scholarly journals The Auto-Diagnosis of Granulation of Information Retrieval on the Web

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 264
Author(s):  
Anna Bryniarska
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Semantic Web ◽  
Fuzzy Sets ◽  
Triangular Norm ◽  
Information Granules ◽  
Logical Operations ◽  
The Relationship ◽  
Operations Systems ◽  
The Web

In this paper, a postulation on the relationship between the memory structure of the brain’s neural network and the representation of information granules in the semantic web is presented. In order to show this connection, abstract operations of inducing information granules are proposed to be used for the proposed logical operations systems, hereinafter referred to as: analysis, reduction, deduction and synthesis. Firstly, the searched information is compared with the information represented by the thesaurus, which is equivalent to the auto-diagnosis of this system. Secondly, triangular norm systems (information perception systems) are built for fuzzy or vague information. These are fuzzy sets. The introduced logical operations and their logical values, denoted as problematic, hypothetical, validity and decidability, are interpreted in these fuzzy sets. In this way, the granularity of the information retrieval on the Web is determined according to the type of reasoning.

Using Semantic Web Concepts to Retrieve Specific Domain Information from the Web

2011 ◽  
pp. 249-270
Author(s):  
Rafael Cunha Cardoso ◽  
Fernando da Fonseca de Souza ◽  
Ana Carolina Salgado
Keyword(s):  
Information Retrieval ◽  
World Wide Web ◽  
Semantic Web ◽  
Web Sites ◽  
World Wide ◽  
Specific Domain ◽  
New Concepts ◽  
The World ◽  
Domain Information ◽  
The Web

Currently, systems dedicated to information retrieval/extraction perform an important role on fetching relevant and qualified information from the World Wide Web (WWW). The Semantic Web can be described as the Web’s future once it introduces a set of new concepts and tools. For instance, ontology is used to insert knowledge into contents of the current WWW to give meaning to such contents. This allows software agents to better understand the Web’s content meaning so that such agents can execute more complex and useful tasks to users. This work introduces an architecture that uses some Semantic Web concepts allied to Regular Expressions (REGEX) in order to develop a system that retrieves/extracts specific domain information from the Web. A prototype, based on such architecture, was developed to find information about offers announced on supermarkets Web sites.

scholarly journals Analytical review on information retrieval methods and applications in the Semantic Web

2016 ◽  
pp. 051-072
Author(s):  
I.J. Grishanova ◽  
Keyword(s):  
Information Retrieval ◽  
Semantic Web ◽  
Search Engines ◽  
Search Model ◽  
Architectural Features ◽  
Proposed Model ◽  
Analytical Review ◽  
Information Retrieval Methods ◽  
The Web

The article describes and analyzes the Information Retrieval (IR) methods and applications in the environment of Semantic Web. The author provided the basic Information Retrieval concepts, problems, models and classification of IR systems on various grounds. Examples of existing modern search engines, as well as highlighted the stages of development and listed a list of functional and architectural features of 3-rd search engines generation. The proposed model of IR extends the classification of search engines and search model with the possibility of finding new objects that have become available in the web, and use knowledge represented in the Semantic Web.

scholarly journals O dispositivo como unidade básica do conhecimento na web semântica | The dispositif as the basic unit of knowledge in semantic web

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Rodrigo De Santis
Keyword(s):  
Information Retrieval ◽  
Semantic Web ◽  
Knowledge Organization ◽  
Basic Unit ◽  
Knowledge Organization System ◽  
Data Volume ◽  
Informational Environment ◽  
Historical Origins ◽  
The Web ◽  
Representation Of Knowledge

RESUMO As bases teóricas que sustentam a proposta de elaboração de um sistema de organização do conhecimento capaz de superar as limitações da abordagem dicotômica tradicional podem ser simbolizadas com o deslocamento da representação imagética do conhecimento da árvore para o rizoma. Neste contexto, o presente artigo propõe a adoção da noção filosófica de dispositivo como unidade básica do conhecimento em sistemas orientados pela recuperação. Para tanto, são investigadas as origens históricas desse deslocamento e analisados os seus impactos na web – um ambiente informacional que se torna maior a cada instante, em termos de volume de dados, e mais complexo, no que diz respeito à dispersão e à fragmentação da informação. São discutidos ainda os desafios e possíveis desdobramentos relativos à organização do conhecimento e à recuperação da informação no âmbito da web semântica.Palavras-chave: Sistema de Organização do Conhecimento; Classificação; Recuperação; Conceito.ABSTRACT The theoretical framework that supports the intent of elaborating a knowledge organization system capable of overcoming the limitations of traditional dichotomous approach can be symbolized by the displacement of the visual representation of knowledge from the tree to the rhizome. In this context, the present work proposes the adoption of the philosophical notion of dispositif as the basic unit of knowledge in systems oriented by the retrieval. To achieve this, the historical origins of that displacement were studied and its impacts on the web – an informational environment that becomes larger at each moment, in terms of data volume, and more complex, in terms of dispersion and fragmentation of information – were studied. The work also discusses the challenges and possible developments regarding knowledge organization and information retrieval in the scope of the semantic web.Keywords: Knowledge Organization System; Classification; Recovery; Concept.

