Automating Knowledge Discovery Workflow Composition Through Ontology-Based Planning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00173914" target="_blank" >RIV/68407700:21230/11:00173914 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5575359" target="_blank" >http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5575359</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TASE.2010.2070838" target="_blank" >10.1109/TASE.2010.2070838</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automating Knowledge Discovery Workflow Composition Through Ontology-Based Planning

Popis výsledku v původním jazyce

The problem addressed in this paper is the challenge of automated construction of knowledge discovery workflows, given the types of inputs and the required outputs of the knowl- edge discovery process. Our methodology consists of two main ingredients. The first one is defining a formal conceptualization of knowledge types and data mining algorithms by means of knowl- edge discovery ontology. The second one is workflow composition formalized as a planning task using the ontology of domain and task descriptions. Two versions of a forward chaining planning algorithm were developed. The baseline version demonstrates suitability of the knowledge discovery ontology for planning and uses Planning Domain Definition Language (PDDL) descriptions of algorithms; to this end, a procedure for converting data mining algorithm descriptions into PDDL was developed. The second directly queries the ontology using a reasoner.

Název v anglickém jazyce

Automating Knowledge Discovery Workflow Composition Through Ontology-Based Planning

Popis výsledku anglicky

The problem addressed in this paper is the challenge of automated construction of knowledge discovery workflows, given the types of inputs and the required outputs of the knowl- edge discovery process. Our methodology consists of two main ingredients. The first one is defining a formal conceptualization of knowledge types and data mining algorithms by means of knowl- edge discovery ontology. The second one is workflow composition formalized as a planning task using the ontology of domain and task descriptions. Two versions of a forward chaining planning algorithm were developed. The baseline version demonstrates suitability of the knowledge discovery ontology for planning and uses Planning Domain Definition Language (PDDL) descriptions of algorithms; to this end, a procedure for converting data mining algorithm descriptions into PDDL was developed. The second directly queries the ontology using a reasoner.

Druh

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

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F09%2F1665" target="_blank" >GA201/09/1665: Překonání propasti mezi systémovou biologií a strojovým učením</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

ISSN

1545-5955

e-ISSN

—

Svazek periodika

8

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

253-264

Kód UT WoS článku

000289204900001

EID výsledku v databázi Scopus

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