Means Of Knowledge Representation Research Articles

E-Science workflow: A semantic approach for airborne pollen prediction

Allergic rhinitis has become a global health problem in recent decades because airborne pollen is a primary trigger of this respiratory disorder. Moreover, pollinosis can exacerbate the symptoms of asthma and favour respiratory infections. Seasonal pollen trends and climatic circumstances (such as temperature, precipitation, relative humidity, wind speed and direction, and other variables) can impact daily airborne pollen concentrations, influencing local pollen emission and dispersion. Because of that, pollen monitoring and prediction are becoming more relevant to the urban population and scientific interest is put into them. Due to such tasks’ high volume of data, scientists are starting to use computational tools like workflows to automate and speed up the process. Furthermore, using the expert scientific domain is critical for improving the analysis, allowing, among others, a better workflow configuration and data provenance. As semantic web technologies have been revealed as an essential means for knowledge representation, we implemented this workflow information as an ontology using formats like RDF(S) and OWL. Consequently, this paper provides a semantic-enhanced e-Science workflow based on the TITAN framework for pollen forecasting analysis using meteorological data. Furthermore, a catalogue of components is developed on the TITAN framework, which allows the creation of different workflow versions. Two case studies of pollen prediction were developed to test the implementation of the aforementioned methodologies. Both were elaborated with airborne pollen data obtained in the city of Málaga (Spain). Still, one was elaborated for Platanus pollen type (narrow annual main pollination period), while the other was done for Amaranthaceae pollen type (extensive annual main pollination period). The predictions have been conducted using machine and deep learning algorithms like SARIMA or CNN-LSTM that intend to optimise the pollen prediction procedure depending on its stational and seasonal profile.

Multimodal perceptual image and linguistic means of its manifestation in an English-language literary text

The article discusses the problem of multimodal perceptual image in the literary text as a language means of conveying the interaction and reciprocity of perceptual sensations obtained through several senses and manifesting an integral multimodal image. The language manifestation of the multimodality of perception is understood as a way to express in a language the results of the perception by several senses at the same time. The purpose of the article is to identify language means of manifestation of the processes of multimodal perception in a literary text. The study is carried out from the perspective of cognitive stylistics, i.e. considering stylistic devices that manifest multimodal perception as a means of knowledge representation. «The Pebble in the Sky» by A. Azimov served as an example of a literary text characterized by an abundance of perceptual vocabulary per text unit. The component analysis, lexical definition analysis, contextual analysis and linguo-stylistic analysis result in the classification of perceptual vocabulary and stylistic means manifesting multimodal perceptual image.

Research on Moral Education Function of Music Art in College Students Based on Bayesian Learning Algorithm

While paying attention to students’ acquisition of knowledge, we should also pay attention to students’ ideological and moral education. Bayesian network is a probabilistic graphical model that was developed in the 1980s. Therefore, it is urgent to establish the correct outlook on life and values of the motherland in the future. Music is one of the main categories of aesthetic education, which not only plays a role in cultivating students’ talents and skills, but also has the function of moral education, so it has its special teaching position. It provides the means of knowledge representation, reasoning, and learning in uncertain environment. Based on Bayesian learning algorithm, this paper studies the moral education function of music art in college students. Bayesian network can effectively carry out multivariate joint prediction, causal reasoning, expression of uncertain knowledge, pattern recognition, image processing, and causal data mining. Among them, the method based on score search regards structural learning as a combinatorial optimization problem, and score function and search method are two important factors that affect the learning effect.

Existence, Hypotheses and Categories in Knowledge Representation

Cognitive architectures employ different means for knowledge representation. In this work, we describe how the Cognitive Systems Toolkit (CST), a toolkit for the construction of cognitive architectures addresses the issue of knowledge representation, by introducing the notion of a computational idea, as being an abstract and generic building block for representing multiple different pieces of knowledge. We particularly address how computational ideas can be used to represent both facts that really happened at an environment and just hypothesis that are not to be considered as being a part of existence, explaining how these are instances of general categories. At the end, we provide different examples to illustrate the subtle differences that are possible to be represented using this knowledge representation scheme.

