apparel production system is proposed. With one order as a unit, a knowledge graph facet (KGF) model, as well as the derived knowledge representation, generation and fusion method, is established to realize dynamic knowledge fusion of the custom apparel production process. Finally, taking the suit production process of a custom apparel factory as an example, the corresponding knowledge graph is constructed based on the ontology knowledge model, and the effectiveness of the proposed knowledge fusion method is verified.

PKC– Production Knowledge Chart for Knowledge Transfer

In the design of production processes in the manufacturing industry, it is necessary to transfer the defect investigation knowledge, which is the empirical knowledge of experienced workers, to beginners. Leonard and Swap pointed out the importance of acquiring empirical knowledge through experimental learning. However, they have not clarified the explicit empirical knowledge representation method and the experimental learning method. If the knowledge cannot be represented, empirical knowledge remains tacit, and the knowledge transfer from experts to beginners is not only individual and inefficient, but also difficult to reuse.In this paper, we propose a production knowledge chart (PKC) that expresses the production process to acquire the empirical knowledge necessary for investigating defects in the production process. In addition, we clarify the effectiveness of the proposed method by expressing a general production process with PKC. In addition, we will discuss future research.

Application and evaluation of sentence embedding and clustering methods in the context of concept hierarchy construction

Concept hierarchies, as part of knowledge representation methods, play an important role in supporting the exchange and sharing of information. We developed and automated a concept hierarchies construction process which includes several artificial intelligence techniques. When automating their construction from an existing, more or less structured, body of knowledge, the evaluation of the resulting concept hierarchy is an important step.We propose an approach for concept hierarchy construction (CHC) from short sentences, that makes use of methods like Sentence Embedding, Clustering, and Automatic Labeling to create a hierarchical representation consisting of three layers. Our major focus in this paper is not on the algorithms used but on their evaluation using manual clustering by experts and fuzzy sets.

РАЗРАБОТКА И АПРОБАЦИЯ ПРОТОТИПА ЭКСПЕРТНОЙ СИСТЕМЫ ДЛЯ ДИАГНОСТИКИ ПОДАГРЫ

Significance. Gout is a rheumatological disease that affects up to 2% of the population of the Russian Federation according to various sources. The pathogenesis of the disease is associated with a disturbance in uric acid metabolism and manifests itself in the deposition of monosodium urate crystals in various tissues of the body. Timely diagnosis and treatment of the disease can prevent the development of gout complications and disability of the patient, however, according to various sources, it takes from 44 to 8 years to diagnose gout. One possible way to improve the quality of gout diagnosis is to develop and implement an expert system for preliminary diagnosis, diagnostic techniques and consultations, and final diagnosis. The purpose of the study is to develop and test a prototype expert system for gout diagnosis. Material and methods. The study used system analysis methods, including decomposition, analysis and synthesis methods, knowledge engineering methods, including knowledge representation methods (frames, semantic networks), ontological approach, fuzzy logic, hierarchical structuring methods and effective evaluation methods, including ROC analysis. Results. A prototype expert system for gout diagnosis has been developed. The prototype consists of a knowledge base, a logical solver, and an explanation subsystem. A specialized knowledge base editor was created for the development and subsequent maintenance of the knowledge base. The knowledge base was filled using 2 information objects jointly developed with a group of experts: the nomenclature of medical concepts (495 basic and 679 synonymous concepts) and logical schemes (40 schemes) related to gout diagnosis. The structure of the knowledge base is a combination of frames and semantic networks and includes 18 objects (7 types of concepts and 11 types of relationships). A graph database is used to store the knowledge base that determines the logic of the logical solver and the explanation subsystem. The logical solver analyzes the patient's clinical data and searches the optimal path, the end points of which are the recommendations of the expert system prototype. The explanation subsystem uses this path to argue the selected solution. The prototype expert system was tested on two depersonalized samples containing 1725 patients provided by the Tyumen Healthcare Department. Conclusion. A prototype expert system has been developed to determine the preliminary diagnosis of gout, prescribe diagnostic techniques and consultations for its verification, and make a final diagnosis. The prototype includes 1174 concepts and 5,640,522 relationships. Testing of the prototype expert system with ROC-analysis showed that its sensitivity was 91.3% CI [89.1%; 93.1%], specificity was 85.4% CI [82.9%; 87.6%], and AUROC was 0.954 CI [0.944; 0.963]. Scope of application. The research results can be used to integrate the prototype expert system into the doctor’s automated workplace of a medical information system in a medical organization.

