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Author: Adrian Horzyk

Affiliation: AGH University of Science and Technology in Krakow, Poland

ISBN: 978-989-758-272-1

Keyword(s): Active Knowledge-based Neural Structures, Semantic Neural Structures, Representation of Complex Entities, Knowledge-based Inference, Deep Neural Network Architectures, Associative Graph Data Structures, Big Data, Associative Database Normalization, Database Transformation, Data Mining, Knowledge Exploration.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Engineering ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Knowledge Acquisition ; Knowledge Engineering and Ontology Development ; Knowledge Representation ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Ontology Engineering ; Symbolic Systems

Abstract: This paper presents new deep associative neural networks that can semantically associate any data, represent their complex relations of various kinds, and be used for fast information search, data mining, and knowledge exploration. They allow to store various horizontal and vertical relations between data and significantly broaden and accelerate various search operations. Many relations which must be searched in the relational databases are immediately available using the presented associative data model based on a new special kind of associative spiking neurons and sensors used for the construction of these networks. The inference operations are also performed using the reactive abilities of these spiking neurons. The paper describes the transformation of any relational database to this kind of networks. All related data and their combinations representing various objects are contextually connected with different strengths reproducing various similarities, proximities, successions, o rders, inclusions, rarities, or frequencies of these data. The computational complexity of the described operations is usually constant and less than operations used in the databases. The theory is illustrated by a few examples and used for inference on this kind of neural networks. (More)

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Paper citation in several formats:
Horzyk, A. (2017). Deep Associative Semantic Neural Graphs for Knowledge Representation and Fast Data Exploration.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, ISBN 978-989-758-272-1, pages 67-79. DOI: 10.5220/0006504100670079

@conference{keod17,
author={Adrian Horzyk.},
title={Deep Associative Semantic Neural Graphs for Knowledge Representation and Fast Data Exploration},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,},
year={2017},
pages={67-79},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006504100670079},
isbn={978-989-758-272-1},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,
TI - Deep Associative Semantic Neural Graphs for Knowledge Representation and Fast Data Exploration
SN - 978-989-758-272-1
AU - Horzyk, A.
PY - 2017
SP - 67
EP - 79
DO - 10.5220/0006504100670079

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