Knowledge Representation Method Research Articles

A Method for Knowledge Representation to Design Intelligent Problems Solver in Mathematics Based on Rela-Ops Model

Knowledge-base is a fundamental platform in the architecture of an intelligent system. Relations and operators are popular knowledge in practice knowledge domains. In this paper, we propose a method to represent the model by combining these kinds of knowledge, called the Rela-Ops model. This model includes foundation components consisting of concepts, relations, operators, and inference rules. It is built based on ontology and object-oriented approaches. Besides the structure, each concept of the Rela-Ops model is a class of objects which also have behaviors to solve problems on their own. The processing of algorithms for solving problems on the Rela-Ops model combines the knowledge of relations and operators in the reasoning. Furthermore, we also propose a knowledge model for multiple knowledge domains, in which each sub-domain has the form as the Rela-Ops model. These representation methods have been applied to build knowledge bases of Intelligent Problems Solver (IPS) in mathematics. The knowledge base of 2D-Analytical Geometry in a high-school is built by using the Rela-Ops model, and the knowledge base of Linear Algebra in university is designed by using the model for multiple knowledge domains. The IPS system can automatically solve basic and advanced exercises in respective courses. The reasoning of their solutions is done in a step-by-step approach. It is similar to the solving method by humans. The solutions are also pedagogical and suitable for the learner’s level and easy to be used by students studying 2D-Analytical Geometry in high-school and Linear Algebra in university.

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.

Application of fuzzy intelligent reasoning method in art color measurement

Under the influence of the COVID-19 epidemic situation, many countries have taken many measures to control the flow of people. The inability of people to gather makes art color measurement a problem. Color matching and coordination and color space conversion have always been the research focus of art color measurement. This paper studies a method of fuzzy intelligent reasoning in art color measurement. Based on case-based reasoning and rule-based reasoning, this method is an important self-learning and self-maintenance method in the field of artificial intelligence and expert system. On the basis of expounding the basic characteristics of color and color space, this paper designs the process principle of case-based reasoning and the process of rule-based reasoning. In this paper, case and rule knowledge representation method, case retrieval technology and rule conflict resolution strategy are established. Based on the above strategy, the color case base is established. In addition, the rule table is established by combining color-brewer with rule reasoning and referring to color-brewer. The rule table has a certain reference value for the application of intelligent reasoning method in the field of art color measurement under the influence of COVID-19 epidemic.

A Cognitive Knowledge-based Model for an Academic Adaptive e-Advising System

Aim/Purpose: This study describes a conceptual model, based on the principles of concept algebra that can provide intelligent academic advice using adaptive, knowledge-based feedback. The proposed model advises students based on their traits and academic history. The system aims to deliver adaptive advice to students using historical data from previous and current students. This data-driven approach utilizes a cognitive knowledge-based (CKB) model to update the weights (values that indicate the strength of relationships between concepts) that exist between student’s performances and recommended courses. Background: A research study conducted at the Public Authority for Applied Education and Training (PAAET), a higher education institution in Kuwait, indicates that students’ have positive perceptions of the e-Advising system. Most students believe that PAAET’s e-Advising system is effective because it allows them to check their academic status, provides a clear vision of their academic timeline, and is a convenient, user-friendly, and attractive online service. Student advising can be a tedious element of academic life but is necessary to fill the gap between student performance and degree requirements. Higher education institutions have prioritized assisting undecided students with career decisions for decades. An important feature of e-Advising systems is personalized feedback, where tailored advice is provided based on students' characteristics and other external parameters. Previous e-Advising systems provide students with advice without taking into consideration their different attributes and goals. Methodology: This research describes a model for an e-Advising system that enables students to select courses recommended based on their personalities and academic performance. Three algorithms are used to provide students with adaptive course selection advice: the knowledge elicitation algorithm that represents students' personalities and academic information, the knowledge bonding algorithm that combines related concepts or ideas within the knowledge base, and the adaptive e-Advising model that recommends relevant courses. The knowledge elicitation algorithm acquires student and academic characteristics from data provided, while the knowledge bonding algorithm fuses the newly acquired features with existing information in the database. The adaptive e-Advising algorithm provides recommended courses to students based on existing cognitive knowledge to overcome the issues associated with traditional knowledge representation methods. Contribution: The design and implementation of an adaptive e-Advising system are challenging because it relies on both academic and student traits. A model that incorporates the conceptual interaction between the various academic and student-specific components is needed to manage these challenges. While other e-Advising systems provide students with general advice, these earlier models are too rudimentary to take student characteristics (e.g., knowledge level, learning style, performance, demographics) into consideration. For the online systems that have replaced face-to-face academic advising to be effective, they need to take into consideration the dynamic nature of contemporary students and academic settings. Findings: The proposed algorithms can accommodate a highly diverse student body by providing information tailored to each student. The academic and student elements are represented as an Object-Attribute-Relationship (OAR) model. Recommendations for Practitioners: The model proposed here provides insight into the potential relationships between students’ characteristics and their academic standing. Furthermore, this novel e-Advising system provides large quantities of data and a platform through which to query students, which should enable developing more effective, knowledge-based approaches to academic advising. Recommendation for Researchers: The proposed model provides researches with a framework to incorporate various academic and student characteristics to determine the optimal advisory factors that affect a student’s performance. Impact on Society: The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advice to students. The proposed approach can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to learning. Future Research: In future studies, the proposed algorithms will be implemented, and the adaptive e-Advising model will be tested on real-world data and then further improved to cater to specific academic settings. The proposed model will benefit e-Advising system developers in implementing updateable algorithms that can be tested and improved to provide adaptive advisory to students. The approach proposed can provide new insight to advisors on possible relationships between student’s characteristics and current academic settings. Thus, providing a means to develop new curriculums and approaches to course recommendation.

