Existing Knowledge Representation Research Articles

Logical-semantic models and methods of knowledge representation: cases for energy management systems and SMR digital infrastructures

The subject of this article is the development of information technologies at the end of the 20th and the beginning of the 21st century for the Fourth Industrial Revolution in the form of Industry 4.0, i.e., Internet of Things (IoT) technologies. Successes in the implementation of this technology have led to the development of new applications in the energy sector, such as energy management systems, small modular reactor (SMR) management systems, and alternative power supply systems based on renewable energy sources. The digital infrastructure of these management systems is characterized by a high "density of knowledge", which requires clarification of fundamental concepts in information theory, namely the content of the concepts "data", "information", "knowledge" and "meaning of knowledge". Special attention was given to defining the role of knowledge. The aim of this study is to further develop methods and models of semiotic theory by determining the role and place of logical as well as eight- and four-factor logical-semantic models of knowledge bases in semiotic space. The tasks: comparing existing knowledge representation methods and models. Research results: We found that the logical models used in the development of knowledge bases are based on the principles of artificial intelligence theory, which relies on sign system hypotheses and formal logic theory. The main drawback is the complexity of practical implementation in the form of expert systems. For logical-semantic models in the form of eight-vector graphic models, it was found that there is currently no theoretical justification for defining the vectors that form the coordinate axes, making these models unique to specific subject areas. It was determined that the advantage of using these methods is that an expert can independently form such a knowledge model. For logical-semantic models in the form of four-factor graphic models, there is a theoretical justification for defining the factors of the model that form the coordinate axes, making these models universal for specific subject areas. It was established that the advantage of these models is that they can be developed by experts without the involvement of a knowledge engineer. Therefore, it is proposed to use four-factor logical-semantic knowledge representation models for further application. It is also proposed to split the element "logical-semantic models" into two elements in the semiotic spatial model in vector K8 "Knowledge Representation Models", namely: "logical-semantic eight-vector models" and "logical-semantic four-factor models". Additionally, it is proposed to add the element "post-Cartesian representation of meta-knowledge" to the element "geometric" in vector K5 "Ideal Models". Conclusions: The theoretical basis for developing eight-factor logical-semantic knowledge representation models is the form of connections between adjacent vectors in the form of Cartesian products on elements of the corresponding inter-coordinate matrices. The theoretical basis for the methodology of developing four-factor logical-semantic knowledge representation models is the form of connections between adjacent vectors in the form of Cartesian products for elements of the corresponding inter-coordinate matrices, as well as for diametrically opposite pairs of factors in the form of dialectical unity of the concepts "general" and "particular." The application of logical-semantic knowledge representation models for alternative energy-source management systems will ensure increased energy efficiency. Other cases related to the development of databases for SMR digital infrastructure are discussed.

ENSURING DATA UNIQUENESS IN SEMANTIC NETWORKS

The article gives a brief description of knowledge representation models. Atoms of meaning (basic, minimal informational units) combined with each other to express a common meaning represent knowledge (data about data, metadata). It is shown that most of the existing knowledge representation models are based on the network representation model. A method is proposed to ensure the uniqueness (originality) of a set of data underlying the network model of knowledge representation. To ensure the uniqueness of knowledge representation by a set of data, the article proposes to use the main theorem of arithmetic: data are denoted by simple numbers (identifiers); when multiplying among themselves several prime numbers (data identifiers) in their totality conveying the aggregate semantic meaning, a natural number is obtained, which is an identifier of knowledge. The use of the basic theorem of arithmetic also provides a formalization of important data properties: internal interpretability, structuredness, connectivity, semantic metrics and activity. The presence of these properties in the data indicates that it is already data over data, or in other words, knowledge. In certain cases, this can reduce the computational complexity of the algorithm of the linguistic processor.

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.

SimH: A Novel Representation Learning Model With Activation and Projection Mechanisms for COVID-19 Knowledge Bases.

