Probabilistic semantic web :: reasoning and learning /
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam, Netherlands :
IOS Press,
[2017]
|
| Schriftenreihe: | Studies on the Semantic Web ;
v. 028. |
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Beschreibung: | 1 online resource (xvi, 173 pages) |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9781614997344 1614997349 |
| ISSN: | 2215-0870 ; |
Internformat
MARC
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| 100 | 1 | |a Zese, Riccardo, |e author. | |
| 245 | 1 | 0 | |a Probabilistic semantic web : |b reasoning and learning / |c Riccardo Zese. |
| 264 | 1 | |a Amsterdam, Netherlands : |b IOS Press, |c [2017] | |
| 300 | |a 1 online resource (xvi, 173 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Studies on the semantic web, |x 2215-0870 ; |v vol. 028 | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Part I. Introduction; Chapter 1. Semantic Web; 1.1 Description Logics and Semantic Web; 1.2 The Current Vision of the Semantic Web; Chapter 2. Probability; 2.1 Probabilistic Inference; 2.2 Probabilistic Learning; Chapter 3. Aims of the Thesis; Chapter 4. Structure of the Thesis; Part II. Description Logics; Chapter 5. Foundations of Description Logics; Chapter 6. Description Logics' Characteristics; 6.1 Concept and Role Constructors; 6.2 Family of DLs; 6.3 Knowledge Base; 6.3.1 TBox; 6.3.2 RBox; 6.3.3 ABox; 6.4 Semantics. | |
| 505 | 8 | |a Chapter 7. Significant Examples of Description Logics; Chapter 8. OWL: the Web Ontology Language; Chapter 9. Inference in Description Logics; 9.1 Approaches to Compute Explanations; 9.1.1 Solving min-a-enum: The Standard Definition; 9.1.2 Resolving min-a-enum: Pinpointing Formula; Part III. A Probabilistic Semantics for Description Logics; Chapter 10. Distribution Semantics; 10.1 Formal Definition; 10.2 PLP Languages under the Distribution Semantics; 10.2.1 Logic Programming; 10.2.2 LPAD; 10.2.3 ProbLog; 10.3 Inference in Probabilistic Logic Programming; 10.3.1 ProbLog Inference System. | |
| 505 | 8 | |a 10.3.2 PITA; 10.4 Learning in Probabilistic Logic Programming; Chapter 11. DISPONTE; Chapter 12. Probabilistic Description Logics; Part IV. Inference in Probabilistic DLs; Chapter 13. Inference; 13.1 Splitting Algorithm; 13.2 Binary Decision Diagrams; Chapter 14. BUNDLE; Chapter 15. TRILL; 15.1 TRILL on SWISH; Chapter 16. TRILL P; Chapter 17. Complexity of Inference; Chapter 18. Related Inference Systems; Chapter 19. Experiments; 19.1 BUNDLE: Comparison with PRONTO; 19.2 BUNDLE: Not Entailed Queries; 19.3 BUNDLE: Inference with Limited Number of Explanations; 19.4 BUNDLE: Scalability. | |
| 505 | 8 | |a 19.5 TRILL, TRILL P & BUNDLE: Comparing Different Approaches; 19.6 Discussion; Part V. Learning in Probabilistic DLs; Chapter 20. Learning; Chapter 21. EDGE: Parameter Learning; 21.1 Expectation Maximization Algorithm; 21.2 EDGE; Chapter 22. LEAP: Structure Learning; 22.1 CELOE; 22.2 LEAP; Chapter 23. Distributed Learning; 23.1 Map Reduce Approach; 23.2 The Message Passing Interface Standard; 23.3 EDGE MR; 23.4 LEAP MR; Chapter 24. Related Learning Systems; Chapter 25. Experiments; 25.1 EDGE: Comparison with Association Rules; 25.2 LEAP & EDGE: a Comparison Between Different Learning Problems. | |
| 505 | 8 | |a 25.3 EDGE MR: Parallelization Speedup; 25.4 EDGE MR: Memory Consumption; 25.5 LEAP MR: Parallelization Speedup; 25.6 Discussion; Part VI. Summary and Future Work; Chapter 26. Conclusion; Chapter 27. Future Work. | |
| 588 | 0 | |a Online resource; title from PDF title page (IOS Press, viewed January 26, 2017). | |
| 650 | 0 | |a Semantic Web. |0 http://id.loc.gov/authorities/subjects/sh2002000569 | |
| 650 | 0 | |a Semantic computing. |0 http://id.loc.gov/authorities/subjects/sh2009007899 | |
| 650 | 6 | |a Web sémantique. | |
| 650 | 6 | |a Informatique sémantique. | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Semantic computing |2 fast | |