Optimization of Decision Trees with Hypotheses for Knowledge Representation

In this paper, we consider decision trees that use two types of queries: queries based on one attribute each and queries based on hypotheses about values of all attributes. Such decision trees are similar to the ones studied in exact learning, where membership and equivalence queries are allowed. We present dynamic programming algorithms for minimization of the depth and number of nodes of above decision trees and discuss results of computer experiments on various data sets and randomly generated Boolean functions. Decision trees with hypotheses generally have less complexity, i.e., they are more understandable and more suitable as a means for knowledge representation.

Abductive Inference Method in Problems of Explaining the Observed

The problems of artificial intelligence, control and decision-making with incomplete or unreliable information include a wide class of problems of abductive explanation of the observed, including cause–effect problems. The study is devoted to the substantiation of the method of logical formation of hypotheses explaining the observed. Means of knowledge representation and derivation of hypotheses are proposed. A language possessing the property of substitutability is introduced. The properties of language and the calculi introduced in it provide hypothesizing by combining deduction and abduction. In contrast to the well-known logical methods of abduction, the proposed techniques make it possible to derive hypotheses (minorants) that are necessary and sufficient for a formal explanation of the observed. Based on minorants in combination with the basic theory of the subject area, reliable causes of the observed or relevant circumstances leading to these causes are formed. In this case, in situations with the availability of empirical data, these causes and circumstances can also be formed in plausible versions. Examples from technology and medicine are considered.

Применение фреймовой технологии в учебных пособиях по развитию навыков чтения

We reveal means of knowledge representation (frame, script and plan) and M. Minskiy’s hierarchical structure of the frame. It highlights different means of applying the frame theory to the native pedagogics as a frame pedagogical technology which enables students to enlarge the volume of knowledge under study without enlarging the volume of time spent on it; as a way of arranging the vocabulary necessary for a text comprehension and as a way of compiling a student’s dictionary. We offer a hyperframe model “A Manual for Master’s Degree Students in International Relations”. We analyze stereotypical and structural lexical and semantic features of mass media texts included into this hyperframe. We focus on the stereotypical structure of exercises and tasks circulating in the hyperframe: pre-text, text and after-text. The article stresses the importance of stereotypical tasks in each module, like “Agree/Disagree” and “Discussion Points” in order to further develop the soft skills of the 21st century. We offer Quizlet as a way of electronic means of learning new vocabulary and prove the necessity of including tasks aimed at the development of academic writing. The study also points out the pedagogical technology “Flipped Classroom” as one of the methods of working with the textbooks for Reading.

An AI Using Construction Grammar to Understand Text

This paper describes an AI that uses construction grammar (CG)—a means of knowledge representation for deep understanding of text. The proposed improvements aim at more versatility of the text form and meaning knowledge structure, as well as for intelligent choosing among possible parses. Along with the improvements, computational CG techniques that form the implementation basis are explained. Evaluation experiments utilize a Winograd schema (WS)—a major test for AI—dataset and compare the implementation with state-of-the-art ones. Results have demonstrated that compared with such techniques as deep learning, the proposed CG approach has a higher potential for the task of anaphora resolution involving deep understanding of the natural language.

Способы представления знаний в наивной риторике (по данным паремиологического фонда русского языка)

The paper aims to describe language means of knowledge representation in naïve rhetoric. Scientific originality of the research involves identifying specificity of naïve rhetorical knowledge in comparison with scientific rhetoric. As a result of the study, the following distinctive features of naïve rhetorical knowledge are revealed: 1) prescriptiveness when transferring traditional knowledge, which is typical of proverbs; 2) untargeted verbal message; 3) use of prescriptive statements with evaluative predicates; 4) diversity of expressive means (sayings, antonyms, adverbial modifiers of manner, etc.) clarifying the information being transferred.

Representation of Knowledge by Decision Trees for Decision Tables with Multiple Decisions

Abstract In this paper, we study decisions trees for decision tables with multiple decisions as a means for knowledge representation. To this end, we consider three methods to design decision trees and evaluate the number of nodes, and local and global misclassification rates of constructed trees. The considered methods are based on a dynamic programming algorithm for bi-objective optimization of decision trees. The goal of this study is to construct trees with reasonable number of nodes and at the same time reasonable accuracy. Previously, it was mentioned that the consideration of only the global misclassification rate of the decision tree is not enough and it is necessary to study also the local misclassification rate. The reason is that even if the global misclassification rate related to the whole tree is enough small, the local misclassification rate related to the terminal nodes of the tree can be too big. One of the considered methods allows us to construct the decision trees with moderate number of nodes as well as moderate global and local misclassification rates. These decision trees can be used for the knowledge representation.