Solving Ontology Metamatching Problem through Improved Multiobjective Particle Swarm Optimization Algorithm

In recent years, knowledge representation in the Artificial Intelligence (AI) domain is able to help people understand the semantics of data and improve the interoperability between diverse knowledge-based applications. Semantic Web (SW), as one of the methods of knowledge representation, is the new generation of World Wide Web (WWW), which integrates AI with web techniques and dedicates to implementing the automatic cooperations among different intelligent applications. Ontology, as an information exchange model that defines concepts and formally describes the relationships between two concepts, is the core technique of SW, implementing semantic information sharing and data interoperability in the Internet of Things (IoT) domain. However, the heterogeneity issue hampers the communications among different ontologies and stops the cooperations among ontology-based intelligent applications. To solve this problem, it is vital to establish semantic relationships between heterogeneous ontologies, which is the so-called ontology matching. Ontology metamatching problem is commonly a complex optimization problem with many local optima. To this end, the ontology metamatching problem is defined as a multiobjective optimization model in this work, and a multiobjective particle swarm optimization (MOPSO) with diversity enhancing (DE) (MOPSO-DE) strategy is proposed to better trade off the convergence and diversity of the population. The well-known benchmark of the Ontology Alignment Evaluation Initiative (OAEI) is used in the experiment to test MOPSO-DE’s performance. Experimental results prove that MOPSO-DE can obtain the high-quality alignment and reduce the MOPSO’s memory consumption.

Mobile Application Based Expert System for Cattle Disease Diagnosis and Treatment in Afan Oromo Language

Ethiopia is best known for its cattle population in Africa. Nonetheless, cattle diseases is the major constraint to cattle production and its safe utilization. For the developing countries, cattle disease creates a considerable effect on the livelihood and lives of the farmers. In this research work we have developed a mobile application based expert system in afan oromo language for diagnosing and treating cattle diseases. Knowledge Base regarding the various diseases affecting cattle’s in the study area was acquired by using both secondary and primary sources. The primary knowledge source was collected from West Guji and Bornea zonal area domain experts and the secondary sources of knowledge was collected using document analysis. Purposive sampling technique was used for selecting domain experts for knowledge acquisition process. The proposed expert system was developed using rule based knowledge representation approach and using decision tree knowledge modeling technique. After knowledge is acquired it was represented using rule based knowledge representation method using Java Programming language and a user interface was designed and developed using XML that fits into the present day smart phones and the application will be cross platform. User acceptance testing and overall system performance testing was made after the prototype was developed. Accordingly, the mobile application demonstrated 81% of accuracy and among the users who were made a user experience evaluation, 84% of them got satisfied by the performance of the mobile application.

Development of knowledge base structure for gout diagnosis

Gout belongs to the group of microcrystalline arthritides and is a systemic tophus manifested by inflammation of various tissues caused by deposition of monosodium urate crystals. Verification of gout diagnosis in Russia takes on average four to eight years from the beginning of the disease. This period is sufficient for the development of various complications (for example, gouty nephropathy) and disability of patient, and, therefore, it is necessary to revise the existing strategy for the diagnosis of this disease. One of the options for improving the provision of medical care can be attributed to the creation of a clinical decision support system (CDSS), which is based on the knowledge of experts, formalized as a knowledge base (KB).Aim. To develop a KB structure for CDSS for gout diagnosis.Material and Methods. Clinical information for gout diagnosis, presented as a nomenclature of medical concepts and logical schemes, which were collected on the basis of federal clinical recommendations, various literature sources and expert knowledge were used as materials. The ontological method was used as a method of knowledge structuring. Combined semantic network and frames were used as the methods of representation.Results. While developing KB structure, a combination of two methods of knowledge representation including semantic network and frames was used. An ontological approach was used in terms of knowledge structuring. The structure was built on the clinical knowledge collected in cooperation with experts in gout diagnosis. Compared with similar developments of diagnosing diseases based on knowledge engineering methods, the main feature of developed KB structure was the use of a separate type “Syndrome” concept as an aggregator accepted in medicine, significantly reducing the volume of KB for diseases.Conclusion. The KB structure was developed comprising the use of seven types of concepts and 11 types of relationships. The structure involved the use of ontological approach and combination of two models of knowledge representation, namely: a semantic network and a frame model.