| 650 | 7 | |a Semantic Web |2 fast | |
| 758 | |i has work: |a Probabilistic Semantic Web (Text) |1 https://id.oclc.org/worldcat/entity/E39PCYWfVmDWRy4H7FcV8jfkcK |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Zese, R. |t Probabilistic Semantic Web : Reasoning and Learning. |d Amsterdam : IOS Press, ©2016 |z 9781614997337 |
| 830 | 0 | |a Studies on the Semantic Web ; |v v. 028. |0 http://id.loc.gov/authorities/names/no2009156151 | |
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Datensatz im Suchindex
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| _version_ | 1826942140128690176 |
| adam_text | |
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| author | Zese, Riccardo |
| author_facet | Zese, Riccardo |
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| author_sort | Zese, Riccardo |
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| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.5913 .Z474 2016 |
| callnumber-search | QA76.5913 .Z474 2016 |
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| contents | Part I. Introduction; Chapter 1. Semantic Web; 1.1 Description Logics and Semantic Web; 1.2 The Current Vision of the Semantic Web; Chapter 2. Probability; 2.1 Probabilistic Inference; 2.2 Probabilistic Learning; Chapter 3. Aims of the Thesis; Chapter 4. Structure of the Thesis; Part II. Description Logics; Chapter 5. Foundations of Description Logics; Chapter 6. Description Logics' Characteristics; 6.1 Concept and Role Constructors; 6.2 Family of DLs; 6.3 Knowledge Base; 6.3.1 TBox; 6.3.2 RBox; 6.3.3 ABox; 6.4 Semantics. Chapter 7. Significant Examples of Description Logics; Chapter 8. OWL: the Web Ontology Language; Chapter 9. Inference in Description Logics; 9.1 Approaches to Compute Explanations; 9.1.1 Solving min-a-enum: The Standard Definition; 9.1.2 Resolving min-a-enum: Pinpointing Formula; Part III. A Probabilistic Semantics for Description Logics; Chapter 10. Distribution Semantics; 10.1 Formal Definition; 10.2 PLP Languages under the Distribution Semantics; 10.2.1 Logic Programming; 10.2.2 LPAD; 10.2.3 ProbLog; 10.3 Inference in Probabilistic Logic Programming; 10.3.1 ProbLog Inference System. 10.3.2 PITA; 10.4 Learning in Probabilistic Logic Programming; Chapter 11. DISPONTE; Chapter 12. Probabilistic Description Logics; Part IV. Inference in Probabilistic DLs; Chapter 13. Inference; 13.1 Splitting Algorithm; 13.2 Binary Decision Diagrams; Chapter 14. BUNDLE; Chapter 15. TRILL; 15.1 TRILL on SWISH; Chapter 16. TRILL P; Chapter 17. Complexity of Inference; Chapter 18. Related Inference Systems; Chapter 19. Experiments; 19.1 BUNDLE: Comparison with PRONTO; 19.2 BUNDLE: Not Entailed Queries; 19.3 BUNDLE: Inference with Limited Number of Explanations; 19.4 BUNDLE: Scalability. 19.5 TRILL, TRILL P & BUNDLE: Comparing Different Approaches; 19.6 Discussion; Part V. Learning in Probabilistic DLs; Chapter 20. Learning; Chapter 21. EDGE: Parameter Learning; 21.1 Expectation Maximization Algorithm; 21.2 EDGE; Chapter 22. LEAP: Structure Learning; 22.1 CELOE; 22.2 LEAP; Chapter 23. Distributed Learning; 23.1 Map Reduce Approach; 23.2 The Message Passing Interface Standard; 23.3 EDGE MR; 23.4 LEAP MR; Chapter 24. Related Learning Systems; Chapter 25. Experiments; 25.1 EDGE: Comparison with Association Rules; 25.2 LEAP & EDGE: a Comparison Between Different Learning Problems. 25.3 EDGE MR: Parallelization Speedup; 25.4 EDGE MR: Memory Consumption; 25.5 LEAP MR: Parallelization Speedup; 25.6 Discussion; Part VI. Summary and Future Work; Chapter 26. Conclusion; Chapter 27. Future Work. |
| ctrlnum | (OCoLC)970041839 |
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| discipline | Allgemeines |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2025-03-18T14:23:16Z |
| institution | BVB |
| isbn | 9781614997344 1614997349 |
| issn | 2215-0870 ; |
| language | English |
| oclc_num | 970041839 |
| open_access_boolean | |