The emergence of coronavirus disease 2019 (COVID-19) has had a significant impact on healthcare and the economy. With representation learning applied in constructing COVID-19 knowledge graphs, abundant COVID-19-related knowledge collected by clinicians and scientists all over the world can be utilized to deepen their understanding of the mechanism and related biological functions of the disease. However, most existing representation learning models cannot deal well with COVID-19 knowledge graph due to its low-connected star-like structure and various complex nonlinear relationships. Besides, lacking reliable negative triplets is also a difficult problem, yet to be adequately resolved. In this article, we propose a novel representation learning model called translation on hyperplanes with an activation operation and similar semantic sampling (SimH) for COVID-19 knowledge graphs. In our proposed SimH, an activation operation is designed to provide additional interaction features for low-in-degree entities. Then the hyperplane projection technique is introduced to the distance-based scoring function so that those complex nonlinear relationships can be modeled with lower complexity maintained in comparison with other nonlinear models. Moreover, a negative triplet sampling method that adaptively replaces entities with similar semantics is introduced to generate reliable negative triplets. To verify the effectiveness of SimH, extensive experiments are conducted on the COVID-19-Concepts dataset. The experimental results show that our SimH model achieves significant improvements in prediction and classification accuracy over existing knowledge representation learning models.

Representation Learning of Knowledge Graphs With Entity Attributes

Most of the existing knowledge representation learning methods project the entities and relations represented by symbols in the knowledge graph into the low-dimensional vector space from the perspective of the structure and semantics of triples, and express the complex relations between entities and relations with dense low-dimensional vectors. However, triples in the knowledge graph not only contain relation triples, but also contain a large number of attribute triples. Existing knowledge representation methods often confuse these two kinds of triples and pay little attention to the semantic information contained in attributes and attribute values. In this paper, a novel representation learning method which makes use of the attribute information of entities is proposed. Specifically, deep convolutional neural network model is used to encode attribute information of entities, and both attribute information and triple structure information are utilized to learn knowledge representation, and then generate attribute-based representation of entities. The knowledge graph completion task was used to evaluate this method, and the experimental results on open data sets FB15K and FB24k showed that the attribute-embodied knowledge representation learning model outperforms the other baselines.

Understanding characteristics of semantic associations in health consumer generated knowledge representation in social media

This study explores knowledge organization behavior on the Web with respect to identifying the semantic relationships of health‐related concepts. In particular, this study aims to investigate the potentials of imparting richer collective intelligence to existing knowledge representation systems in health. The study focuses on detecting semantic relationships between semantic groups of major concepts mined from health consumers' descriptions of health issues and associated user‐generated metadata (i.e., tags). A total of 50,263 blogs and associated 341,720 tags were collected from Tumblr, a blogging social networking site. Text mining and semantic network analysis methods were used to explore the usage patterns at semantic type levels of the identified medical concepts in tags, in blogs, and between tags and blogs. More various associations among semantic types were identified both in tags and in blogs. These associations were more diverse and complicated than the relationships in the Unified Medical Language System Semantic Network. Among the groups of concepts in tags and blogs, groups showed relatively stronger and more diverse relationships with other groups of concepts. In addition, many direct and close relations were found between tags and blogs.

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.

Role based access control design using three-way formal concept analysis

Role based access control (RBAC) is one of the popular access control models. On representing the policy behind RBAC, the literatures investigate the use of various knowledge representation techniques such as Descriptive logics, Formal Concept Analysis (FCA), Ontology etc. Based on the input of binary access control table, the existing knowledge representation techniques on RBAC derives two-way decisions whether to permit the access request or not. It works well when single element in the set of elements of a constituent of RBAC initiates the access request. Consider the scenario of multiple distinct elements in the set of elements of a constituent of RBAC initiate the collective access request to a set of elements in other constituent of RBAC. In many cases of this scenario, some elements possess but not all of the elements possess the permission to access all elements in other subset of a constituent of RBAC. On this situation, the collective access decision to those multiple distinct elements in the set of elements of a RBAC constituent appears in three forms such as permit, deny and non-commitment. Three-way formal concept analysis (3WCA) is an emerging knowledge representation technique which provides two types of three-way concepts and their lattices to enable three-way decisions from the binary information table. At this juncture, it is more suitable to apply 3WCA on representing the RBAC policy to enable three-way decisions instead of existing two-way decisions in classical FCA and triadic FCA. The main objective of this paper is to propose a methodology for modelling RBAC using 3WCA and attain its distinctive merits. Our discussion is on two lines of inquiry. We present on how 3WCA can provide suitable representation of RBAC policy and whether this representation follows role hierarchy and constraints of RBAC.