Semantic Knowledge Management and Blockchain-based Privacy for Internet of Things Applications

Design of distributed complex systems raises several important challenges, such as: confidentiality, data authentication and integrity, semantic contextual knowledge sharing, as well as common and intelligible understanding of the environment. Among the many challenges are semantic heterogeneity that occurs during dynamic knowledge extraction and authorization decisions which need to be taken when a resource is Accessem in an open, dynamic environment. Blockchain offers the tools to protect sensitive personal data and solve reliability issues by providing a secure communication architecture. However, setting-up blockchain-based applications comes with many challenges, including processing and fusing heterogeneous information from various sources. The ontology model explored in this paper relies on a unified knowledge representation method and thus is the backbone of a distributed system aiming to tackle semantic heterogeneity and to model decentralized management of Access control authorizations.We intertwine the blockchain technology with an ontological model to enhance knowledge management processes for distributed systems. Therefore, rather than reling on the mediation of a third party, the approach enhances autonomous decision-making. The proposed approach collects data generated by sensors into higher-level abstraction using n-ary hierarchical structures to describe entities and actions. Moreover, the proposed semantic architecture relies on hyperledger fabric to ensure the checking and authentication of knowledge integrity while preserving privacy.

Tiny-UKSIE

The application of semantic web technologies such as semantic inference to the field of the internet of things (IoT) can realize data semantic information enhancement and semantic knowledge discovery, which plays a key role in enhancing data value and application intelligence. However, mainstream semantic inference engines cannot be applied to IoT computing devices with limited storage resources and weak computing power and cannot reason about uncertain knowledge. To solve this problem, the authors propose a lightweight semantic inference engine, Tiny-UKSIE, based on the RETE algorithm. The genetic algorithm (GA) is adopted to optimize the Alpha network sequence, and the inference time can be reduced by 8.73% before and after optimization. Moreover, a four-tuple knowledge representation method with probability factors is proposed, and probabilistic inference rules are constructed to enable the inference engine to infer uncertain knowledge. Compared with mainstream inference engines, storage resource usage is reduced by up to 97.37%, and inference time is reduced by up to 24.55%.

An Approach to Task Representation Based on Object Features and Affordances.

Multi-purpose service robots must execute their tasks reliably in different situations, as well as learn from humans and explain their plans to them. We address these issues by introducing a knowledge representation scheme to facilitate skill generalization and explainability. This scheme allows representing knowledge of the robot’s understanding of a scene and performed task. We also present techniques for extracting this knowledge from raw data. Such knowledge representation and extraction methods have not been explored adequately in previous research. Our approach does not require any prior knowledge or 3D models of the objects involved. Moreover, the representation scheme is easy to understand for humans. The system is modular so that new recognition or reasoning routines can be added without changing the basic architecture. We developed a computer vision system and a task reasoning module that works with our knowledge representation. The efficacy of our approach is demonstrated with two different tasks: hanging items on pegs and stacking one item on another. A formalization of our knowledge representation scheme is presented, showing how the system is capable of learning from a few demonstrations.

Retraction Note to: Research on intelligent knowledge representation method and algorithm based on basic-element theory

Retraction Note to: Research on intelligent knowledge representation method and algorithm based on basic-element theory