Learning Semantic Web Usage Profiles by Using Genetic Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00184153" target="_blank" >RIV/68407700:21230/11:00184153 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/11:00184153

Výsledek na webu

<a href="http://ijits-bg.com/" target="_blank" >http://ijits-bg.com/</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Learning Semantic Web Usage Profiles by Using Genetic Algorithms

Popis výsledku v původním jazyce

Web usage profile is very important in recommender systems. More interesting is the semantic enriched profile, which can describe visitor intents by ontologies and express more information and relations of visitor's character. Our research is based on processing semantically enriched clickstream and application of scoring algorithm, which is based on symbolic regression. A semantic enrichment uses Linked Data principles. The scoring assigns to each pageview a value, which represents and involves visitorinterests. Scoring involves all know attributes of each pageview including semantic annotation. The score of each pageview is used to establish a visitor profile. The established profile can be in form of ontologies. In this paper, we propose integratescoring algorithm into semantic web usage mining and publish visitor profile in RDF/OWL representation. We suggest merge the profiles from different web sites and integrate additional related information from publicly available reso

Název v anglickém jazyce

Learning Semantic Web Usage Profiles by Using Genetic Algorithms

Popis výsledku anglicky

Web usage profile is very important in recommender systems. More interesting is the semantic enriched profile, which can describe visitor intents by ontologies and express more information and relations of visitor's character. Our research is based on processing semantically enriched clickstream and application of scoring algorithm, which is based on symbolic regression. A semantic enrichment uses Linked Data principles. The scoring assigns to each pageview a value, which represents and involves visitorinterests. Scoring involves all know attributes of each pageview including semantic annotation. The score of each pageview is used to establish a visitor profile. The established profile can be in form of ontologies. In this paper, we propose integratescoring algorithm into semantic web usage mining and publish visitor profile in RDF/OWL representation. We suggest merge the profiles from different web sites and integrate additional related information from publicly available reso

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal on Information Technologies and Security

ISSN

1313-8251

e-ISSN

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Svazek periodika

3

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

BG - Bulharská republika

Počet stran výsledku

18

Strana od-do

3-20

Kód UT WoS článku

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EID výsledku v databázi Scopus

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