000			02357cam a2200445Mu 4500
999			_c544498 _d544496
001			002754695
003			Ge_NSL
005			20201019012202.0
006			m o d
007			cr un|||||||||
008			190110e20180928sz o 000 u eng d
020			_a9783631606513
020			_a3631606516
035			_a(OCoLC)1082971313
040			_aOAPEN _beng _epn _cOAPEN _dOCLCQ
041			_aeng
049			_aMYGG
072		7	_aUBJ _2bicssc
072		7	_aUFL _2bicssc
080			_a004
100	1		_aWohlgenannt, Gerhard. _4aut _9177360
245	1	0	_aLearning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources.
260			_aBern : _bPeter Lang International Academic Publishers, _c2018.
300			_a1 online resource (222 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
520			_aThe manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi- )automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach.
546			_aEnglish.
653			_asemantic web _alanguage corpus
653			_aსემანტიკური კომპიუტერული ქსელი _aენობრივი კორპუსი
856	4	0	_uhttp://www.oapen.org/download/?type=document&docid=1003170
910			_aebookload190226
942			_2udc _cER
946			_mDOAB
947			_aOCLC _bWorldCat Holdings _d190222
947			_aOpen Access Publishing in European Networks _bDirectory of Open Access Books _d190222
949		1	_1Internet Access _an _bNET _h**See URL(s) _o8 _x02