Knowledge Representation Method Research Articles

A pre-control method in safety risk management of metro construction

ABSTRACT With the rapid development of metro construction in China, construction safety accidents occur frequently due to the lack of effective safety risk controls. Continuously improving the level of safety risk pre-control has become a significant factor in the safety managements of metro construction. This paper presents a reasoning model for safety risk pre-control measures in metro construction using case-based reasoning. To establish the case database, a frame-based knowledge representation method is adopted based on the characteristics of metro construction safety accident cases. Additionally, a two-level retrieval strategy combining hierarchical retrieval and nearest-neighbor algorithms is used for case retrieval. Furthermore, a new approach, the cloud model, is proposed to determine the attribute weight of metro construction safety accidents. This approach addresses quantitative attributes with fuzziness and randomness. Finally, a specific metro station is taken as a case to validate the feasibility of the approach. The results showed that the case-based reasoning method enables engineers to quickly and effectively develop risk pre-control measures based on previous cases. In addition, the use of case-based reasoning in decision-making enhances knowledge sharing and reuse in the metro construction industry. This paper presents an effective reasoning approach for pre-control measure analysis in metro construction, allowing engineers to quickly and efficiently carry out scientific and reasonable pre-control measures from past cases.

Leveraging artificial intelligence and software engineering methods in epidemiology for the co-creation of decision-support tools based on mechanistic models

Epidemiological modelling is a key lever for infectious disease control and prevention on farms. It makes it possible to understand the spread of pathogens, but also to compare intervention scenarios even in counterfactual situations. However, the actual capability of decision makers to use mechanistic models to support timely interventions is limited. This study demonstrates how artificial intelligence (AI) techniques can make mechanistic epidemiological models more accessible to farmers and veterinarians, and how to transform such models into user-friendly decision-support tools (DST). By leveraging knowledge representation methods, such as the textual formalization of model components through a domain-specific language (DSL), the co-design of mechanistic models and DST becomes more efficient and collaborative. This facilitates the integration of explicit expert knowledge and practical insights into the modelling process. Furthermore, the utilization of AI and software engineering enables the automation of web application generation based on existing mechanistic models. This automation simplifies the development of DST, as tool designers can focus on identifying users' needs and specifying expected features and meaningful presentations of outcomes, instead of wasting time in writing code to wrap models into web apps.To illustrate the practical application of this approach, we consider the example of Bovine Respiratory Disease (BRD), a tough challenge in fattening farms where young beef bulls often develop BRD shortly after being allocated into pens. BRD is a multi-factorial, multi-pathogen disease that is difficult to anticipate and control, often resulting in the massive use of antimicrobials to mitigate its impact on animal health, welfare, and economic losses. The DST developed from an existing mechanistic BRD model empowers users, including farmers and veterinarians, to customize scenarios based on their specific farm conditions. It enables them to anticipate the effects of various pathogens, compare the epidemiological and economic outcomes associated with different farming practices, and decide how to balance the reduction of disease impact and the reduction of antimicrobial usage (AMU).The generic method presented in this article illustrates the potential of artificial intelligence (AI) and software engineering methods to enhance the co-creation of DST based on mechanistic models in veterinary epidemiology. The corresponding pipeline is distributed as an open-source software. By leveraging these advancements, this research aims to bridge the gap between theoretical models and the practical usage of their outcomes on the field.

Modification and completion of geological structure knowledge graph based on pattern matching

As a knowledge representation method, knowledge graph is widely used in intelligent question answering systems and recommendation systems. At present, the research on knowledge graph mainly focuses on information query and retrieval based on knowledge graph. In some domain knowledge graphs, specific subgraph structures (patterns) have specific physical meanings. Aiming at this problem, this paper proposes a method and framework of knowledge graph pattern mining based on gat. Firstly, the patterns with specific physical meaning were transformed into subgraph structures containing topological structures and entity attributes. Secondly, the subgraph structure of the pattern is regarded as the query graph, and the knowledge graph is regarded as the data graph, so that the problem is transformed into an approximate subgraph matching problem. Then, the improved relational graph attention network is used to fuse the adaptive edge deletion mechanism to realize the approximate subgraph matching of subgraph structure and attribute, so as to obtain the best matching subgraph. The proposed method is trained in an end-to-end manner. The approximate subgraph matching is realized on the existing data set, and the research work of key pattern mining of complex geological structure knowledge graph is carried out.

