000			03731nam a22005175i 4500
001			978-0-387-39252-3
003			DE-He213
005			20201213200413.0
007			cr nn 008mamaa
008			100301s2006 xxu| s |||| 0|eng d
020			_a9780387392523 _9978-0-387-39252-3
024	7		_a10.1007/978-0-387-39252-3 _2doi
050		4	_aQA76.76.A65
072		7	_aUNH _2bicssc
072		7	_aUDBD _2bicssc
072		7	_aCOM032000 _2bisacsh
082	0	4	_a005.7 _223
100	1		_aCimiano, Philipp. _eauthor.
245	1	0	_aOntology Learning and Population from Text _h[electronic resource] : _bAlgorithms, Evaluation and Applications / _cby Philipp Cimiano.
264		1	_aBoston, MA : _bSpringer US, _c2006.
300			_aXXVIIII, 347 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aPreliminaries -- Ontologies -- Ontology Learning from Text -- Basics -- Datasets -- Methods and Applications -- Concept Hierarchy Induction -- Learning Attributes and Relations -- Population -- Applications -- Conclusion -- Contribution and Outlook -- Concluding Remarks.
520			_aStandard formalisms for knowledge representation such as RDFS or OWL have been recently developed by the semantic web community and are now in place. However, the crucial question still remains: how will we acquire all the knowledge available in people's heads to feed our machines? Natural language is THE means of communication for humans, and consequently texts are massively available on the Web. Terabytes and terabytes of texts containing opinions, ideas, facts and information of all sorts are waiting to be mined for interesting patterns and relationships, or used to annotate documents to facilitate their retrieval. A semantic web which ignores the massive amount of information encoded in text, might actually be a semantic, but not a very useful, web. Knowledge acquisition, and in particular ontology learning from text, actually has to be regarded as a crucial step within the vision of a semantic web. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications presents approaches for ontology learning from text and will be relevant for researchers working on text mining, natural language processing, information retrieval, semantic web and ontologies. Containing introductory material and a quantity of related work on the one hand, but also detailed descriptions of algorithms, evaluation procedures etc. on the other, this book is suitable for novices, and experts in the field, as well as lecturers. Datasets, algorithms and course material can be downloaded at http://www.cimiano.de/olp. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications is designed for practitioners in industry, as well researchers and graduate-level students in computer science.
650		0	_aComputer science.
650		0	_aComputer Communication Networks.
650		0	_aDatabase management.
650		0	_aInformation systems.
650		0	_aMultimedia systems.
650		0	_aArtificial intelligence.
650	1	4	_aComputer Science.
650	2	4	_aInformation Systems Applications (incl.Internet).
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aDatabase Management.
650	2	4	_aMultimedia Information Systems.
650	2	4	_aComputer Communication Networks.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387306322
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-39252-3
912			_aZDB-2-SCS
950			_aComputer Science (Springer-11645)
999			_c12288 _d12288