A content-based approach for document representation and retrieval

Rinaldi, Antonio M.

doi:10.1145/1410140.1410163

Cited by 26 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach emphasizes the reuse of science but does not reflect the evolution of theory. The methods of classification are classified into two categories: principle-based and statistical-based classification [5,6]. In terms of principle-based categorization, it must be supported by specific knowledge and principles in this area.…”

Section: Related Workmentioning

confidence: 99%

Automatic Relationship Construction in Domain Ontology Engineering using Semantic and Thematic Graph Generation Process and Convolution Neural Network

Guruvayur*¹,

Suchithra²

2019

IJRTE

View full text Add to dashboard Cite

In recent studies, Ontology construction plays an important role in translating raw text into useful knowledge. The proposed methodology supports efficient retrieval using multidimensional theory and implements integrated data training techniques before enter the trial process. The proposed approach has used the Semantic and Thematic Graph Generation Process to extract useful knowledge, and uses data mining techniques and web solutions to present knowledge as well as improve search speed and information retrieval accuracy. Established ontology can help clarify what it means for different ideas and relationships. Due to the rise of the ontology repository, the process of matching can take a long time. To avoid this, the method produces a hierarchical structure with in-depth interpretation of the data. A system is designed to remove domain dependencies using a dynamic labeling scheme using basic theorem, and the results show that it is possible to automatically and independently construct an independent domain

show abstract

Section: Related Workmentioning

confidence: 99%

Automatic Relationship Construction in Domain Ontology Engineering using Semantic and Thematic Graph Generation Process and Convolution Neural Network

Guruvayur*¹,

Suchithra²

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…The methods of text classification are divided into two categories, including rules-based and statistical classification methods 2 Scientific Programming [1,2]. Among them, the rules-based classification methods need more knowledge and rules base in this field.…”

Section: Text Classificationmentioning

confidence: 99%

A Robust Text Classifier Based on Denoising Deep Neural Network in the Analysis of Big Data

Wulamu

Zhang

Xie

et al. 2017

Scientific Programming

View full text Add to dashboard Cite

Text classification has always been an interesting issue in the research area of natural language processing (NLP). While entering the era of big data, a good text classifier is critical to achieving NLP for scientific big data analytics. With the ever-increasing size of text data, it has posed important challenges in developing effective algorithm for text classification. Given the success of deep neural network (DNN) in analyzing big data, this article proposes a novel text classifier using DNN, in an effort to improve the computational performance of addressing big text data with hybrid outliers. Specifically, through the use of denoising autoencoder (DAE) and restricted Boltzmann machine (RBM), our proposed method, named denoising deep neural network (DDNN), is able to achieve significant improvement with better performance of antinoise and feature extraction, compared to the traditional text classification algorithms. The simulations on benchmark datasets verify the effectiveness and robustness of our proposed text classifier.

show abstract

“…For example, the heterogeneity issue, due to the existence of heterogeneous information sources, has required sophisticated solutions to associate metadata to the collected data in order to correctly interpret the results. Semantically enriched metadata -formalized through ontologies using the semantic web languages -inside increasingly large collection of heterogeneous data have arisen the need for efficiently dealing with large ontologies, spawning new research fields, such as, knowledge representation and retrieval (Albanese et al, 2005;Rinaldi, 2008;Rinaldi, 2014), ontology matching and integration (Euzenat et al, 2007), partitioning (Amato et al, 2015b;Amato et al, 2015a), reuse (Modoni et al, 2015), versioning and maintenance (Flouris et al, 2006). A description of the existing solutions that deal with the other technological challenges of Big Data is out of the scope of this paper.…”

Section: A Framework For a Qualitative Evaluation Of Big Data Solutionsmentioning

confidence: 99%

Big Data: A Survey - The New Paradigms, Methodologies and Tools

Caldarola

Rinaldi

2015

Proceedings of 4th International Conference on Data Management Technologies and Applications

View full text Add to dashboard Cite

For several years we are living in the era of information. Since any human activity is carried out by means of information technologies and tends to be digitized, it produces a humongous stack of data that becomes more and more attractive to different stakeholders such as data scientists, entrepreneurs or just privates. All of them are interested in the possibility to gain a deep understanding about people and things, by accurately and wisely analyzing the gold mine of data they produce. The reason for such interest derives from the competitive advantage and the increase in revenues expected from this deep understanding. In order to help analysts in revealing the insights hidden behind data, new paradigms, methodologies and tools have emerged in the last years. There has been a great explosion of technological solutions that arises the need for a review of the current state of the art in the Big Data technologies scenario. Thus, after a characterization of the new paradigm under study, this work aims at surveying the most spread technologies under the Big Data umbrella, throughout a qualitative analysis of their characterizing features. 362 Caldarola E. and Rinaldi A..

show abstract

A content-based approach for document representation and retrieval

Cited by 26 publications

References 8 publications

Automatic Relationship Construction in Domain Ontology Engineering using Semantic and Thematic Graph Generation Process and Convolution Neural Network

Automatic Relationship Construction in Domain Ontology Engineering using Semantic and Thematic Graph Generation Process and Convolution Neural Network

A Robust Text Classifier Based on Denoising Deep Neural Network in the Analysis of Big Data

Big Data: A Survey - The New Paradigms, Methodologies and Tools

Contact Info

Product

Resources

About