Online biomedical named entities recognition by data and knowledge-driven model

Named entity recognition (NER) is an important task for the natural language processing of biomedical text. Currently, most NER studies standardized biomedical text, but NER for unstandardized biomedical text draws less attention from researchers. Named entities in online biomedical text exist with errors and polymorphisms, which negatively impact NER models’ performance and impede support from knowledge representation methods. In this paper, we propose a neural network method that can effectively recognize entities in unstandardized online medical/health text. We introduce a new pre-training scheme that uses large-scale online question-answering pairs to enhance transformers’ model capacity on online biomedical text. Moreover, we supply models with knowledge representations from a knowledge base called multi-channel knowledge labels, and this method overcomes the restriction from languages, like Chinese, that require word segmentation tools to represent knowledge. Our model outperforms other baseline methods significantly in experiments on a dataset for Chinese online medical entity recognition and achieves state-of-the-art results.

Knowledge Graph Construction and Representation Method for Potato Diseases and Pests

Potato diseases and pests have a serious impact on the quality and yield of potatoes, and timely prevention and control of potato diseases and pests is essential. A rich knowledge reserve of potato diseases and pests is one of the most important prevention and control measures; however, valuable knowledge is buried in the massive data of potato diseases and pests, making it difficult for potato growers and managers to obtain and use it in a timely manner and to develop the potential of knowledge. Therefore, this paper explores the construction method of a knowledge graph for automatic knowledge extraction, which extracts the knowledge of potato diseases and pests scattered in heterogeneous data from multiple sources, organises it into a semantically related knowledge base, and provides potato growers with professional knowledge and timely guidance to effectively prevent and control potato diseases and pests. In this paper, a data corpus on potato diseases and pests, called PotatoRE, is first constructed. Then, a model of ALBert-BiLSTM-Self_Att-CRF is designed to extract knowledge from the corpus to form a triplet structure, which is imported into the Neo4j graph database for storage and visualisation. Furthermore, the performance of the model constructed in this paper is compared and verified using the datasets PotatoRE and People’s Daily. The results show that compared to the SOTA models of ALBert BiLSTM-CRF and ALBert BiGRU-CRF, the accuracy of our model has been improved by 2.92% and 3.12%, respectively, using PotatoRE. Compared to the Bert BiLSTM-CRF model on two datasets, our model not only improves the accuracy, recall, and F1 values, but also has a higher efficiency. The model in this paper solves the problem of the difficult recognition of nested entities. On this basis, through comparative experiments, the TransH model is used to effectively represent the constructed knowledge graph, which lays the foundation for achieving inference, extension, and automatic updating of the knowledge base. The achievements of the thesis have made certain contributions to the automatic construction of large-scale knowledge bases.

Mobility knowledge graph: review and its application in public transport

AbstractUnderstanding human mobility in urban areas is crucial for transportation planning, operations, and online control. The availability of large-scale and diverse mobility data (e.g., smart card data, GPS data), provides valuable insights into human mobility patterns. However, organizing and analyzing such data pose significant challenges. Knowledge graph (KG), a graph-based knowledge representation method, has been successfully applied in various domains but has limited applications in urban mobility. This paper aims to address this gap by reviewing existing KG studies, introducing the concept of a mobility knowledge graph (MKG), and proposing a general learning framework to construct MKG from smart card data. The MKG represents hidden travel activities between public transport stations, with stations as nodes and their relations as edges. Two decomposition approaches, rule-based and neural network-based models, are developed to extract MKG relations from smart card data, capturing latent spatiotemporal travel dependencies. The case study is conducted using smart card data from a heavily used urban railway system to validate the effectiveness of MKG in predicting individual trip destinations. The results demonstrate the significance of establishing an MKG database, as it assists in a typical problem of predicting individual trip destinations for public transport systems with only tap-in records. Additionally, the MKG framework offers potential for efficient data management and applications such as individual mobility prediction and personalized travel recommendations.

