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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the thoroughly refereed post-conference proceedings of the Satellite Events of the 16th Extended Semantic Web Conference, ESWC 2019, held in Portoroz, Slovenia, in June 2019.The volume contains 38 poster and demonstration papers, 2 workshop papers,5 PhD symposium papers, and 3 industry track papers, selected out of a total of 68 submissions. They deal with all areas of semantic web research, semantic technologies on the Web and Linked Data.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2014, held in Varna, Bulgaria in September 2014. The 14 revised full papers and 9 short papers presented were carefully reviewed and selected from 53 submissions. The range of topics is almost equally broad, from traditional areas such as computer vision and natural language processing to emerging areas such as mining the behavior of Web-based communities.

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paperback. Condición: New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback. Pub Date: December 2012 Pages: 294 Language: English Publisher: Tsinghua University Press. the world's leading computer textbook selection: semantic Web technology base. introduces the Semantic Web foundation. The world-renowned computer Textbooks: Semantic Web technology base from a practical point of view. from the development of the Semantic Web. introduced several of today's popular Semantic Web technology. is the first domestic and foreign books on Semantic Web technology. The book consists of nine chapters. each depicting a formal semantics of the origin of semantic technologies. the basics of RDF. RDF and RDFS Web Ontology Language OWL. OWL formal semantics. ontologies and rules. the body of the query language. ontology engineering. as well as body some applications. Contents: Chapter 1 semantic quest 1.1 model 1.2 with the knowledge to calculate the 1.3 exchange information 1.4 Semantic Web technology 1.5 Further reading simple ontology rdf and rdf mode Chapter 2 Introduction 2.1.1 Figure 2.1 rdf rather than tree 2.1. 2 rdf Name: values ??uri2.1.3 rdf: text 2.2 rdf syntax 2.2.1 triples from Figure 2.2.2 simple triplet syntax: n3. n-triple and turtle2.2.3 rdf xml sequence 2.2.4 of the xml rdf: 2.2.5 shorter uri the uri with other issues: the xml entity. and relative uri2.2.6 uri come from. what they mean is what 2.3 advanced features .2.3.1 rdf data type the 2.3.2 language settings and data types 2.3.3 multi-value relationship 2.3.4 2.4.1 class attributes Example 2.4.2 subclass and class structure 2.4.3 2.4.4 sub-attributes simple body blank node 2.4 rdf mode limit and attribute hierarchies 2.4.5 Properties 2.4.6 rdfs Additional information 2.5 encoded in a special data structure 2.5.1 rdf list 2.5.2 materialized 2.6 An example on the proposition proposition: 2.7 This chapter summarizes 2.7.1 rdf (s ) language constitutes an overview of the exercises 2.9 further reading Chapter 3 rdf formal semantics 3.1 Why do we need semantics 3.2 rdf (s). model-theoretic semantics 3.2.1 simple explanation explains 3.2.2 rdf-3.2.3 rdfs explained 3.2.4 Data 2.8 3.2.5 example of the type of explanation 3.3 3.3.1 simple derivation of deductive reasoning according to the inference rules of syntax rules 3.3.2 rdf-interpretation of the derivation rules deduced 3.3.3 rdfs-interpretation of the rules 3.3.4 Additional rules for data types 3.3 example of deductive .5 rdfs 3.4 rdf (s) of the semantic limitations 3.5 This chapter summarizes 3.6 Exercises 3.7 Further reading Chapter 4 Web Ontology body the owl said the language owl4.1 with the 4.1.1 owl body head 4.1.2. roles and the individual 4.1.3 simple class 4.1.4 the relationship between individuals 4.1.5 closed class 4.1.6 Boolean class constructor 4.1.7 role limitations 4.1.9 4.1.8 role relationships role characteristics 4.1.1 0 reasoning type 4.2 type of owl's upcoming 4.2.1 owl full4.2.2 owl dl4.2.3 owl lite4.3 owl 2 Standard 4.3.1 owl 2 dl4.3.2 owl 2 SUMMARY Figure 4.3.3 owl 2 full4.4 chapter summarizes 4.4.1 owl The language constructs sub-Overview 4.4.2 owl 2 additional language construct sub-Overview 4.5 Exercises 4.6 Further reading Chapter 5 of the owl the form of semantic 5.1 the description logic 5.1.1 Description Logic alc5.1.2 named as a description logic the owl dl5.1.3 of description logic and their 5.3.2 negate model On the the semantic epitaxial 5.2.1 sroiq semantic 5.2.2 the predicate logic define sroiq semantic 5.3 owl automatic reasoning 5.3.1 reasoning problems with the relations 5.1.4 sroiq owl sublanguage between forms of syntax 5.2 owl the tableaux algorithm 5.3.5 the tableaux algorithm paradigms 5.3.3 alc 5.3.4 shiq computational complexity 5.4 This chapter summarizes 5.5 Exercise 5.6 Further reading Chapter 6 body and rules 6.1 What is Rule 620 first-order rule language datalog6.2.1 datalog Introduction the synthesis semantics combine 6.3.1 6.2.2 datal.

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The human brain possesses the remarkable capability of understanding, - terpreting, and producing human language, thereby relying mostly on the left hemisphere. The ability to acquire language is innate as can be seen from d- orders such as speci c language impairment (SLI), which manifests itself in a missing sense for grammaticality. Language exhibits strong compositionality and structure. Hence biological neural networks are naturally connected to processing and generation of high-level symbolic structures. Unlike their biological counterparts, arti cial neural networks and logic do not form such a close liason. Symbolic inference mechanisms and statistical machine learning constitute two major and very di erent paradigms in ar- cial intelligence which both have their strengths and weaknesses: Statistical methods o er exible and highly e ective tools which are ideally suited for possibly corrupted or noisy data, high uncertainty and missing information as occur in everyday life such as sensor streams in robotics, measurements in medicine such as EEG and EKG, nancial and market indices, etc. The m- els, however, are often reduced to black box mechanisms which complicate the integration of prior high level knowledge or human inspection, and they lack theabilitytocopewitharichstructureofobjects,classes,andrelations. S- bolic mechanisms, on the other hand, are perfectly applicative for intuitive human-machine interaction, the integration of complex prior knowledge, and well founded recursive inference. Their capability of dealing with uncertainty andnoiseandtheire ciencywhenaddressingcorruptedlargescalereal-world data sets, however, is limited. Thus, the inherent strengths and weaknesses of these two methods ideally complement each other.

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. State-of-the-art research Fast-track conference proceedings Unique visibilityThe two-volume set LNCS 7044 and 7045 constitutes the refereed proceedings of three confederated international conferences: Cooperative Information Systems.