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Comparison of the nodes of knowledge method with other graphical methods for knowledge representation
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One of the research paths in the field of artificial intelligence is knowledge representation. There are different approaches, formalisms, methods and languages. They vary from simple to complex and from less semantically rich to very expressive. In their previous papers, the authors introduced a new method for knowledge representation named Nodes of Knowledge (NOK), bearing the idea that it should be simple but semantically rich. This article presents a brief example of the basic concepts in the NOK method and its comparison with the following methods: Basic Conceptual Graphs, Multi-layered extended semantic networks, Hierarchical Semantic Form and Resource Description Framework. All these methods belong to the same class as the NOK method - graphical methods for knowledge representation.
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... For further research we plan to experiment with the NOK method [32] or some other graph based formalism for lexical relation representation [33]. Additionally, we would like to experiment with the approach that we try with an ontology-based information retrieval in which the classical VSM is projected onto a smaller vector space [34]. ...
Academic plagiarism is a serious problem nowadays. Due to the existence of inexhaustible sources of digital information, today it is easier to plagiarize more than ever before. The good thing is that plagiarism detection techniques have improved and are powerful enough to detect attempts of plagiarism in education. We are now witnessing efficient plagiarism detection software in action, such as Turnitin, iThenticate or SafeAssign. In the introduction we explore software that is used within the Croatian academic community for plagiarism detection in universities and/or in scientific journals. The question is: is this enough? Current software has proven to be successful, however the problem of identifying paraphrasing or obfuscation plagiarism remains unresolved. In this paper we present a report of how semantic similarity measures can be used in the plagiarism detection task.
... In addition to NOK, several formalisms have been developed: Diagram Node of Knowledge (DNOK)-formalism for graphical representation [15,16], Formalized Node of Knowledge (FNOK)-formalism for textual knowledge representation [17,18] and QFNOK-formalism for question representation. ...
The Node of Knowledge (NOK) method is a method for knowledge representation. It is used as a basis for development of formalism for textual knowledge representation (FNOK). FNOK was used for development of a Question Answering (QA) System whose research results are presented in this paper. The QA system is based on storing text in relational databases without losing semantics and it is implemented in Oracle. In this paper, we present the results of preliminary evaluation of the QA system for its independency on the language used. The system was tested using the same set of sentences and questions translated in English, German, Italian and Croatian language. Knowledge in the system was based on 26 simple sentences. 44 questions were set, and the system offered 62 answers for each language (some questions had more than one answer). Research results have shown that this QA system can be used for any of the four languages without the need to change functions and algorithms. It is only necessary to set the dictionary data for each language, implement synonyms and recognize different word forms.
... The Nodes of Knowledge (NOK) method [4], [5] belongs to semantic networks. Node of Knowledge also uses nodes and links, but is simpler than other methods (having fewer elements), more expressive (allows to display knowledge at different levels of abstraction) and easier to read (in the NOK method it is possible to read the knowledge starting from any node, but with the use of the link role) [6]. ...
... For further research we plan to experiment with the NOK method [32] or some other graph based formalism for lexical relation representation [33]. Additionally, we would like to experiment with the approach that we try with an ontology-based information retrieval in which the classical VSM is projected onto a smaller vector space [34]. ...
The methodology and practical solutions for managing students' scientific and educational activities on the basis of ontological management of the educational process are described. The need for the creation and application of a modern transdisciplinary ontologically managed system of monitoring and evaluation of school youth activities, which should provide the following: identification of gifted youth from all regions of Ukraine, is determined. The factors and requirements that characterize the monitoring and rating system for displaying students' scientific and educational activity are determined. The characteristics of software tools and a formal model that implements the display of a particular subject area are given. The mechanisms for effective processing, generalization and use of available information on students' scientific and educational activity are presented. The ontological model of a student is defined as the main one, which is functionally capable of providing a full-scale representation of data on students, schools, regions, competition areas, sections, as well as various attributes in the form of a taxonomic hierarchical structure based on formal concepts and relationships between them. Such a model can greatly facilitate the procedures for updating and entering new information from various sources, its validation and automatic processing, while maintaining the ability to monitor and apply analytical queries. It is determined that the creation of ontology-based systems has significant advantages both in the development process and in their practical application. The system of monitoring and evaluation of students' learning achievements, which was created at the National Center “Small Academy of Sciences of Ukraine”, is described. Its characteristics are presented, the implementation of which ensures a holistic, transparent and flexible approach to evaluating the results of various intellectual competitions, including subject Olympiads, competitions for the defense of research papers of the Junior Academy of Sciences and specialized tournaments. A key feature of the system is its ontology management, which allows it to display the multi-level structure of educational events: from the name of the competition and the direction (section) to detailed information about each student. The application of the ontological approach not only simplified the structuring process (searching and collecting the necessary information), but also made it possible to semantically integrate data with additional sources of information.
