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The article presents the method of heterogeneous information available on Internet portal structuring and systematizing. In this paper we would like to present methodology for ontology-based development of knowledge Internet portals providing the content-based access to knowledge and information resources as well as to discuss an experience of exploitation of this methodology, problems that arise...
Representation Learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low dimensional space. There exits two kinds of representation methods for entities in Knowledge Graphs (KGs), including structure-based representation and description-based representation. Most methods represent entities with fact triples of KGs through translating embedding models, which...
An ontology is a taxonomic hierarchy of lexical terms and their syntactic and semantic relations for representing a framework of structured knowledge. Ontology used to be problem-specific and manually built due to its extreme complexity. Based on the latest advances in cognitive knowledge learning and formal semantic analyses, an Algorithm of Formal Ontology Generation (AFOG) is developed. The methodology...
This paper is a continuation of a previous paper on self-modeling systems, concerning mitigation methods for the Get Stuck Theorems, which are powerful theorems about the limits of knowledge representation. The First Get Stuck Theorem says that since there are only finitely many data structures of any given size, it follows that as a system tries to save more and more data / information / knowledge,...
Development problems of the information systems analysis and design for monitoring the status of large-scale infrastructural objects in the categorical models systems have been considered at this article. The formal apparatus of a multilevel knowledge representation on the basis of the categorical approach, the theory of computational models, knowledge representation production systems was described...
In the education field, most developed systems aim to provide an adapted learning process according to the user's needs. Intelligent tutoring systems (ITSs) are defined as instructional software that use artificial intelligence techniques with knowledge based on cognitive psychology and education to provide personalized learning process. To ensure this goal, the system should respond to the learner's...
Knowledge representation models and automated reasoning algorithms are most important problems in designing knowledge base systems in artificial intelligence, especially in intelligence problem solver (IPS). One of effective models is the COKB (Computational Objects Knowledge Base model), which can be used to represent and to design the knowledge-base of practical intelligent systems. Nevertheless,...
Generated from the urgent demand of innovative teaching methods, the teaching strategy should be developed based on ability development of the students. An approach of designing for a fully instructive and smart system which builds optimizations that helps solve the current planar geometrical problems will be presented in this article. The technical solution for building a helpfully instructive and...
Knowledge models play a very important role for designing knowledge base systems. There are many effective methods for representing such as: rule-based system, computational network, ontology. In fact, a popular form of knowledge domain is knowledge about operations and computational relations, especially in building the intelligent problem solver for computational knowledge domain about mathematics...
Belief change in Probabilistic Graphical Models in general, and Bayesian Networks in particular, is often thought of as change in the model parameters when data consistent with the graphical model is observed. The assumption is the network structure for the graphical model is a true representation of the knowledge about the domain and therefore it does not change. In dynamic environments, this assumption...
In this paper, a fuzzy Petri net model has been proposed based on generalized weighted fuzzy production rule. Generalization has been made on the basis of inclusion of the intuitionistic fuzzy set with the associated intuitionistic fuzzy weight as input values. Such complex model may often give rise to a fuzzy Petri net model based on a complex logical operator resulting in higher computational complexity...
Mobile context-aware computing is an essential component of the smart cities infrastructure. Attempts were made to develop a model that can effectively represent a system in device to support context-aware behavior. The purpose of this paper is to identify deficiencies of the previously developed model and propose solutions to improve it.
Mobile context-aware computing is an essential component of the smart cities infrastructure. Attempts were made to develop a model that can effectively represent a system in device to support context-aware behavior. The purpose of this paper is to identify deficiencies of the previously developed model and propose solutions to improve it.
Commonsense knowledge is considered the basis for machine understanding of natural language semantics. In this paper, we propose a new method for fine-grained commonsense knowledge extraction using dependency graphs built through an unsupervised method. We implement the method on a large-scale text collection, and show how our knowledge model can be applied to develop a generic pronoun resolution...
A key design aspect for virtual learning companions is their believability. A lot of attention has been paid to emotion modeling which is at the core of believability. However, most of the existing emotion models neglect the epistemology-based emotions, which are knowledge-related emotions that affect the human learning process. Studies have shown that curiosity is an important epistemology-based...
“Binary Model of Knowledge” (BMK) is the system of concept-oriented languages intended for specifying and interpreting ontologies with their fact bases (instances). BMK is under development at National Research University “MPEI” (Moscow, Russia) and at Institute of Informational and Computational Technologies (Almaty, Kazakhstan). The BMK languages combine the advantages of frame-based object-oriented...
This paper shows how you can use the System Dynamics methodology for modeling social problems, however in these models as elsewhere, the knowledge acquisition phase becomes to be the bottleneck and affect other phases as the system representation and the decision making phase. An alternative to the acquisition of knowledge is based on expert systems methodologies that use judgments embodied in human...
Representing human knowledge plays an important role in artificial intelligence science. Nowadays, there are many effective methods for representing knowledge such as semantic networks, conceptual graphs, computational networks. Computational Objects Knowledge Base (COKB) can be used to represent knowledge in many kinds of knowledge domain, such as Linear Algebra, Analytic Geometry, Direct Current...
Intelligent problem solvers must have suitable knowledge base used by the inference engine to solve problems in certain knowledge domain, and they can solve problems in general forms. Knowledge representation models should be convenient for designing the knowledge base, inference engine and interface of the system. This article presents a design method using the model of computational objects knowledge...
Labor productivity is a fundamental building block of planning and controlling in construction, and therefore, predicting labor productivity levels for a given condition is very important in construction management. However, predicting labor productivity is extremely difficult due to a large number of factors that can affect productivity in perplexing ways. Another obstacle to predicting labor productivity...
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