LEADER 06506nam 22007335 450 001 9910298978803321 005 20220302173847.0 010 $a3-662-43585-3 024 7 $a10.1007/978-3-662-43585-4 035 $a(CKB)3710000000281292 035 $a(EBL)1966024 035 $a(OCoLC)895661364 035 $a(SSID)ssj0001386085 035 $a(PQKBManifestationID)11781020 035 $a(PQKBTitleCode)TC0001386085 035 $a(PQKBWorkID)11350161 035 $a(PQKB)11394800 035 $a(MiAaPQ)EBC1966024 035 $a(DE-He213)978-3-662-43585-4 035 $a(PPN)183086821 035 $a(EXLCZ)993710000000281292 100 $a20141113d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aTowards the Multilingual Semantic Web $ePrinciples, Methods and Applications /$fedited by Paul Buitelaar, Philipp Cimiano 205 $a1st ed. 2014. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2014. 215 $a1 online resource (339 p.) 300 $aDescription based upon print version of record. 311 0 $a3-662-43584-5 320 $aIncludes bibliographical references and index. 327 $aPart Principles -- Overcoming Linguistic Barriers to the Multilingual Semantic Web -- Design Patterns for Engineering the Ontology-Lexicon Interface -- Context and Terminology in the Multilingual Semantic Web -- The Multilingual Semantic Web as Virtual Knowledge Commons: The Case of the Under-resourced South African Languages -- A three-dimensional paradigm for conceptually scoped language technology -- Towards Verbalizing Multilingual N-ary Relations -- Part Methods -- Publishing Linked Data on the Web: the Multilingual Dimension -- State-of-the-art in Multilingual and Cross-Lingual Ontology Matching -- Mind the cultural gap: bridging language specific DBpedia chapters for Question Answering -- Multilingual Extraction Ontologies -- Collaborative Management of Multilingual Ontologies -- From RDF to Natural Language and Back -- Multilingual Natural Language Interaction with Semantic Web Knowledge Bases and Linked Open Data -- A Cross-Lingual Correcting and Completive Method for Multilingual Ontology Labels -- A Cross-Lingual Correcting and Completive Method for Multilingual Ontology Labels -- Multilingual Lexicalisation and Population of Event Ontologies. A Case Study for Social Media -- Part Applications -- Semantically-Assisted XBRL-Taxonomy Alignment Across Languages -- Lexicalizing a multilingual ontology for searching in the Assistive Technology domain -- Service Oriented Architecture for Interoperability of Multi-Language Services. 330 $aTo date, the relation between multilingualism and the Semantic Web has not yet received enough attention in the research community. One major challenge for the Semantic Web community is to develop architectures, frameworks and systems that can help in overcoming national and language barriers, facilitating equal access to information produced in different cultures and languages. As such, this volume aims at documenting the state-of-the-art with regard to the vision of a Multilingual Semantic Web, in which semantic information will be accessible in and across multiple languages. The Multilingual Semantic Web as envisioned in this volume will support the following functionalities:  (1) responding to information needs in any language with regard to semantically structured data available on the Semantic Web and Linked Open Data (LOD) cloud, (2) verbalizing and accessing semantically structured data, ontologies or other conceptualizations in multiple languages, (3) harmonizing, integrating, aggregating, comparing and repurposing semantically structured data across languages, and (4) aligning and reconciling ontologies or other conceptualizations across languages. The volume is divided into three main sections: Principles, Methods and Applications. The section on ?Principles? discusses models, architectures, and methodologies that enrich the current Semantic Web architecture with features necessary to handle multiple languages. The section on ?Methods? describes algorithms and approaches for solving key issues related to the construction of the Multilingual Semantic Web. The section on ?Applications? describes the use of Multilingual Semantic Web based approaches in the context of several  application domains. This volume is essential reading for all academic and industrial researchers who want to embark on this new research field at the intersection of various research topics, including the Semantic Web, Linked Data, natural language processing, computational linguistics, terminology, and information retrieval. It will also be of great interest to practitioners who are interested in re-examining their existing infrastructure and methodologies for handling multiple languages in Web applications or information retrieval systems. 606 $aArtificial intelligence 606 $aComputational linguistics 606 $aInformation organization 606 $aInformation retrieval 606 $aUser interfaces (Computer systems) 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputational Linguistics$3https://scigraph.springernature.com/ontologies/product-market-codes/N22000 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aUser Interfaces and Human Computer Interaction$3https://scigraph.springernature.com/ontologies/product-market-codes/I18067 615 0$aArtificial intelligence. 615 0$aComputational linguistics. 615 0$aInformation organization. 