Szczegóły publikacji
Opis bibliograficzny
Deep associative semantic neural graphs for knowledge representation and fast data exploration / Adrian HORZYK // W: IC3K 2017 [Dokument elektroniczny] : proceedings of the 9th International joint Conference on Knowledge discovery, Knowledge engineering and Knowledge management : Funchal, Madeira - Portugal, 1–3 November, 2017. Vol. 2, KEOD / eds. David Aveiro, Jan Dietz, Joaquim Filipe. — Wersja do Windows. — Dane tekstowe. — [Portugal] : SCITEPRESS - Science and Technology Publications, cop. [2017]. — e-ISBN: 978-989-758-272-1. — S. 67-79. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: http://www.scitepress.org/DigitalLibrary/PublicationsDetail.a... [2017-12-04]. — Bibliogr. s. 78–79, Abstr. — Dostęp po zalogowaniu
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 110765 |
|---|---|
| Data dodania do BaDAP | 2018-02-20 |
| DOI | 10.5220/0006504100670079 |
| Rok publikacji | 2017 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management 2017 |
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.
