Knowledge based classification systems: Basic issues, a toy system and further prospects

This paper gives a brief description of the being created system of information support for innovation activities in the Republic of Kazakhstan, which is built as an information portal, that provides navigation in the national and global information space through the provision of metadata about information resources, relevant to the user’s task. The corporate knowledge management system is considered as a component of the information infrastructure for supporting innovation. An approach to the management of polythematic knowledge is proposed, envisaging the representation of knowledge, based on the use of classification type languages. In this case, a thematic rubricator is introduced into the ontology model instead of a thesaurus, where each category (rubric) has its own code, name and set of keywords, characterizing its thematic content. The proposed joint use of thematic rubrics of Russian State rubricator of scientific-engineering information and All-Russian institute of scientific and engineering information allows increase the degree of accuracy of the knowledge presentation, as well as take advantage of establishing the associative relations between different classification systems. Along with this, there is maintained the possibility of a verbal knowledge description in terms of keywords, characterizing the content of subject entries and words from the rubrics titles.

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CREATIVE INDUSTRIES: CLASSIFICATION SYSTEMS AND MODELS IN THE CONTEXT OF A CREATIVE ECONOMY OF THE 21ST CENTURY / KŪRYBINĖS INDUSTRIJOS: KLASIFIKAVIMO SISTEMOS IR MODELIAI XXI A. KŪRYBOS EKONOMIKOS KONTEKSTE

Mokslas - Lietuvos ateitis ◽

10.3846/mla.2011.065 ◽

2011 ◽

Vol 3 (4) ◽

pp. 21-27

Author(s):

Rasa Levickaitė

Keyword(s):

21St Century ◽

Interdisciplinary Research ◽

Concentric Circle ◽

Creative Industries ◽

Classification Systems ◽

Creative Economy ◽

Knowledge Based ◽

Production Cycles ◽

Text Model ◽

Circle Model

The article discusses a concept of creative industries, looks at its definition varying in different countries and presents a phenomenon of a creative economy. In the context of the creative economy of the 21st century, the paper deals with classification systems and models of creative industries. Theoretical principals are based on the UNCTAD analysis of the creative economy. Creative industries are based on the production cycles of creative content that applies to creativity and intellectual property which is knowledge based activities covering both tangible products and an intangible creation of intellectual or creative services. According to the UNCTAD classification system, four groups of creative industries, including heritage, arts, media, and functional production can be distinguished and subdivided into subgroups. Research on worldwide creative industries refers to four models: UK DCMS model, symbolic text model, concentric circle model and WIPO copyright model. While appealing to the above mentioned models, classification systems with the output of complex interdisciplinary research might be designed.

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Review of Biomedical Knowledge and Data Representation with Conceptual Graphs

Methods of Information in Medicine ◽

10.1055/s-0038-1634504 ◽

1998 ◽

Vol 37 (01) ◽

pp. 86-96 ◽

Cited By ~ 12

Author(s):

M. Joubert ◽

M. Fieschi ◽

F. Volot

Keyword(s):

Data Representation ◽

Classification Systems ◽

Conceptual Graphs ◽

Biomedical Data ◽

Biomedical Knowledge ◽

Language Understanding ◽

Concept Representation ◽

Semantic Relationships ◽

Knowledge Based ◽

Conceptual Graph

Abstract:The basis of conceptual graphs theory is an ontology of types of concepts. Concepts issued from the ontology are interlinked by semantic relationships and constitute canonical conceptual graphs. Canonical graphs may be combined to derive new conceptual graphs by means of formation rules. This formalism allows to separate knowledge representation into a conceptual level and a domain-dependent level, and enables to share and reuse a representation. This paper presents conceptual graph applications to biomedical data and concept representation, classification systems, information retrieval, and natural language understanding and processing. A discussion on the unifying role conceptual graphs theory plays in the implementation of knowledge-based systems is also presented.

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Use and Management of Classification Systems for Knowledge-Based Indexing

Advances in Classification Research Online ◽

10.7152/acro.v2i1.12547 ◽

1991 ◽

Vol 2 (1) ◽

Author(s):

Susanne M. Humphrey

Keyword(s):

Classification Systems ◽

Knowledge Based

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Analyzing Newspaper Articles for Text Related data for finding vulnerable Posts over the Internet that are linked to Terrorist activities

International Journal of Information Security and Privacy ◽

10.4018/ijisp.285581 ◽

2022 ◽

Vol 16 (1) ◽

pp. 0-0

Keyword(s):

Classification Systems ◽

Natural Languages ◽

Suggested Approach ◽

Word Similarity ◽

Useful Knowledge ◽

Related Data ◽

Knowledge Based ◽

Related Information ◽

N Gram ◽

Experimental Findings

One of the most critical activities of revealing terrorism-related information is classifying online documents.The internet provides consumers with a variety of useful knowledge, and the volume of web material is increasingly growing. This makes finding potentially hazardous records incredibly difficult. To define the contents, merely extracting keywords from records is inadequate. Many methods have been studied so far to develop automatic document classification systems, they are mainly computational and knowledge-based approaches. due to the complexities of natural languages, these approaches do not provide sufficient results. To fix this shortcoming, we given approach of structure dependent on the WordNet hierarchy and the frequency of n-gram data that employs word similarity. Using four different queries terms from four different regions, this approach was checked for the NY Times articles that were sampled. Our suggested approach successfully removes background words and phrases from the document recognizes connected to terrorism texts, according to experimental findings.