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| series | Studies on the Semantic Web ; |
| series2 | Studies on the semantic web, |
| spelling | Zese, Riccardo, author. Probabilistic semantic web : reasoning and learning / Riccardo Zese. Amsterdam, Netherlands : IOS Press, [2017] 1 online resource (xvi, 173 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier data file rda Studies on the semantic web, 2215-0870 ; vol. 028 Includes bibliographical references. Part I. Introduction; Chapter 1. Semantic Web; 1.1 Description Logics and Semantic Web; 1.2 The Current Vision of the Semantic Web; Chapter 2. Probability; 2.1 Probabilistic Inference; 2.2 Probabilistic Learning; Chapter 3. Aims of the Thesis; Chapter 4. Structure of the Thesis; Part II. Description Logics; Chapter 5. Foundations of Description Logics; Chapter 6. Description Logics' Characteristics; 6.1 Concept and Role Constructors; 6.2 Family of DLs; 6.3 Knowledge Base; 6.3.1 TBox; 6.3.2 RBox; 6.3.3 ABox; 6.4 Semantics. Chapter 7. Significant Examples of Description Logics; Chapter 8. OWL: the Web Ontology Language; Chapter 9. Inference in Description Logics; 9.1 Approaches to Compute Explanations; 9.1.1 Solving min-a-enum: The Standard Definition; 9.1.2 Resolving min-a-enum: Pinpointing Formula; Part III. A Probabilistic Semantics for Description Logics; Chapter 10. Distribution Semantics; 10.1 Formal Definition; 10.2 PLP Languages under the Distribution Semantics; 10.2.1 Logic Programming; 10.2.2 LPAD; 10.2.3 ProbLog; 10.3 Inference in Probabilistic Logic Programming; 10.3.1 ProbLog Inference System. 10.3.2 PITA; 10.4 Learning in Probabilistic Logic Programming; Chapter 11. DISPONTE; Chapter 12. Probabilistic Description Logics; Part IV. Inference in Probabilistic DLs; Chapter 13. Inference; 13.1 Splitting Algorithm; 13.2 Binary Decision Diagrams; Chapter 14. BUNDLE; Chapter 15. TRILL; 15.1 TRILL on SWISH; Chapter 16. TRILL P; Chapter 17. Complexity of Inference; Chapter 18. Related Inference Systems; Chapter 19. Experiments; 19.1 BUNDLE: Comparison with PRONTO; 19.2 BUNDLE: Not Entailed Queries; 19.3 BUNDLE: Inference with Limited Number of Explanations; 19.4 BUNDLE: Scalability. 19.5 TRILL, TRILL P & BUNDLE: Comparing Different Approaches; 19.6 Discussion; Part V. Learning in Probabilistic DLs; Chapter 20. Learning; Chapter 21. EDGE: Parameter Learning; 21.1 Expectation Maximization Algorithm; 21.2 EDGE; Chapter 22. LEAP: Structure Learning; 22.1 CELOE; 22.2 LEAP; Chapter 23. Distributed Learning; 23.1 Map Reduce Approach; 23.2 The Message Passing Interface Standard; 23.3 EDGE MR; 23.4 LEAP MR; Chapter 24. Related Learning Systems; Chapter 25. Experiments; 25.1 EDGE: Comparison with Association Rules; 25.2 LEAP & EDGE: a Comparison Between Different Learning Problems. 25.3 EDGE MR: Parallelization Speedup; 25.4 EDGE MR: Memory Consumption; 25.5 LEAP MR: Parallelization Speedup; 25.6 Discussion; Part VI. Summary and Future Work; Chapter 26. Conclusion; Chapter 27. Future Work. Online resource; title from PDF title page (IOS Press, viewed January 26, 2017). Semantic Web. http://id.loc.gov/authorities/subjects/sh2002000569 Semantic computing. http://id.loc.gov/authorities/subjects/sh2009007899 Web sémantique. Informatique sémantique. COMPUTERS Intelligence (AI) & Semantics. bisacsh Semantic computing fast Semantic Web fast has work: Probabilistic Semantic Web (Text) https://id.oclc.org/worldcat/entity/E39PCYWfVmDWRy4H7FcV8jfkcK https://id.oclc.org/worldcat/ontology/hasWork Print version: Zese, R. Probabilistic Semantic Web : Reasoning and Learning. Amsterdam : IOS Press, ©2016 9781614997337 Studies on the Semantic Web ; v. 028. http://id.loc.gov/authorities/names/no2009156151 |
