Deep Associative Semantic Neural Graphs for Knowledge Representation and Fast Data Exploration

Adrian Horzyk

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, orders, 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.

Download


Paper Citation


in Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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