Metadata- and Ontology-Based Semantic Web Mining

2008 ◽  
pp. 3531-3556
Author(s):  
Marie Aude Aufaure ◽  
Bénédicte Le Grand ◽  
Michel Soto ◽  
Nacera Bennacer
Keyword(s):  
Information Retrieval ◽  
Web Services ◽  
Semantic Web ◽  
Web Mining ◽  
State Of The Art ◽  
Next Generation ◽  
Research Areas ◽  
Semantic Data ◽  
Open Research ◽  
The Web

The increasing volume of data available on the Web makes information retrieval a tedious and difficult task. The vision of the Semantic Web introduces the next generation of the Web by establishing a layer of machine-understandable data, e.g., for software agents, sophisticated search engines and Web services. The success of the Semantic Web crucially depends on the easy creation, integration and use of semantic data. This chapter is a state-of-the-art review of techniques which could make the Web more “semantic”. Beyond this state-of-the-art, we describe open research areas and we present major current research programs in this domain.

Supervised and Unsupervised Information Granulation

2010 ◽  
pp. 48-68
Author(s):  
Andrzej Bargiela ◽  
Witold Pedrycz
Keyword(s):  
Neural Network ◽  
Fuzzy Sets ◽  
Supervised Learning ◽  
Membership Function ◽  
Fuzzy Set ◽  
Information Granules ◽  
Information Granulation ◽  
Pattern Space ◽  
Efficient Representation ◽  
Improved Performance

In this study, we are concerned with information granulation realized both in supervised and unsupervised mode. Our focus is on the exploitation of the technology of hyperboxes and fuzzy sets as a fundamental conceptual vehicle of information granulation. In case of supervised learning (classification), each class is described by one or more fuzzy hyperboxes defined by their corresponding minimumand maximum vertices and the corresponding hyperbox membership function. Two types of hyperboxes are formed, namely inclusion hyperboxes that contain input patterns belonging to the same class, and exclusion hyperboxes that contain patterns belonging to two or more classes, thus representing contentious areas of the pattern space. With these two types of hyperboxes each class fuzzy set is represented as a union of inclusion hyperboxes of the same class minus a union of exclusion hyperboxes. The subtraction of sets provides for efficient representation of complex topologies of pattern classes without resorting to a large number of small hyperboxes to describe each class. The proposed fuzzy hyperbox classification is compared to the original Min-Max Neural Network and the General Fuzzy Min-Max Neural Network and the origins of the improved performance of the proposed classification are identified. When it comes to the unsupervised mode of learning, we revisit a well-known method of Fuzzy C-Means (FCM) by incorporating Tchebyschev distance using which we naturally form hyperbox-like prototypes. The design of hyperbox information granules is presented and the constructs formed in this manner are evaluated with respect to their abilities to capture the structure of data.

Metadata- and Ontology-Based Semantic Web Mining

2006 ◽  
pp. 259-296 ◽  
Author(s):  
Marie Aude Aufaure ◽  
Bénédicte Le Grand ◽  
Michel Soto ◽  
Nacera Bennacer
Keyword(s):  
Information Retrieval ◽  
Web Services ◽  
Semantic Web ◽  
Web Mining ◽  
State Of The Art ◽  
Next Generation ◽  
Research Areas ◽  
Semantic Data ◽  
Open Research ◽  
The Web

The increasing volume of data available on the Web makes information retrieval a tedious and difficult task. The vision of the Semantic Web introduces the next generation of the Web by establishing a layer of machine-understandable data, e.g., for software agents, sophisticated search engines and Web services. The success of the Semantic Web crucially depends on the easy creation, integration and use of semantic data. This chapter is a state-of-the-art review of techniques which could make the Web more “semantic”. Beyond this state-of-the-art, we describe open research areas and we present major current research programs in this domain.

An Associative and Adaptive Network Model For Information Retrieval In The Semantic Web

2010 ◽  
pp. 309-344
Author(s):  
Peter Scheir ◽  
Peter Prettenhofer ◽  
Stefanie N. Lindstaedt ◽  
Chiara Ghidini
Keyword(s):  
Information Retrieval ◽  
Semantic Web ◽  
Soft Computing ◽  
Relevance Feedback ◽  
Semantic Information ◽  
Retrieval Performance ◽  
Search Results ◽  
Low Coverage ◽  
Relevant Material ◽  
The Web

While it is agreed that semantic enrichment of resources would lead to better search results, at present the low coverage of resources on the web with semantic information presents a major hurdle in realizing the vision of search on the Semantic Web. To address this problem, this chapter investigates how to improve retrieval performance in settings where resources are sparsely annotated with semantic information. Techniques from soft computing are employed to find relevant material that was not originally annotated with the concepts used in a query. The authors present an associative retrieval model for the Semantic Web and evaluate if and to which extent the use of associative retrieval techniques increases retrieval performance. In addition, the authors present recent work on adapting the network structure based on relevance feedback by the user to further improve retrieval effectiveness. The evaluation of new retrieval paradigms - such as retrieval based on technology for the Semantic Web - presents an additional challenge since no off-the-shelf test corpora exist. Hence, this chapter gives a detailed description of the approach taken to evaluate the information retrieval service the authors have built.

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