1. CS652 Spring 2004 Summary

2. Course Objectives  Learn how to extract, structure, and integrate Web information  Learn what the Semantic Web is  Learn how to build ontologies for the Semantic Web  Investigate class-related research topics  Be introduced to Semantic Web services

3. Generally Applicable Ideas  Semantic Understanding Data: attribute-value pairs Information: data in a conceptual model Knowledge: information with agreement Meaning: useful knowledge  Measuring Success Recall: NrCorrect/TotalCorrect Precision: NrCorrect/(NrCorrect+NrIncorrect) F-measure: (β 2 +1)PR/(β 2 P+R)

4. Information Extraction  Get relevant information  Not: Information retrieval: get relevant pages Web mining: discover unknown associations  Wrapper: maps data to a suitable format  Generation techniques Machine learning (e.g. RAPIER) Natural language processing (e.g. RAPIER) Hidden Markov Models By-example generation tools (e.g. Lixto) By-pattern generation (e.g. RoadRunner)  Wrapper Maintenance

5. Information Extraction – BYU Ontos  Ontology-based  Data frames  Strengths Resilient to page changes Robust across sites within the same domain Works well with all types of data-rich text  Weaknesses Hand-crafted ontologies and data frames Requires record-boundary recognition Does not learn  Applications Extraction High-precision classification Schema mapping Semantic Web annotation Agent communication Ontology generation

6. Semantic Web  Tim Berners-Lee “information [has a] well-defined meaning” “[enables] computers and people to work in cooperation”  Adds context and structure via metadata  Agent computing paradigm  Knowledge markup; semantic annotation

7. Ontologies  “a formal, explicit specification of a shared conceptualization” [Gruber93] Formal: machine readable; FOL Explicit: concepts and constraints explicitly defined Shared: community accepted Conceptualization: abstract model (OSM)  “shared vocabulary”

8. Ontology Formalism Ontology O = where V = vocabulary = predicate symbols (each with some arity) A = axioms = formulas (constraints and rules) Predicates: Owner(x), Vehicle(x), Car(x), Truck(x), Owner(x) owns Vehicle(y) Formulas: x(Car(x)Truck(x)  Vehicle(x)) x(Owner(x)   1 y(Owner(x) owns Vehicle(y)) Inference Rules: TruckOwner(x) :- Owner(x), Owner(x) owns Vehicle(y), Truck(y)

9. Semantic Web Ontologies  RDF  DAML+OIL  OWL

10. Semantic Web Annotation with BYU Ontos osm.cs.byu.edu/CS652s04/ontologies/annotatedPages/carSrch1_semweb.html osm.cs.byu.edu/CS652s04/ontologies/OWL/carads.owl BYU Ontos Extraction Ontology OWL Ontology Annotated Semantic Web Page

11. Ontology Generation for the Semantic Web  Necessary for the Semantic Web  Ontology engineering Tools Methodology Languages (e.g. SHOE, OWL)  Semiautomatic generation NLP + machine learning (e.g. OntoText) Create from dictionary or lexicon (e.g. Doddle) Generation from tables (e.g. TANGO)  Ontology maintenance

12. Ontology Libraries for the Semantic Web  Locating ontologies Indexing and organization Search mechanisms  Reusing ontologies Find one and modify Find several, merge and modify

13. Ontology Mapping, Merging, and Integration for the Semantic Web  Ontology reuse  Heterogeneous agent communication Agent commitment to a new ontology On the fly: map, merge, integrate (nontrivial to automate)  Can we do well enough?  Can we synergistically involve a user?  Information extraction wrt target Table extraction (BYU Ontos) Semiautomatic wrapper/mediator construction by automatically providing mappings

14. Schema Mapping  Schema-level matchers Name matchers (dictionaries – WordNet) Structural context matchers  Instance-level matchers Value characteristics Data-frame matchers  Mapping cardinality 1:1 (direct) 1:n, n:1, n:m (indirect, complex)  Multi-faceted mapping techniques

15. Schema Integration  FCA merge using lattices  Global as View (GAV) Global mediator relations are views over source relations  Dynamic mediator schema – changes to accommodate new sources (hard to add new sources)  Query only requires view unfolding  Good for static, centralized systems TSIMMIS  Local as View (LAV) Local source relations are views over mediator relations  Fixed mediator schema – new sources identify components covered (easy to add new sources)  Complex query rewriting  Good for dynamic, distributed systems Information Manifold

16. What is your dream for the Semantic Web?  Intelligent personal agents that can: Gather (just) the information we want and deliver it to us when we want it Help us with scheduling Help us buy the goods we want Negotiate and conduct business for us …  Intelligent business agents  Intelligent discovery agents …… What can you do to make your dreams come true?