615 0$aInformation retrieval. 615 0$aUser interfaces (Computer systems) 615 14$aArtificial Intelligence. 615 24$aComputational Linguistics. 615 24$aInformation Storage and Retrieval. 615 24$aUser Interfaces and Human Computer Interaction. 676 $a004 676 $a005.437 676 $a006.3 676 $a025.04 702 $aBuitelaar$b Paul$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCimiano$b Philipp$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910298978803321 996 $aTowards the Multilingual Semantic Web$92025283 997 $aUNINA LEADER 05303nam 22006614a 450 001 9911019275103321 005 20200520144314.0 010 $a9786610551699 010 $a9781280551697 010 $a1280551690 010 $a9780470051313 010 $a0470051310 010 $a9780470051306 010 $a0470051302 035 $a(CKB)1000000000354827 035 $a(EBL)269139 035 $a(OCoLC)166353635 035 $a(SSID)ssj0000215350 035 $a(PQKBManifestationID)11189737 035 $a(PQKBTitleCode)TC0000215350 035 $a(PQKBWorkID)10184707 035 $a(PQKB)10634923 035 $a(MiAaPQ)EBC269139 035 $a(Perlego)2765550 035 $a(EXLCZ)991000000000354827 100 $a20060309d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOperational risk $emodeling analytics /$fHarry H. Panjer 210 $aHoboken, N.J. $cWiley Interscience$dc2006 215 $a1 online resource (460 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 08$a9780471760894 311 08$a0471760897 320 $aIncludes bibliographical references (p. 417-425) and index. 327 $aOperational Risk; Contents; Preface; Acknowledgments; Part I Introduction to operational risk modeling; 1 Operational risk; 1.1 Introduction; 1.1.1 Basel II - General; 1.1.2 Basel II - Operational risk; 1.2 Operational risk in insurance; 1.3 The analysis of operational risk; 1.4 The model- based approach; 1.4.1 The modeling process; 1.5 Organization of this book; 2 Basic probability concepts; 2.1 Introduction; 2.2 Distribution functions and related concepts; 2.3 Moments; 2.4 Quantiles of a distribution; 2.5 Generating functions; 2.6 Exercises; 3 Measures of risk; 3.1 Introduction 327 $a3.2 Risk measures3.3 Tail- Value-at- Risk; Part II Probabilistic tools for operational risk modeling; 4 Models for the size of losses: Continuous distributions; 4.1 Introduction; 4.2 An inventory of continuous distributions; 4.2 1 One-parameter distributions; 4.2.2 Two-parameter distributions; 4.2.3 Three-parameter distributions; 4.2.4 Four-parameter distributions; 4.2.5 Distributions with finite support; 4.3 Selected distributions and their relationships; 4.3.1 Introduction; 4.3.2 Two important parametric families; 4.4 Limiting distributions; 4.5 The role of parameters 327 $a4.5.1 Parametric and scale distributions4.5.2 Finite mixture distributions; 4.5.3 Data-dependent distributions; 4.6 Tails of distributions; 4.6.1 Classification based on moments; 4.6.2 Classification based on tail behavior; 4.6.3 Classification based on hazard rate function; 4.7 Creating new distributions; 4.7.1 Introduction; 4.7.2 Multiplication by a constant; 4.7.3 Transformation by raising to a power; 4.7.4 Transformation by exponentiation; 4.7.5 Continuous mixture of distributions; 4.7.6 Frailty models; 4.7.7 Splicing pieces of distributions; 4.8 TVaR for continuous distributions 327 $a4.8.1 Continuous elliptical distributions4.8.2 Continuous exponential dispersion distributions; 4.9 Exercises; 5 Models for the number of losses: Counting distributions; 5.1 Introduction; 5.2 The Poisson distribution; 5.3 The negative binomial distribution; 5.4 The binomial distribution; 5.5 The (a, b, 0) class; 5.6 The (a, b, 1) class; 5.7 Compound frequency models; 5.8 Recursive calculation of compound probabilities; 5.9 An inventory of discrete distributions; 5.9.1 The (a, b, 0) class; 5.9.2 The (a, b, 1 ) class; 5.9.3 The zero-truncated subclass; 5.9.4 The zero-modified subclass 327 $a5.9.5 The compound class5.10 A hierarchy of discrete distributions; 5.11 Further properties of the compound Poisson class; 5.12 Mixed frequency models; 5.13 Poisson mixtures; 5.14 Effect of exposure on loss counts; 5.15 TVaR for discrete distributions; 5.15.1 TVaR for discrete exponential dispersion distributions; 5.16 Exercises; 6 Aggregate loss models; 6.1 Introduction; 6.2 Model choices; 6.3 The compound model for aggregate losses; 6.4 Some analytic results; 6.5 Evaluation of the aggregate loss distribution; 6.6 The recursive method; 6.6.1 Compound frequency models 327 $a6.6.2 Underflow/overflow problems 330 $aDiscover how to optimize business strategies from both qualitative and quantitative points of viewOperational Risk: Modeling Analytics is organized around the principle that the analysis of operational risk consists, in part, of the collection of data and the building of mathematical models to describe risk. This book is designed to provide risk analysts with a framework of the mathematical models and methods used in the measurement and modeling of operational risk in both the banking and insurance sectors.Beginning with a foundation for operational risk modeling and a focus on 410 0$aWiley series in probability and statistics. 606 $aRisk management 615 0$aRisk management. 676 $a658.15/5 700 $aPanjer$b Harry H$0437806 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019275103321 996 $aOperational risk$94418567 997 $aUNINA