Digital 3D reconstructed models A proposition for structuring visualisation workflows using semantic technologies for recommendations

It is common for cultural heritage applications to use spatial and/or spectral data for documentation, analysis and visualisation. Knowledge on data requirements coming from the cultural heritage application and technical alternatives to generate the required data based on object characteristics and other influencings factors paves the way for the optimal selection of a recording technology. It is a collaborative process requiring knowledge of experts from cultural heritage domains and technical domains. Currently, this knowledge is structured and stored in an ontology (so-called COSCHKR). It has the purpose to support CH experts not familiar with technologies through prescribing an optimal spatial or spectral recording strategy adapted to the physical characteristics of the cultural heritage object and the data requirements of the targeted CH application. The creation of digital 3D reconstructed models for analysis and visualisation purposes is getting more and more common within humanities disciplines. Therefore, an implementation of mechanisms involved in visualisation applications into this ontology would have huge benefits in creating a powerful recommendation solution. A structured view on such project workflows facilitates a rough match with the existing knowledge representation. Illustrating the overall structure of COSCHKR, this paper addresses and discusses challenges in structuring the processes of cultural heritage visualisation and implementing these into the ontology.

Research on A New Knowledge Model in Network Teaching

In view of the problems of the mismatch between the way of knowledge expression and the essential goal of teaching in network-teaching applications, based on the nature of teaching, this thesis analyzes the essential differences between network teaching and traditional teaching and the inelasticity of the existing knowledge representation to the network teaching. Based on this, a new knowledge model for the training of cognitive ability, schema and schema construction methods is proposed. The related concepts are discussed and the visual modeling and implementation are given. The new knowledge model implements a unified description of the basic knowledge and the pattern knowledge and supports the construction and application of these two types of knowledge. Finally, combined with a specific instance, the role of the pattern knowledge in teaching process is expounded and the basic application of this knowledge model is realized.

Paired structures in knowledge representation

In this position paper we propose a consistent and unifying view to all those basic knowledge representation models that are based on the existence of two somehow opposite fuzzy concepts. A number of these basic models can be found in fuzzy logic and multi-valued logic literature. Here it is claimed that it is the semantic relationship between two paired concepts what determines the emergence of different types of neutrality, namely indeterminacy, ambivalence and conflict, widely used under different frameworks (possibly under different names). It will be shown the potential relevance of paired structures, generated from two paired concepts together with their associated neutrality, all of them to be modeled as fuzzy sets. In this way, paired structures can be viewed as a standard basic model from which different models arise. This unifying view should therefore allow a deeper analysis of the relationships between several existing knowledge representation formalisms, providing a basis from which more expressive models can be later developed.

Temporal Bayesian Knowledge Bases – Reasoning about uncertainty with temporal constraints

Time is ubiquitous. Accounting for time and its interaction with change is crucial to modeling the dynamic world, especially in domains whose study of data is sensitive to time such as in medical diagnosis, financial investment, and natural language processing, to name a few. We present a framework that incorporates both uncertainty and time in its reasoning scheme. It is based on an existing knowledge representation called Bayesian Knowledge Bases. It provides a graphical representation of knowledge, time and uncertainty, and enables probabilistic and temporal inferencing. The reasoning scheme is probabilistically sound and the fusion of temporal fragments is well defined. We will discuss some properties of this framework and introduce algorithms to ensure groundedness during the construction of the model. The framework has been applied to both artificial and real world scenarios.

Creation of extension knowledge base system about intelligent detection in dendrobium huoshanense photosynthesis process

Aiming at the limitations of the existing knowledge representations in intelligent detection, a novel extension-based knowledge representation (EKR) is proposed. The definitions, grammar rules, and formal semantics of EKR are presented. A rhombus solving strategy (RSS) based on EKR is discussed in detail, including creation of the problem oriented model, extension operator, the solution formation of contradictions problem and extended inference of matter-element. A knowledge base system based on EKR and RSS is developed, which is applied in intelligent detection in the Dendrobium huoshanense photosynthesis process (DHPP). More reasonable results are obtained than traditional rule-based system. The EKR is feasible in intelligent detection to solve the limitations of traditional knowledge representations.