A conceptual framework Node of knowledge enables, among others, presentation of knowledge in a formalized record suitable for insert into a relational database. To correctly transform all words from natural language sentences to formalized records, it is necessary to design a metamodel of a language and define rules for transformation of each sentence part. This paper focuses on adverbs and prepositions and their representation in Croatian and English language. It presents rules and examples for their transformation into formalized records in both languages and discusses results of preliminary testing of the system with a small set of sentences and questions.
Verbalized text contains knowledge necessary and sufficient for transfer of numerous human cognitions. The question is how this knowledge was saved into text. Authors believe that they found an important idea how to assemble knowledge into nodes of knowledge. Similar methods in the field of knowledge networks exist, but none of them does it in the same way as the Node of Knowledge (NOK) method. Basic terms and concepts in a language are represented in words whose meaning is final and cannot be divided in subterms. More complex meaning can be achieved by combining words in sentences. According to authors’ opinion, each sentence includes connective medium for words in the sentence (related to semantic reasons), which is inwrought in the Node of Knowledge method. Using the Node of Knowledge method each sentence can be presented as a network of connected words. This network is enriched with links between words so a computer can interpret meaning and knowledge of the sentence in the same way an intelligent person does. A formalized and semantically enriched record of sentences (called Formalized Node of Knowledge – FNOK) is developed. Authors find that in this way, even without statistical text analysis, an algorithm can give correct answer to a question set based on written text. This paper presents the system for transformation of textual knowledge expressed in natural language sentences into a relational database. The system is a part of a larger knowledge-based system based on the Node of Knowledge (NOK) conceptual framework for knowledge-based system development. This paper starts with sentences written in the formalized and enriched form for which a logical transformation into the structure of a relational database is proposed. The system and algorithms for the transformation of formalized sentences into n-tuples of the relational database are distributed and represented in several steps. The research has shown that it is possible to store semantically enriched sentences in relational databases. The solution presented in this paper is important for further development of the system for receiving questions from users and providing answers (i.e. question-answering system), with the ability to use well-developed relational SQL languages. Relational database of texts enables numerous applications in the field of expert and intelligent systems.
Software Process Improvement (SPI) models are a very important topic for SPI workers as software engineering students that require a good comprehension of the process assessment models. This paper proposes a visual multi-layered representational model describing in a highly practical way the ISO/IEC 33000 Assessment Framework through of structural and behavioural views. This kind of representation comprises several semantic layers based on the dimensions of the key elements of the model with an additional dimension for specifying measurements. The structural elements of the model are assigned on distinct layers and connected through dependence and co-occurrence connector in applying the Assessment Model on software processes. The representation model was tested with software developers, academic experts and students.
This paper reports on the ongoing effort in building an RDF ontology for the de-facto standard conceptual model for library catalogs. We motivate our work by a concrete real world application and demonstrate how using the CoGui Conceptual Graphs ontology editor will highly benefit the task.
In Artificial Intelligence, knowledge representation studies the formalisation of knowledge and its processing within machines. Techniques of automated reasoning allow a computer system to draw conclusions from knowledge represented in a machine-interpretable form. Recently, ontologies have evolved in computer science as computational artefacts to provide computer systems with a conceptual yet computational model of a particular domain of interest. In this way, computer systems can base decisions on reasoning about domain knowledge, similar to humans. This chapter gives an overview on basic knowledge representation aspects and on ontologies as used within computer systems. After introducing ontologies in terms of their appearance, usage and classification, it addresses concrete ontology languages that are particularly important in the context of the Semantic Web. The most recent and predominant ontology languages and formalisms are presented in relation to each other and a selection of them is discussed in more detail.
