000			03674cam a22005052 4500
001			1751280330
003			DE-627
005			20241227231600.0
007			cr uuu---uuuuu
008			210312s2017 xx |||||o 00| ||eng c
020			_a9783319682884
035			_a(DE-627)1751280330
035			_a(DE-599)KEP055179894
035			_a(EBL)EBL6296897
035			_a(EBC)EBC6296897
035			_a(DE-627-1)055179894
040			_aDE-627 _bger _cDE-627 _erda
041			_aeng
082	0		_a025.04
100	1		_aFernandez, Miriam _eVerfasserIn _4aut
245	1	4	_aThe Semantic Web - ISWC 2017 _b16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part I
264		1	_aCham _bSpringer International Publishing AG _c2017
264		4	_c©2017
300			_a1 online resource (806 pages)
336			_aText _btxt _2rdacontent
337			_aComputermedien _bc _2rdamedia
338			_aOnline-Ressource _bcr _2rdacarrier
490	0		_aLecture Notes in Computer Science Ser. _vv.10587
500			_aDescription based on publisher supplied metadata and other sources
520			_aIntro -- Preface -- Organization -- Abstracts of Invited Talks -- From Relational to Semantic Data Mining -- Ontologies for the Modern Age -- Applied Semantics: Beyond the Catalog -- Contents - Part I -- Contents -- Part II -- Research Track -- Multi-label Based Learning for Better Multi-criteria Ranking of Ontology Reasoners -- 1 Introduction -- 2 Background and Related Works -- 2.1 Key Notions of Multi-label Learning Paradigm -- 2.2 Multi-label Learning Techniques for Algorithm Selection -- 2.3 Ontology Features -- 3 Novel Multi-label Learning Method for Multi-criteria Ranking of Ontology Reasoners -- 3.1 Reasoner Ranking Criteria and Preference Rules -- 3.2 Specification of the Novel Multi-label Ranking Method -- 3.3 Multi-RakSOR Learning and Prediction Steps -- 3.4 Ranking Consistency Checking Method -- 4 Data Collection -- 5 Experimental Evaluation of Multi-RakSOR -- 5.1 Evaluation Metrics -- 5.2 Multi-label Learning Methods -- 5.3 Relevance Prediction Assessment Results -- 5.4 Ranking Prediction Assessment Results -- 6 Experimental Evaluation of Meta-RakSOR -- 7 Conclusion -- References -- The Efficacy of OWL and DL on User Understanding of Axioms and Their Entailments -- 1 Introduction -- 2 Background -- 3 Tasks: Understanding Axioms and Inference -- 4 Main Hypotheses -- 5 Empirical Study Design -- 5.1 Designing Questions for the Study -- 5.2 Experiment Phases -- 5.3 Experiment Execution -- 5.4 Statistical Methods -- 6 Results -- 6.1 Understanding Tasks -- 6.2 Sound Inference Tasks -- 6.3 Unsound Inference Tasks -- 6.4 Time Performance -- 7 Discussion -- 7.1 Understanding Axioms -- 7.2 Sound Inferences -- 7.3 Unsound Inferences -- 8 Threats to Validity -- 9 Conclusion -- References -- A Decidable Very Expressive Description Logic for Databases -- 1 Introduction -- 2 The Description Logic DLR+ -- 3 Expressiveness of DLR+.
650		4	_aSemantic Web-Congresses
650		4	_aElectronic books
700	1		_aTamma, Valentina _eMitwirkendeR _4ctb
700	1		_aLecue, Freddy _eMitwirkendeR _4ctb
700	1		_aCudré-Mauroux, Philippe _eMitwirkendeR _4ctb
700	1		_aSequeda, Juan _eMitwirkendeR _4ctb
700	1		_aLange, Christoph _eMitwirkendeR _4ctb
700	1		_aHeflin, Jeff _eMitwirkendeR _4ctb
700	1		_ad'Amato, Claudia _eMitwirkendeR _4ctb
776	1		_z9783319682877
776	0	8	_iErscheint auch als _nDruck-Ausgabe _z9783319682877
856	4	0	_uhttps://ebookcentral.proquest.com/lib/kxp/detail.action?docID=6296897 _mX:EBC _zlizenzpflichtig
942			_2ddc _cBK _n0
951			_aBO
999			_c222 _d222