Research on Knowledge Representation and Automatic Recognition of Dynamic Words for Chinese Automatic Syntactic Analysis

There are many temporarily constructed dynamic words in Chinese sentences. Dynamic words are sentence building units that are not included in the general lexicon and are not suitable for further syntactic analysis. Automatic recognition and analysis of dynamic words in sentences play an important role in improving the efficiency and accuracy of Chinese automatic syntactic analysis. The existing researches on dynamic words mainly focus on the qualitative description of concepts and categories. There is no overall algorithm design and experimental exploration on automatic recognition of dynamic words. In the practice of automatic syntactic analysis, dynamic words are generally segmented, and the components are analyzed according to syntax, while the automatic recognition and analysis of dynamic words as a whole are ignored. In this study, the dynamic word is separated from syntactic analysis as the content of lexical analysis and recognized and analyzed as a whole. This paper uses the method of knowledge engineering to research and analyze dynamic words for Chinese automatic syntactic analysis based on sentence pattern structure, initially designs a knowledge representation method of dynamic words, secondly constructs the dynamic word structural mode knowledge base by annotating the dynamic words in the corpus of a certain scale of international Chinese textbooks, and finally explores the automatic recognition methods of dynamic words based on regular expressions, semantic category combinations and machine learning classification algorithms. The experimental results show that the three algorithms can cover the recognition of all types of dynamic words, and achieve relatively ideal accuracy and recall rate.

A Semantic Expansion Model for VGI Retrieval

OpenStreetMap (OSM) is a representative volunteered geographic information (VGI) project. However, there have been difficulties in retrieving spatial information from OSM. Ontology is an effective knowledge organization and representation method that is often used to enrich the search capabilities of search systems. This paper constructed an OSM ontology model with semantic property items. A query expansion method is also proposed based on the similarity of properties of the ontology model. Moreover, a relevant experiment is conducted using OSM data related to China. The experimental results demonstrate that the recall and precision of the proposed method reach 80% and 87% for geographic information retrieval, respectively. This study provides a method that can be used as a reference for subsequent research on spatial information retrieval.

Accounting for System-Wide Regularities in System-Object Modeling of Organizational Knowledge

This article clarifies the definition of system in terms of the Unit–Function–Object system-object approach based on the Abadi–Cardelli object calculus. The formalization of the concepts system-forming factor and system adaptation is proposed. The presented formalisms are used to take system-wide regularities in constructing graphical-analytical models of organizational systems and business processes by the system-object method of knowledge representation into account. The identified possible ways of taking regularities into account are compared with the capabilities of the system-structural and object-oriented approaches, as well as with the Business Process Model and Notation. A formal description of system-wide regularities is given that makes it possible to substantiate the hierarchical relationship between them.