| spellingShingle | Zese, Riccardo Probabilistic semantic web : reasoning and learning / Studies on the Semantic Web ; Part I. Introduction; Chapter 1. Semantic Web; 1.1 Description Logics and Semantic Web; 1.2 The Current Vision of the Semantic Web; Chapter 2. Probability; 2.1 Probabilistic Inference; 2.2 Probabilistic Learning; Chapter 3. Aims of the Thesis; Chapter 4. Structure of the Thesis; Part II. Description Logics; Chapter 5. Foundations of Description Logics; Chapter 6. Description Logics' Characteristics; 6.1 Concept and Role Constructors; 6.2 Family of DLs; 6.3 Knowledge Base; 6.3.1 TBox; 6.3.2 RBox; 6.3.3 ABox; 6.4 Semantics. Chapter 7. Significant Examples of Description Logics; Chapter 8. OWL: the Web Ontology Language; Chapter 9. Inference in Description Logics; 9.1 Approaches to Compute Explanations; 9.1.1 Solving min-a-enum: The Standard Definition; 9.1.2 Resolving min-a-enum: Pinpointing Formula; Part III. A Probabilistic Semantics for Description Logics; Chapter 10. Distribution Semantics; 10.1 Formal Definition; 10.2 PLP Languages under the Distribution Semantics; 10.2.1 Logic Programming; 10.2.2 LPAD; 10.2.3 ProbLog; 10.3 Inference in Probabilistic Logic Programming; 10.3.1 ProbLog Inference System. 10.3.2 PITA; 10.4 Learning in Probabilistic Logic Programming; Chapter 11. DISPONTE; Chapter 12. Probabilistic Description Logics; Part IV. Inference in Probabilistic DLs; Chapter 13. Inference; 13.1 Splitting Algorithm; 13.2 Binary Decision Diagrams; Chapter 14. BUNDLE; Chapter 15. TRILL; 15.1 TRILL on SWISH; Chapter 16. TRILL P; Chapter 17. Complexity of Inference; Chapter 18. Related Inference Systems; Chapter 19. Experiments; 19.1 BUNDLE: Comparison with PRONTO; 19.2 BUNDLE: Not Entailed Queries; 19.3 BUNDLE: Inference with Limited Number of Explanations; 19.4 BUNDLE: Scalability. 19.5 TRILL, TRILL P & BUNDLE: Comparing Different Approaches; 19.6 Discussion; Part V. Learning in Probabilistic DLs; Chapter 20. Learning; Chapter 21. EDGE: Parameter Learning; 21.1 Expectation Maximization Algorithm; 21.2 EDGE; Chapter 22. LEAP: Structure Learning; 22.1 CELOE; 22.2 LEAP; Chapter 23. Distributed Learning; 23.1 Map Reduce Approach; 23.2 The Message Passing Interface Standard; 23.3 EDGE MR; 23.4 LEAP MR; Chapter 24. Related Learning Systems; Chapter 25. Experiments; 25.1 EDGE: Comparison with Association Rules; 25.2 LEAP & EDGE: a Comparison Between Different Learning Problems. 25.3 EDGE MR: Parallelization Speedup; 25.4 EDGE MR: Memory Consumption; 25.5 LEAP MR: Parallelization Speedup; 25.6 Discussion; Part VI. Summary and Future Work; Chapter 26. Conclusion; Chapter 27. Future Work. Semantic Web. http://id.loc.gov/authorities/subjects/sh2002000569 Semantic computing. http://id.loc.gov/authorities/subjects/sh2009007899 Web sémantique. Informatique sémantique. COMPUTERS Intelligence (AI) & Semantics. bisacsh Semantic computing fast Semantic Web fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2002000569 http://id.loc.gov/authorities/subjects/sh2009007899 |
| title | Probabilistic semantic web : reasoning and learning / |
| title_auth | Probabilistic semantic web : reasoning and learning / |
| title_exact_search | Probabilistic semantic web : reasoning and learning / |
| title_full | Probabilistic semantic web : reasoning and learning / Riccardo Zese. |
| title_fullStr | Probabilistic semantic web : reasoning and learning / Riccardo Zese. |
| title_full_unstemmed | Probabilistic semantic web : reasoning and learning / Riccardo Zese. |
| title_short | Probabilistic semantic web : |
| title_sort | probabilistic semantic web reasoning and learning |
| title_sub | reasoning and learning / |
| topic | Semantic Web. http://id.loc.gov/authorities/subjects/sh2002000569 Semantic computing. http://id.loc.gov/authorities/subjects/sh2009007899 Web sémantique. Informatique sémantique. COMPUTERS Intelligence (AI) & Semantics. bisacsh Semantic computing fast Semantic Web fast |
| topic_facet | Semantic Web. Semantic computing. Web sémantique. Informatique sémantique. COMPUTERS Intelligence (AI) & Semantics. Semantic computing Semantic Web |
| work_keys_str_mv | AT zesericcardo probabilisticsemanticwebreasoningandlearning |