Representation of texts in structured form

Although the existing knowledge representation techniques, ranging from the relational databases to the most recent Semantic web languages, are successfully applied in numerous practical applications, they are still unable to represent the information contained in text documents and web pages in structured form, suitable for productive text processing. Text files can represent text documents with no loss of information, however, this information is represented in an unstructured form. Various knowledge formalisms used in different phases of Natural Language Understanding, such as lexical, syntactic, semantic, pragmatic and discourse analysis, are still unable to represent texts in structured form with no loss of information. In this paper, we define the crucial requirements for structured text representation and then, we give a brief introduction to a representation technique that fulfills all these requirements, including the basic data types and learning techniques used to create, maintain and interpret the resulting representation formalism.

Extension Modeling Strategy of Intelligent Detection in D.huoshanense Photosynthesis Process

Aiming at the limitations of the existing knowledge representations in intelligent detection, a new method of Extension-based Knowledge Representation (EKR) was proposed. The definitions, grammar rules, and storage structure of EKR were presented. An Extension Solving Model (ESM) based on EKR was discussed in detail, including creation of the extension constraint graph, extended inference, calculation of relevant functions and generation of extension set. A knowledge base system based on EKR and ESM was developed, which was applied in extension repository system intelligent design of detection in photosynthesis process of D.huoshanense. More reasonable results were obtained than traditional rule-based system. EKR was feasible in intelligent design to solve the problem of intelligent detection knowledge representations.

Contrasting contributions of phonological short‐term memory and long‐term knowledge to vocabulary learning in a foreign language

The contributions of phonological short‐term memory and existing foreign vocabulary knowledge to the learning of new words in a second language were compared in a sample of 40 Greek children studying English at school. The children's speed of learning new English words in a paired‐associate learning task was strongly influenced by their current English vocabulary, but was independent of phonological memory skill, indexed by nonword repetition ability. However, phonological memory performance was closely linked to English vocabulary scores. The findings suggest that in learners with considerable familiarity with a second language, foreign vocabulary acquisition is mediated largely by use of existing knowledge representations.

Accelerating the computer-based simulation and design of electromagnetic devices by applying knowledge

The paper provides a survey of existing knowledge representation paradigms in the context of electromagnetic device design. The use of a variety of knowledge-based paradigms in decreasing the time taken to achieve a desired maximum error in the solution, as well as in the optimization of a design, is discussed, and a typical example of the improvements in one process (adaptive solution in finite elements) is given.

Multilingual Knowledge Bases in Agriculture

The diffusion of knowledge is constrained by language barriers. The cost of translating sophisticated software products, like expert systems, is overwhelming. Existing knowledge representation schemes for building up a KB do not provide mechanisms to handle KB translation problems. However, a TR for the construction of a KB seems to provide an appropriate basis for dealing with this problem. According to this TR scheme the whole KB is represented by a set of tables having the proper structure under the management capabilities of a RDBMS. In the present paper, the principles of this design are described, and the TR structure is exploited in order to develop a multilingual KB under an RDBMS. The main issues concerning the maintenance of this multilingual KB under a RDBMS are discussed, and ways are suggested to interface the TR of the KB to a dedicated expert shell. Finally, the results of the implementation under the Microsoft Windows environment are reported.

Inference-driven construction of valuation systems from first-order clauses

Network-based representations of uncertain knowledge are inherently propositional, and cannot easily accommodate generic, problem independent knowledge. A tremendous effort has been devoted in the knowledge representation community to develop languages that adequately represent different types of generic knowledge. The authors propose inference-driven construction as a means to use these languages for extending the expressiveness of uncertainty networks: they let a knowledge representation system represent generic knowledge and infer solutions to specific problem instances, and then copy the resulting inference structure to an uncertainty network that models these instances. From a dual perspective, inference-driven construction is a way of extending an existing knowledge representation system by attaching an uncertainty calculus to it. In this paper, the authors focus on one particular case of inference-driven construction: building Shenoy-Shafer's valuation systems from uncertain knowledge expressed in the form of first order clauses annotated by Dempster-Shafer's measures of belief. The authors detail an automatic construction procedure for this case, discuss a sample implementation, and provide a soundness and completeness result. >

The Knowledge-Based Optimizing of Electronic Controllers

The concept presented was developed to support the repeated optimizing process of controllers by an expert system in a very simple way. We developed a method that enables us to record the history of such a process very easily. It even enables us to derive new knowledge from the history during the optimizing process. The constructs for analyzing history which are added as new elements to an existing knowledge representation language are based on the principal mechanisms of acquiring new knowledge (gained from the evaluation of analogies). These constructs support the explicit modelling of experience from the history.