On Ontology-Based Tourist Knowledge Representation and Recommendation

In the rapid development of the information technology age, many travelers search for travel articles through the Internet. These travel articles include the experience and knowledge of traveler, which can be used as a reference for tourism planning and attraction selection. At present, the most travel experience and knowledge is available in online travel reviews (OTR). OTR and eWOM (electronic word-of-mouth) contain a lot of knowledge of consumers and travelers. Many travelers often look for OTR content through virtual communities, blogs, and search engine, but the search results often cause information overload problems. In addition, through virtual communities, blogs, and search engines, an OTR search still requires using keywords. However, most travelers cannot know the name of the attraction; therefore, travelers cannot use the correct keywords to search. That causes travelers to be unable to get enough information from OTR and unable to make the best travel plan. Therefore, this study focuses on the ontology-based tourist knowledge representation and recommendation method. And the study is to search for popular attractions from the OTR content and construct a tourist knowledge structure for these travelers. When the tourists do not need to know the keywords of the popular attraction name, they just need to get their current location; and then ORT content will recommend the next attraction to the traveler, which helps the traveler make the correct travel decision. The evaluation result showed that the method proposed in this study can help the travelers to quickly make the travel decision and is better than the traditional searching methods.

Ten years of knowledge representation for health care (2009-2018): Topics, trends, and challenges.

In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide. Carry out a review of the papers accepted in KR4HC in the 2009-2018 decade, analyze and characterize the topics and trends within this field, and identify challenges for the evolution of the area in the near future. We reviewed the title, the abstract, and the keywords of the 112 papers that were accepted to the workshop, identified the medical and technological topics involved in these works, provided a classification of these papers in medical and technological perspectives and obtained the timeline of these topics in order to determine interest growths and declines. The experience of the authors in the field and the evidences after the review were the basis to propose a list of challenges of knowledge representation in health care for the future. The most generic knowledge representation methods are ontologies (31%), semantic web related formalisms (26%), decision tables and rules (19%), logic (14%), and probabilistic models (10%). From a medical informatics perspective, knowledge is mainly represented as computer interpretable clinical guidelines (43%), medical domain ontologies (26%), and electronic health care records (22%). Within the knowledge lifecycle, contributions are found in knowledge generation (38%), knowledge specification (24%), exception detection and management (12%), knowledge enactment (8%), temporal knowledge and reasoning (7%), and knowledge sharing and maintenance (7%). The clinical emphasis of knowledge is mainly related to clinical treatments (27%), diagnosis (13%), clinical quality indicators (13%), and guideline integration for multimorbid patients (12%). According to the level of development of the works presented, we distinguished four maturity levels: formal (22%), implementation (52%), testing (13%), and deployment (2%) levels. Some papers described technologies for specific clinical issues or diseases, mainly cancer (22%) and diseases of the circulatory system (20%). Chronicity and comorbidity were present in 10% and 8% of the papers, respectively. KR4HC is a stable community, still active after ten years. A persistent focus has been knowledge representation, with an emphasis on semantic-web ontologies and on clinical-guideline based decision-support. Among others, two topics receive growing attention: integration of computer-interpretable guideline knowledge for the management of multimorbidity patients, and patient empowerment and patient-centric care.

Analogical stimuli retrieval approach based on R-SBF ontology model

ABSTRACTAnalogy-based design is an effective approach for innovative design involving knowledge representation, analogical sources retrieval, mapping between two systems, and adaptation of candidate solutions. The knowledge representation is the first task to determine whether analogy can be implemented successfully. It is, therefore, necessary to have an efficient description method for knowledge representation and information retrieval. Based on the Structure-Behaviour-Function (SBF) model, this paper proposes an R-SBF model to integrate the flow, structure, behaviour and function of design knowledge. Relational types of states are used to describe the information behaviour with relations of ‘and’, ‘or’, ‘not’, ‘sequence’, ‘parallel’ and ‘feedback’. The R-SBF ontology model is constructed using the ontology editing tool Protégé 5.2.0 to link the knowledge representation and analogues retrieval. In the proposed algorithm of computing function similarity, both the semantic similarity and conceptual correlation are considered for the query scalability. The evaluation criteria for feasibility include Recall, Precision, and F-measure. An analogy-aided design innovation software prototype is developed to support the design activity. The improvement of a robot vacuum cleaner is discussed as an example of applications of the proposed method. The solution illustrates the effectiveness of the analogical stimuli to facilitate the design performance.

FORMATION OF THE LEXEME’S CORPUS FOR THE LEVEL MODEL DEVELOPMENT OF LANGUAGE

Purpose: The article deals with the principles of selection of factual material on the problem of fixing language invariants and variants.
 Methodology: This is an analytic-dialectical research in which the data was obtained through the analysis of language and grammar in English texts.
 Main Findings: As a result of using the system-object method of knowledge representation, a model for securing language variants is constructed. The UFO model of the organizational system can act as a network, production, and frame models. In conclusion, only the internal flows are of interest at the level of the function description, since the algebra of images of the theory of patterns and the theory of graphs are involved in external flows.
 Applications: This article can be used by regional schools, cultural organizations and universities.
 Novelty/Originality: In this research, a level model of language invariants and variants was developed and presented. Statistical accounting methods, linguistic and computer modeling methods were used on this model.

