Contents Info

Probabilistic semantic web : reasoning and learning

Title/Author: Probabilistic semantic web : reasoning and learning / Riccardo Zese

Formatted Contents Note: 완전내용Part 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.

Formatted Contents Note: 완전내용Chapter 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.

Formatted Contents Note: 완전내용10.3.2 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.

Formatted Contents Note: 완전내용19.5 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.

Formatted Contents Note: 완전내용25.3 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.

Material
Reg No.	Call No.	Location	Status	Lend Info
E017245	E	E-Book Library	E-Book	E-Book 마이폴더 부재도서신고

* Reservations are available in the borrowing book. To make reservations, Please click the reservation button

로그인 후 이용 가능합니다.

본문

서브메뉴