LEADER 01546nam  2200361z- 450 
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005 20210211
010   $a1000030761
035   $a(CKB)4920000000094613
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/55253
035   $a(oapen)doab55253
035   $a(EXLCZ)994920000000094613
100   $a20202102d2013      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aOntology Alignment using Biologically-inspired Optimisation Algorithms
210   $cKIT Scientific Publishing$d2013
215   $a1 online resource (XIV, 220 p. p.)
311 08$a3-86644-936-4 
330   $aIt is investigated how biologically-inspired optimisation methods can be used to compute alignments between ontologies. Independent of particular similarity metrics, the developed techniques demonstrate anytime behaviour and high scalability. Due to the inherent parallelisability of these population-based algorithms it is possible to exploit dynamically scalable cloud infrastructures - a step towards the provisioning of Alignment-as-a-Service solutions for future semantic applications.
610   $aalignment
610   $acloud
610   $aevolution
610   $aontology
610   $aPSO
700   $aBock$b Jürgen$4auth$01309825
906   $aBOOK
912   $a9910346708103321
996   $aOntology Alignment using Biologically-inspired Optimisation Algorithms$93029621
997   $aUNINA