Knowledge Reduction Model of Crowd Evacuation Stability Based on Rough Set Theory

To reduce the redundant knowledge discovered from the evolution of crowd evacuation in case of emergencies, this paper aims at knowledge reduction model of crowd evacuation stability evolution. First, the evolutionary mechanism of evacuation stability state is analysed from the perspective of knowledge engineering, and the characteristics of evacuation scenario elements such as physical properties of crowd, structure of evacuation scenario and psychological behaviour of evacuees are analysed and extracted by using self-organizing mapping network algorithm; Secondly, the evolutionary characteristics of evacuation scenario elements and evacuation stability state are discretized and mapped to conditional attributes and decision attributes in rough set universe respectively. Thereby a knowledge representation method of evacuation stability evolution mechanism based on rough set decision information table is constructed. Then, the improved attribute reduction algorithm based on discernibility matrix is used to reduce the knowledge of decision information table processed by self-organizing network algorithm. Finally, a knowledge reduction model of crowd evacuation stability based on rough sets is proposed. It is validated effective to achieve the decision-making of crowd stability, and which provides theoretical basis and methodological support for scientific guidance of emergency evacuation.

Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry.

Case-based reasoning (CBR) has been extensively employed in various construction management areas, involving construction cost prediction, duration estimation, risk management, tendering, bidding and procurement. However, there has been a dearth of research integrating CBR with construction safety management for preventing safety accidents. This paper proposes a CBR model which focuses on case retrieval and reuse to provide safety solutions for new problems. It begins with the identification of case problem attribute and solution attribute, the state of hazard is used to describe the problem attribute based on principles of people’s unsafe behavior and objective’s unsafe state. Frame-based knowledge representation method is adopted to establish the case database from dimensions of slot, facet and facet’s value. Besides, cloud graph method is introduced to determine the attribute weight through analyzing the numerical characteristics of expectation value, entropy value and hyper entropy value. Next, thesaurus method is employed to calculate the similarity between cases including word level similarity and sentence level similarity. Principles and procedures have been provided on case revise and case retain. Finally, a real-world case is conducted to illustrate the applicability and effectiveness of the proposed model. Considering the high potential for pre-control and decision-making of construction safety accident, the proposed model is expected to contribute safety managers to take decisions on prevention measures more efficiently.

A Survey of Knowledge-Based Intelligent Fault Diagnosis Techniques

With the development of information technologies, more and more real-time data can be obtained from production and operation process. Thus, how to extract effective information from these massive data, so as to carry out in-depth statistics and mining of faults, and gradually explore the faults laws and causes are crucial for intelligent factories. In recent years, a variety of statistical learning and data analysis methods have been used in fault diagnosis. Due to the complex structure, multi-source failure and suddenness of the industrial production system, the combination of empirical knowledge and mechanism principles can solve various fault problems. This paper summarizes several commonly used fault diagnosis methods, and focuses on knowledge-based intelligent fault diagnosis, including first-order logic knowledge representation method, production knowledge representation method, framework knowledge representation method, object-oriented knowledge representation method and Semantic-based knowledge representation methods.

Knowledge representation and reasoning using self-learning interval type-2 fuzzy Petri nets and extended TOPSIS

Fuzzy Petri nets (FPNs), which have been extensively used in many fields, are a promising method for knowledge representation and reasoning (KRR). However, there are still some shortcomings in these FPNs. Although there are many improved FPNs, the existing FPNs still have difficulties to ‘embrace’ the inconsistent cognition of experts and individualized features of different systems. In this paper, a new type FPNs are proposed, called self-learning interval type-2 fuzzy Petri nets (SLIT2FPNs). The extended TOPSIS is proposed to determine the optimal alternative, and to collect cognition of experts. An interval numbers ordered weighted averaging operator is introduced to improve the knowledge reasoning capabilities of SLIT2FPNs. Moreover, because of the introduction of state transition algorithm, the model has the ability of self-learning and adjustment according to information dynamics. Finally, two comparison tests are presented to demonstrate the proposed methods. In addition, the applications in aluminum reduction process show that SLIT2FPNs are feasible to ‘embrace’ inconsistent cognition, and to attract individualized features of each reduction cell, and acquire a KRR model with good performance. These facts demonstrate that the methods and ideas proposed in this paper provide a solution for KRR of knowledge automation in the industrial processes.

