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Automating Knowledge Discovery Workflow Composition Through Ontology-Based Planning

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Automating Knowledge Discovery Workflow Composition Through Ontology-Based Planning

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    JC - Computer hardware and software

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GA201%2F09%2F1665" target="_blank" >GA201/09/1665: Bridging the gap between system biology and machine learning</a><br>

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2011

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

  • ISSN

    1545-5955

  • e-ISSN

  • Volume of the periodical

    8

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    12

  • Pages from-to

    253-264

  • UT code for WoS article

    000289204900001

  • EID of the result in the Scopus database