USE OF ONTOLOGICAL ANALYSIS FOR CREATION OF ENCYCLOPEDIC PORTALS

We analyze the expediency of the development of the intelligent, semantically structured information Web resources that are oriented both on their use by humans and their suitability for automated analysis. The advantages and disadvantages of the Wiki technology and the perspectives of its semantic expansion with the use of ontological analysis are considered. Formal ontological model of the Wiki-resource that underlies the development of the user task ontology oriented for utilization by external intelligent applications (for example, for semantic search) is proposed. The main stages of construction of this ontology are described. The implementation of the proposed ontology-based approach is demonstrated on example of the development of on-line encyclopaedias combining free access to materials through the Web with a high level confidence in content developed by experts, is considered. We analyze the basic requirements for software that provides such projects. The necessity of semantization of such resources is substantiated and the main directions of development of the functional of semantic electronic encyclopedias are formulated. The article presents an analysis of the expressive power of the Wiki means for knowledge representation and processing. The need to expand them with artificial intelligence methods and Semantic Web technologies is stated. An ontological model of the encyclopedia serving for development of the portal version of the Great Ukrainian Encyclopedia is built. The main categories and semantic properties of typical information objects that are used in this Encyclopedia that can be applied for retrieval and integration of information are described.

H-KaaS: A Knowledge-as-a-Service architecture for E-health

Due to the need to improve access to knowledge and the establishment of means for sharing and organizing data in the health area, this research proposes an architecture based on the paradigm of Knowledge-as-a-Service (KaaS). This can be used in the medical field and can offer centralized access to ontologies and other means of knowledge representation. In this paper, a detailed description of each part of the architecture and its implementation was made, highlighting its main features and interfaces. In addition, a communication protocol was specified and used between the knowledge consumer and the knowledge service provider. Thus, the development of this research contributed to the creation of a new architecture, called H-KaaS, which established itself as a platform capable of managing multiple data sources and knowledge models, centralizing access through an easily adaptable API.

ENUMERATION AS A MEANS OF FINANCIAL MARKETS DATA REPRESENTATION

Cognitive linguistics has developed into one of the most attractive and influential frameworks within linguistics at large. And it draws the linguists’ attention to the study of aspects of language within cognitive paradigm as reflections of conceptual organization and categorization principles, as well as the ways of presenting knowledge about the world. This paper focuses on the cognitive analysis of enumeration as an instrument for organizing and conveying domain-specific information. The aim of the analysis is to establish the role of enumeration as a way of arriving at true knowledge of the world. The tasks of the research consist in the description of enumeration as a means of knowledge representation, as well as the study of the peculiarities of enumeration as a means of representation of financial markets information. The novelty of the analysis is defined by the study of enumeration as a means of representation of financial markets information. The theoretical value of the analysis is reasoned with the study of enumeration as a way of organizing knowledge that reflects collective experience of the community within a specific environment. The results of the study of enumeration in economic discourse show that it serves to structure and represent domain-specific knowledge.

What does knowledge look like? Drawing as a means of knowledge representation and knowledge construction

What does knowledge look like? Drawing as a means of knowledge representation and knowledge construction

Ontological Engineering Approach Towards Botnet Detection in Network Forensics

The abundance in the usage of Internet, in every arena of life from social to personal, commercial to domestic and other aspects of life as well, leads the rise in cybercrime at an upsetting speed. More illegal activities as a result of cyber crime, reason to tempts many network attacks and threats. Network forensics is the branch of fornesics that deals in the detection of network attacks. Botnet is one of the most common attacks, but hazardos. It  is a network of hacked computers It  involves the capturing, storing and then analysis of the network packets, in order to identify the source of the attack.  Various methods based on this approach for botnet detection are suggested in literature but there is no generalized method to represent the basic methodology used by any of the botnet detection method. With such guidelines, the comparison among the various implementations, a roadmap for the new implementation, development of reusable implementations can be addressed. Accordingly, there is a requirement of a generic framework that can characterize the general methodology followed by any of the botnet detection methods. This paper, review various prevalent methods of botnet detection to extract commonalities among them. A global model for the detection of botnets is represented as ontology. Ontology is used as a means of knowledge representation. The botnet ontology is represented using Web Ontology Language (OWL). OWL is used because it is a language with layered architecture and high expressive power.Â