In Artificial Intelligence, knowledge representation studies the formalisation of knowl- edge and its processing within machines. Techniques of automated reasoning allow a computer sys- tem to draw conclusions from knowledge represented in a machine-interpretable form. Recently, ontologies have evolved in computer science as computational artefacts to provide computer systems with a conceptual yet computational model of a particular domain of interest. In this way, computer systems can base decisions on reasoning about domain knowledge, similar to humans. This chapter gives an overview on basic knowledge representation aspects and on ontologies as used within com- puter systems. After introducing ontologies in terms of their appearance, usage and classification, it addresses concrete ontology languages that are particularly important in the context of the Semantic Web. The most recent and predominant ontology languages and formalisms are presented in relation to each other and a selection of them is discussed in more detail.
Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs.
The model presented in this talk is a computational model. It aims at representing knowledge by computational objects and at reasoning with the represented knowledge, i.e., at processing them by algorithms, hence philosophical or psychological aspects of knowledge will not be discussed. We first present the main properties a knowledge representation formalism should have and briefly survey graph or graphical models (in Computer Science). Then, we detail the model itself which is graph-based in the following sense: knowledge is represented by labeled graphs and reasoning mechanisms are based on graph operations. The third part is devoted to the relationships of this model with logics and other computational models, especially the relational data base model and RDF/S. Finally, some tools and applications are mentioned.
Hermann Helbig
Knowledge Representation and the Semantics of Natural Language
Cognitive Technologies Series
ISBN 3-540-24461-1
Overview
The book presents an interdisciplinary approach to knowledge
representation and the treatment of semantic phenomena of natural
language, which is positioned between artificial intelligence,
computational linguistics, and cognitive psychology. The proposed method is based on the Multilayered Extended Semantic Network (MultiNet), which can be used for theoretical investigations
into the semantics of natural language, for cognitive modeling, for describing lexical entries in a computational lexicon, and for natural language processing (NLP).
Part I deals with fundamental problems of semantic knowledge representation and semantic interpretation of natural language phenomena. Part II provides a systematic description of the
representational means of MultiNet, one of the most comprehensive and thoroughly specified collections of relations and functions used in real NLP applications.
MultiNet is embedded into a system of software tools comprising a workbench for the knowledge engineer, a semantic interpreter translating natural language expressions into formal meaning
structures, and a workbench for the computer lexicographer. The book has been used for courses in artificial intelligence at several universities and is one of the cornerstones for teaching computational
linguistics in the virtual electronic laboratory VILAB.
Key Contents
Part I – Knowledge Representation with MultiNet: Introduction; Historical Roots; Basic Concepts;
Semantic Characterization of Objects;
Semantic Characterization of Situations;
The Comparison of Entities;
The Spatiotemporal Characterization of Entities;
Modality and Negation; Quantification and
Pluralities;
The Role of Layer Information in Semantic Representations;
Relations Between Situations;
Lexicon and Knowledge Representation;
Question Answering and Inferences;
Software
Tools for the Knowledge Engineer, Sample Applications;
Comparison Between MultiNet and Other Semantic Formalizms
Part II – The Representational Means of MultiNet: Overview and
Representational Principles;
Means for Expressing Classification and Stratification; Relations and Functions
Appendices – Table of Abbreviations;
Overview of the Representational Means;
Semantic Templatesfor the Meaning of Relations;
Characterization of Arcs with Regard to Their Knowledge Type;
Classes of Typical Axioms;
Literature; List of Figures; Index
Good knowledge organization is crucial in two cases—in the representation of semantically related information and in the representation of huge quantities of data. The answer to the problem of semantic knowledge representation is given by various knowledge representation techniques used in Artificial Intelligence, while for the representation of large quantities of data, relational databases represent the optimal choice. If we analyze the characteristics important from the knowledge organization point of view, we could say that structure representation provides means for knowledge representation, unique object representation ensures representational efficiency, while representation of bottom-up hierarchies (e.g. indexing scheme in relational databases) brings search efficiency. Furthermore, unique object representation and bottom-up (contextual) hierarchies provide support for semantically organized knowledge. In this paper we describe a knowledge representation technique, Hierarchical Semantic Form (HSF), which, together with the Space Of Universal Links (SOUL) algorithm, aims at supporting both of the above mentioned characteristics of well organized knowledge. HSF represents a hybrid solution that enables both structure and context representations, combining the characteristics of classicist approach (symbolic and semantic processing) with the characteristics of connectionist approach (parallelism, learning).