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008			220601s2014 sz | o |||| 0|eng d
020			_a9783031021541
024	7		_a10.1007/978-3-031-02154-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.N38 _b2014 EB
100	1		_aCimiano, Philipp _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686420
245	1	0	_aOntology-Based Interpretation of Natural Language _cby Philipp Cimiano, Christina Unger, John McCrae
250			_a1st edition 2014
264		1	_aCham _bSpringer International Publishing _c2014
300			_a1 recurso en línea (XIX, 158 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Human Language Technologies _x1947-4059
505	0		_aList of Figures -- Preface -- Acknowledgments -- Introduction -- Ontologies -- Linguistic Formalisms -- Ontology Lexica -- Grammar Generation -- Putting Everything Together -- Ontological Reasoning for Ambiguity Resolution -- Temporal Interpretation -- Ontology-Based Interpretation for Question Answering -- Conclusion -- Bibliography -- Authors' Biographies .
520			_aFor humans, understanding a natural language sentence or discourse is so effortless that we hardly ever think about it. For machines, however, the task of interpreting natural language, especially grasping meaning beyond the literal content, has proven extremely difficult and requires a large amount of background knowledge. This book focuses on the interpretation of natural language with respect to specific domain knowledge captured in ontologies. The main contribution is an approach that puts ontologies at the center of the interpretation process. This means that ontologies not only provide a formalization of domain knowledge necessary for interpretation but also support and guide the construction of meaning representations. We start with an introduction to ontologies and demonstrate how linguistic information can be attached to them by means of the ontology lexicon model lemon. These lexica then serve as basis for the automatic generation of grammars, which we use to compositionally construct meaning representations that conform with the vocabulary of an underlying ontology. As a result, the level of representational granularity is not driven by language but by the semantic distinctions made in the underlying ontology and thus by distinctions that are relevant in the context of a particular domain. We highlight some of the challenges involved in the construction of ontology-based meaning representations, and show how ontologies can be exploited for ambiguity resolution and the interpretation of temporal expressions. Finally, we present a question answering system that combines all tools and techniques introduced throughout the book in a real-world application, and sketch how the presented approach can scale to larger, multi-domain scenarios in the context of the Semantic Web. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Ontologies / Linguistic Formalisms / Ontology Lexica / Grammar Generation / Putting Everything Together / Ontological Reasoning for Ambiguity Resolution / Temporal Interpretation / Ontology-Based Interpretation for Question Answering / Conclusion / Bibliography / Authors' Biographies.
988			_aSynthesis Collection of Technology_2014
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
650		7	_2embne _9666075 _aLingüística computacional
700	1		_aUnger, Andrea Christina _d1982- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686421
700	1		_aMcCrae, John _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _978550
776	0	8	_iPrinted edition: _z9783031010262
776	0	8	_iPrinted edition: _z9783031032820
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02154-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_dz _eb _zSI _b02/2023