1. Web3.0 and Language Resources Knowledge Media Institute (KMi) The Open University Semantic Technologies @ KMi

4. Knowledge-level Architectures for Sharing and Reuse Application of the modelling paradigm to the specification and use of libraries of reusable components for knowledge systems Knowledge-level Architectures for Sharing and Reuse

7. A Constructive Approach... Let’s define our own framework...

14. Task-Method Structures Problem Type Primitive PSM

16. Picture so far.. Problem Solving Method Classification Simple Classifier Lunar rocks Application Model Generic Task Multi-Functional Domain

22. Complete Picture Problem Solving Method Application Model Generic Task Multi-Functional Domain Mapping Knowledge Application-specific Problem-Solving Knowledge Application Configuration

23. Detailed Example: A Library of Components for Classification

25. Example Observables Candidate Sols. Criterion Classification Solution {background=green; area=china...} Complete-coverage-criterion (every observable has to be explained) {chinese-granny, dutch-granny, etc..} {chinese-granny}

32. Hierarchy of Criteria Match Criterion Match Score Comparison Rel Macro Score Mechanism Feature Score Mechanism Match Score Mechanism Solution Criterion

46. Task-Method Hierarchy

48. Knowledge fusion scenario RDF Images Other data Annotation Fusion Text Internal corporate reports (Intranet) Pre-defined public sources (WWW) Domain ontology KnoFuss Knowledge base

49. Fusion workflow Source KB Target KB SPARQL query translation Knowledge fusion Ontology integration Knowledge base integration Ontology matching Instance transformation Coreference resolution Dependency processing

55. New paradigm: use of background knowledge A B Background Knowledge (external source) A’ B’ R R

57. The Question is … How to combine online ontologies to derive mappings?

58. Strategy 1 - Definition Find ontologies that contain equivalent classes for A and B and use their relationship in the ontologies to derive the mapping. A B rel Semantic Web A 1 ’ B 1 ’ A 2 ’ B 2 ’ A n ’ B n ’ O 1 O 2 O n For each ontology use these rules: … These rules can be extended to take into account indirect relations between A’ and B’, e.g., between parents of A’ and B’:

59. Strategy 1- Examples But what if there exists no ontology that contains both A and B? ka2.rdf Researcher AcademicStaff Semantic Web Researcher AcademicStaff ISWC SWRC Beef Food Semantic Web Beef RedMeat Tap Food MeatOrPoultry SR-16 FAO_Agrovoc

60. Strategy 2 - Definition Principle: If no ontologies are found that contain the two terms then combine information from multiple ontologies to find a mapping. A B rel Semantic Web A’ B C C’ B’ rel rel Details: (1) Select all ontologies containing A’ equiv. with A (2) For each ontology containing A’: (a) if find relation between C and B. (b) if find relation between C and B. Details: (1) Select all ontologies containing A’ equiv. with A (2) For each ontology containing A’: (a) if find relation between C and B. (b) if find relation between C and B.

61. Strategy 2 - Examples Vs. (midlevel-onto) (Tap) Ex1: Vs. Ex2: (r1) (pizza-to-go) (SUMO) (Same results for Duck, Goose, Turkey) (r1) Vs. Ex3: (pizza-to-go) (wine.owl) (r3)

66. is a Search Engine for the Semantic Web Gateway

67. Architecture

68. Web Interface

69. Web Interface Advanced Keyword Search

70. Web Interface Ontology Exploration

71. Web Interface Ontology Metadata

72. Web Interface Querying

74. Next Generation Semantic Web Applications WATSON enables a new generation of Semantic Web applications that need to access and reuse semantic information distributed on the entire Web.

75. Examples of NGSW

79. PowerAqua Open domain QA by exploring distributed semantic data. Natural language question Answers from online semantic data