Semantic similarity measures for formal concept analysis using linked data and WordNet

Formal Concept Analysis (FCA) is a field of applied mathematics with its roots in order theory, in particular the theory of complete lattices. It is not only a method for data analysis and knowledge representation, but also a formal formulation for concept formation and learning. Over the past 20 years, FCA has been widely studied. In this paper, the current research progresses and the existing problems of similarity measures in FCA are analyzed. To address the drawbacks of the existing methods, we propose a kind of novel semantic similarity measure for FCA by using Linked Data and WordNet. We aim to develop a method that is fully automatic without requiring predefined domain ontologies and can be used independently of the domain in applications requiring semantic similarity measures in FCA. To realize the semantic similarity estimation for FCA, we firstly extend the similarity assessment methods for resources (or entities) in Linked Data into semantic cases by using WordNet. Furthermore, we propose two kinds of semantic similarity measures (i.e., context-free method and context-aware method) for FCA concepts and concept lattices, respectively. Compared with the existing similarity measure methods in FCA, the proposed approach uses concept of possibility theory to determine lower and upper bounds of similarity intervals. Finally, we evaluate the proposed similarity assessment approaches by applying them to real-worlds datasets.

Fuzzy spatio‐temporal ontologies and formal construction based on fuzzy Petri nets

AbstractFuzzy spatio‐temporal knowledge is required in a wide range of application fields, including GIS, spatio‐temporal database, and artificial intelligence. Ontology, as a formal method of knowledge representation, plays a key role in the Semantic Web. Therefore, how to extend ontologies to represent fuzzy spatio‐temporal knowledge needs to be solved. In this paper, we propose an approach for representing fuzzy spatio‐temporal knowledge with ontologies and investigate a formal construction of fuzzy spatio‐temporal ontologies based on fuzzy Petri nets. First, we propose a formal definition of fuzzy spatio‐temporal ontologies. In addition, based on fuzzy description logic, a description logic named the fuzzy spatio‐temporal description logic, which provides a logical basis for the fuzzy spatio‐temporal ontologies, is presented. Furthermore, a fuzzy spatio‐temporal web ontology language, which is fuzzy and spatio‐temporal extension of standard ontology language OWL, is proposed to make the fuzzy spatio‐temporal ontologies easy to use and support efficient reasoning. On the basis of fuzzy spatio‐temporal ontologies, we investigate a formal approach for constructing fuzzy spatio‐temporal ontologies from fuzzy spatio‐temporal Petri nets, ie, transforming fuzzy spatio‐temporal Petri nets (including Petri net model and Petri net instance) into fuzzy spatio‐temporal ontologies at both structure and instance levels. Finally, we prove the correctness of the transformation and provide a detailed transformation example.

Automatic Construction of OWL Ontologies From Petri Nets

Ontology, as a formal representation method of domain knowledge, plays a particular important key role in semantic web. How to construct ontologies has become a key technology in the semantic web, especially constructing ontologies from existing domain knowledge. Currently, Petri nets have been a mathematical modeling tool, and have been widely studied and successfully applied in modeling of software engineering, database and artificial intelligence. In particular, PNML (Petri Net Markup Language) language has been a part of ISO/IEC Petri nets standard for representing and exchanging data on Petri nets. Therefore, how to construct ontologies from PNML model of Petri nets needs to be investigated. In this article, the authors investigate a method for automatic construction of web ontology language (OWL) ontologies from PNML of Petri nets. Firstly, this paper gives a formal definition and the semantics of PNML models of Petri nets. On this basis, a formal approach for constructing OWL ontologies from PNML model of Petri nets is proposed, i.e., this paper transforms Petri nets (including PNML model and PNML document of the Petri nets) into OWL ontologies at both structure and instance levels. Furthermore, the correctness of the transformation is proved. Finally, a prototype construction tool called PN2OWL is developed to transform Petri nets models into OWL ontologies automatically.

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.

Object Calculus in the System–Object Method of Knowledge Representation

In this work, methods of formalization of the Unit–Function–Object system approach and the system–object method of representation of knowledge based on this approach are considered. The expediency of applying some ideas of the object calculation of Abadi–Cardeli and the pattern theory of Grenander for further formalization is substantiated. By analogy with the above algebraic apparatus, the calculus of special objects that represent elements of system–object models, which includes graphic formulism and basic operations with objects, has been developed. It is shown that using the proposed formal-semantic alphabet of special objects, it is possible to simplify the procedure for decomposition of a complex system on the basis of the basic hierarchy of classes of systemic connections. A substantiation of a number of system-wide regularities is given.