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Approximate Reasoning in Supervised Classification Systems

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2006.p0586 ◽

2006 ◽

Vol 10 (4) ◽

pp. 586-593 ◽

Cited By ~ 1

Author(s):

Hamid Seridi ◽

◽

Herman Akdag ◽

Rachid Mansouri ◽

Mohamed Nemissi ◽

...

Keyword(s):

Modus Ponens ◽

Classification Systems ◽

Approximate Reasoning ◽

Production Rules ◽

Training Set ◽

Inference System ◽

Degree Of Belief ◽

Knowledge Based ◽

Symbolic Approach ◽

Iris Data

In knowledge-based systems, uncertainty in propositions can be represented by various degrees of belief encoded by numerical or symbolic values. The use of symbolic values is necessary in areas where the exact numerical values associated with a fact are unknown by experts. In this paper we present an expert system of supervised automatic classification based on a symbolic approach. This last is composed of two sub-systems. The first sub-system automatically generates the production rules using training set; the generated rules are accompanied by a symbolic degree of belief which characterizes their classes of memberships. The second is the inference system, which receives in entry the base of rules and the object to classify. Using classical reasoning (Modus Ponens), the inference system provides the membership class of this object with a certain symbolic degree of belief. Methods to evaluate the degree of belief are numerous but they are often tarnished with uncertainty. To appreciate the performances of our symbolic approach, tests are made on the Iris data basis.

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Recommendation and Classification Systems: A Systematic Mapping Study

Scientific Programming ◽

10.1155/2019/8043905 ◽

2019 ◽

Vol 2019 ◽

pp. 1-18

Author(s):

J. G. Enríquez ◽

L. Morales-Trujillo ◽

Fernando Calle-Alonso ◽

F. J. Domínguez-Mayo ◽

J. M. Lucas-Rodríguez

Keyword(s):

Early Stage ◽

Open Data ◽

Optimal Solution ◽

Heterogeneous Data ◽

Point Of View ◽

Classification Systems ◽

Systematic Mapping Study ◽

Industrial Environment ◽

Knowledge Based ◽

Clear Vision

Today, recommendation algorithms are widely used by companies in multiple sectors with the aim of increasing their profits or offering a more specialized service to their customers. Moreover, there are countless applications in which classification algorithms are used, seeking to find patterns that are difficult for people to detect or whose detection cost is very high. Sometimes, it is necessary to use a mixture of both algorithms to give an optimal solution to a problem. This is the case of the ADAGIO, a R&D project that combines machine learning (ML) strategies from heterogeneous data sources to generate valuable knowledge based on the available open data. In order to support the ADAGIO project requirements, the main objective of this paper is to provide a clear vision of the existing classification and recommendation ML systems to help researchers and practitioners to choose the best option. To achieve this goal, this work presents a systematic review applied in two contexts: scientific and industrial. More than a thousand papers have been analyzed resulting in 80 primary studies. Conclusions show that the combination of these two algorithms (classification and recommendation) is not very used in practice. In fact, the validation presented for both cases is very scarce in the industrial environment. From the point of view of software development life cycle, this review also shows that the work being done in the ML (for classification and recommendation) research and industrial environment is far from earlier stages such as business requirements and analysis. This makes it very difficult to find efficient and effective solutions that support real business needs from an early stage. It is therefore that the article suggests the development of new ML research lines to facilitate its application in the different domains.

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Is the Personal Always Political?

Journal of Individual Differences ◽

10.1027/1614-0001/a000230 ◽

2017 ◽

Vol 38 (3) ◽

pp. 133-143 ◽

Cited By ~ 4

Author(s):

Danny Osborne ◽

Yannick Dufresne ◽

Gregory Eady ◽

Jennifer Lees-Marshment ◽

Cliff van der Linden

Keyword(s):

Survey Data ◽

National Survey ◽

Negative Relationship ◽

Openness To Experience ◽

Political Sophistication ◽

Knowledge Based

Abstract. Research demonstrates that the negative relationship between Openness to Experience and conservatism is heightened among the informed. We extend this literature using national survey data (Study 1; N = 13,203) and data from students (Study 2; N = 311). As predicted, education – a correlate of political sophistication – strengthened the negative relationship between Openness and conservatism (Study 1). Study 2 employed a knowledge-based measure of political sophistication to show that the Openness × Political Sophistication interaction was restricted to the Openness aspect of Openness. These studies demonstrate that knowledge helps people align their ideology with their personality, but that the Openness × Political Sophistication interaction is specific to one aspect of Openness – nuances that are overlooked in the literature.

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