Medieval Representations of Change and Their Early Modern Application

The article investigates the role of symbolic means of knowledge representation in concept development using the historical example of medieval diagrams of change employed in early modern work on the motion of fall. The parallel cases of Galileo Galilei, Thomas Harriot, and Rene Descartes and Isaac Beeckman are discussed. It is argued that the similarities concerning the achievements as well as the shortcomings of their respective work on the motion of fall can to a large extent be attributed to their shared use of means of knowledge representation handed down from antiquity and the Middle Ages. While the interpretation of medieval diagrams was unproblematic in the scholastic context from which they arose, in the early modern context, which was characterized by the confluence of natural philosophy and practical mathematics, it became ambiguous. It was the early modern mathematicians’ work within this contradictory framework that brought about a new conceptualization of motion which, in particular, eventually led to an infinitesimal concept of velocity. In this process, the diagrams themselves remained largely unchanged and thus functioned as a catalyst for concept development.

Personal ontologies: Generation of user profiles based on the YAGO ontology

Personalized search is aimed at tailoring the search outcome to users; to this aim user profiles play an important role: the more faithfully a user profile represents the user interests and preferences, the higher is the probability to improve the search process.In the approaches proposed in the literature, user profiles are formally represented as bags of words, as vectors, or as conceptual taxonomies, generally defined based on external knowledge resources (such as the WordNet and the ODP – Open Directory Project). Ontologies have been more recently considered as a powerful expressive means for knowledge representation. The advantage offered by ontological languages is that they allow a more structured and expressive knowledge representation with respect to the above mentioned approaches. A challenging research activity consists in defining user profiles by a knowledge extraction process from an existing ontology, with the main aim of producing a semantically rich representation of the user interests.In this paper a method to automatically define a personal ontology via a knowledge extraction process from the general purpose ontology YAGO is presented; starting from a set of keywords, which are representatives of the user interests, the process is aimed to define a structured and semantically coherent representation of the user topical interests. In the paper the proposed method is described, as well as some evaluations that show its effectiveness.

A Knowledge Engineering Approach to Develop Domain Ontology

Ontologies are one of the most popular and widespread means of knowledge representation and reuse. A few research groups have proposed a series of methodologies for developing their own standard ontologies. However, because this ontological construction concerns special fields, there is no standard method to build domain ontology. In this paper, based on discussing and analyzing representative ontology building methodologies, the authors propose a knowledge engineering approach to build domain ontology by combining software development life cycle standard IEEE 1074-2006 with design ontology criteria proposed by T. R. Gruber. The authors use the ontology editor Hozo to develop a marine biology ontology for an e-learning course. They verify the validity and rationality of marine biology ontology by applying it to a practical system called OASIS. The authors then demonstrate the applicability of their proposed knowledge engineering approach.

Representation of defaults and constraints in EHCPRs system: an implementation

A knowledge representation (KR) language using constraints programming technique is considered. Each knowledge base structuring scheme has merits and demerits, each should be applied for the purpose it best serves, the prime focus being on creating well-grained and efficient KR. The use of the object-oriented approach makes it possible to unify various means of KR such as frames, semantic networks, and production rules as well as methods of constraint programming within the framework of one language. In this paper issues related to representation and reasoning with defaults and constraints in the EHCPRs system and its suitable implementation are taken up.

Automatic ontology creation using adaptation

AbstractOntologies are an emerging means of knowledge representation to improve information organization and management, and they are becoming more prevalent in the domain of engineering design. The task of creating new ontologies manually is not only tedious and cumbersome but also time consuming and expensive. Research aimed at addressing these problems in creating ontologies has investigated methods of automating ontology reuse mainly by extracting smaller application ontologies from larger, more general purpose ontologies. Motivated by the wide variety of existing learning algorithms, this paper describes a new approach focused on the reuse of domain-specific ontologies. The approach integrates existing software tools for natural language processing with new algorithms for pruning concepts not relevant to the new domain and extending the pruned ontology by adding relevant concepts. The approach is assessed experimentally by automatically adapting a design rationale ontology for the software engineering domain to a new one for the related domain of engineering design. The experiment produced an ontology that exhibits comparable quality to previous attempts to automate ontology creation as measured by standard content performance metrics such as coverage, accuracy, precision, and recall. However, further analysis of the ontology suggests that the automated approach should be augmented with recommendations presented to a domain expert who monitors the pruning and extending processes in order to improve the structure of the ontology.