An analysis of knowledge representation methods in intelligent decision-making support systems

The scientific task, which is solved in the research, is the analysis of knowledge representation methods in intelligent decision-making support systems. The problem is explained by the fact that the form of knowledge representation significantly affects the characteristics and properties of the system. In order to operate all kinds of knowledge from the real world with the help of a computer, it is necessary to carry out their simulation. In such cases, it is necessary to distinguish knowledge intended for processing by computational devices from knowledge used by humans. In addition, with a large amount of knowledge, it is desirable to simplify the sequential management of individual elements of knowledge. A homogeneous representation leads to a simplification of the logic management mechanism and a simplification of knowledge management. The research is aimed at the analysis of knowledge representation methods in intelligent decision-making support systems. Currently, many models of knowledge representation have been developed. The main models include: logical models; frame model; network models (or semantic networks); production models. Therefore, the object of research is the intelligent decision-making support system. The subject of research is an intelligent decision-making support system. The following is set: – the methods (models, approaches) presented in the research for presenting knowledge in intelligent decision-making support systems in a canonical form are not advisable to use for a number of objective reasons given in subsection 3.1 of the research; – it is necessary to develop new (improvement of existing) representations of knowledge in intelligent decision-making support systems, which will have the advantages of these approaches without their disadvantages. Further improvement of these approaches to reduce the number of shortcomings and limitations of their application should be considered as the direction of further research.

An Ontology-Based Knowledge Representation Method for Typhoon Events from Chinese News Reports

Typhoons are destructive weather events. News media reports contain large amounts of typhoon information. Transforming this information into useful knowledge to provide a basis for mining typhoon knowledge and supporting disaster prevention and relief is urgently required to solve this problem. Knowledge representation can be used to address this problem, although it presents several challenges. These challenges lie in expressing the static and dynamic characteristics of typhoons and formalizing the knowledge representation method and making it suitable for machine processing. Moreover, the general Chinese time and space representation method is overly cumbersome for use in ontologies. The present study proposes an ontology-based typhoon event representation method that solves the representation problems of the typhoon static concept and dynamic features. Furthermore, it summarizes the fixed patterns of time and space in Chinese news and designs a time and space model suitable for typhoon event ontologies. From the ontology population, typhoon event ontology instances are created, and the typhoon event ontology model is applied to the analysis of typhoon processes, verifying the effectiveness of the typhoon event ontology model.

Articulating Intra- and Intertextual Relationships in the Fragment Collection Working with the Digital Edition of Giacomo Leopardi’s Zibaldone

Leopardi’s Zibaldone presents a compelling study of semantic relations between textual fragments because the author’s interpretive annotations form complex intra- and intertextual networks that elude close reading. The Digital Zibaldone project has built the foundation of a semantic digital edition to reconstruct the discourse order of Leopardi’s intellectual inquiries and his reception of fictional and scholarly works, while offering readers some tools for their analysis. Its larger research framework aims to create synergy between the hermeneutic project of the fragment genre and digital methods of knowledge representation.

EDET: Entity Descriptor Encoder of Transformer for Multi-Modal Knowledge Graph in Scene Parsing

In scene parsing, the model is required to be able to process complex multi-modal data such as images and contexts in real scenes, and discover their implicit connections from objects existing in the scene. As a storage method that contains entity information and the relationship between entities, a knowledge graph can well express objects and the semantic relationship between objects in the scene. In this paper, a new multi-phase process was proposed to solve scene parsing tasks; first, a knowledge graph was used to align the multi-modal information and then the graph-based model generates results. We also designed an experiment of feature engineering’s validation for a deep-learning model to preliminarily verify the effectiveness of this method. Hence, we proposed a knowledge representation method named Entity Descriptor Encoder of Transformer (EDET), which uses both the entity itself and its internal attributes for knowledge representation. This method can be embedded into the transformer structure to solve multi-modal scene parsing tasks. EDET can aggregate the multi-modal attributes of entities, and the results in the scene graph generation and image captioning tasks prove that EDET has excellent performance in multi-modal fields. Finally, the proposed method was applied to the industrial scene, which confirmed the viability of our method.

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.