1. Cover Today We will be able to ▫Write a Statement in N-Triple Format ▫Learn Creation of Abox with TBox ▫Use Eclipse ▫Use Command Line ▫Cover These Slides.

2. Assignment Take Intrusion Detection and make ▫A short narrative, in English ▫Email to scott@scottstreit.com ▫When I approve it. ▫Use Protoge to make an ontology ▫Monitor attacks on IP addresses.

3. 2.2.1.3 Requirements Common to Both Solution must be ▫Inexpensive ▫Easy to implement ▫Intuitive ▫Evolutionary, not revolutionary ▫Compatible with existing web standards

4. 2.2.1.3 Requirements Common to Both Solution must be ▫Inexpensive ▫Easy to implement ▫Intuitive ▫Evolutionary, not revolutionary ▫Compatible with existing web standards

5. 2.2.1.3 Requirements Common to Both Solution must be ▫Inexpensive ▫Easy to implement ▫Intuitive ▫Evolutionary, not revolutionary ▫Compatible with existing web standards

6. 2.2.2 Satisfy Computer Requirements Structured distributed representations to enable applications Supporting language

7. 2.2.2.1 Structured Representations Computers need ▫Consistently structured information collections ▫Inference rules to conduct automated reasoning ▫Representations formal enough to detect inconsistencies and errors ▫Network-distributed information to support scalability

8. 2.2.2.2 Supporting Language Need a tagged markup language to provide ▫Syntax  Language format rules; open & vendor-neutral ▫Semantics  Meaning of concepts; formal, finite, & extensible ▫Expressiveness  Richness; able to express concepts & relationships  Completeness, correctness, & efficiency (hardest!) ▫Standards  Common language for all

9. 2.2.3 Compromise Must balance need for structure with need for human-friendly data representations ▫True natural language processing not yet ready ▫Humans don’t like to process raw structured data Proposed solution ▫Humans must augment content with markup ▫Must show an ROI payoff for extra effort

10. 2.3 Semantic Web to the Rescue Next evolutionary generation of the web ▫Structured information representations provide explicit meaning ▫Information “marked up” according to language standards ▫Software provides new functionality by interpreting, exchanging, & processing meaning Technologies focus on information representations tied to explicit meaning

11. 2.3.1 Semantic Web History Term coined by Sir Tim Berners-Lee US Dept of Defense/DARPA created DAML ▫DARPA Agent Markup Language ▫Helped define critical concepts European Union created OIL ▫Ontology Interface Layer ▫Combined with DAML to create DAML+OIL W3C built on DAML+OIL to create OWL ▫Web Ontology Language (yes, it’s out of order) ▫First draft approved February 2004

12. 2.3.2 Semantic Web Vision Next generation of the web Vast object-oriented integrated knowledge base that can be accessed and inferenced via machine-understandable schemas Transparent to the end-user Link documents and the information in them Leverage the current web infrastructure Reduce the cost of performing tasks

13. 2.3.3 Populating the Semantic Web Developing representation standards ▫Scope the domain/analyze requirements ▫Define terms and relationships ▫Encode vocabulary & relationships (ontology) ▫Publish representation on servers Requires significant up-front effort, but Yields greater returns than current solutions Cost reduces as reuse grows

14. 2.3.4 Use Cases Tactical level functionality ▫Lower-level functions & basic operations ▫Behind the scenes Strategic applications ▫Higher-level compositions of tactical features ▫Provide more complex functionality ▫Customer-facing

15. 2.3.4.1 Tactical Services Describe distributed information ▫Harvest content, process, & exchange results Support queries ▫Answer questions & explain reasoning Support searching ▫Find information based on meaning, not keywords Support inferring ▫Drawing conclusions from explicit facts ▫Reduces size & complexity of knowledge bases

16. 2.3.4.2 Strategic Applications Vertical applications ▫Provide specialized services to a particular domain ▫E-commerce (B2B, B2C) Agent software ▫Autonomous; mobile; architecture-independent ▫Find & interpret information, act, report results Information management ▫Migrate intelligence from the software to the data ▫Provide new functionality without modifying code ▫Integrate repositories

17. 2.3.5 Appropriate Applications Semantic web applications appropriate to: ▫Publish content for both humans and computers ▫Share information without understanding model ▫Inferring new facts & joining information sources Characteristics of good candidate domains: ▫Well-understood but dynamic domain ▫Heterogeneous information sources ▫Existing information interchange requirements Not suited to binary data, e.g. image processing

18. 2.4 Semantic Web Intro Summary Existing challenges ▫Humans want information in readable formats ▫Computers need structured formats ▫Solution must minimize human investment, but meet computer needs Semantic web is the solution ▫Builds on the existing web ▫Supplies new information representation features ▫Presents information understandable to both

19. Chapter 3

20. 3 Ontologies Enable the Semantic Web Ontology definitions Development issues Description methods Ontology features Language issues

21. 3.1 Ontology Definitions Historical definition ▫Studies of the science of being, and the nature and organization of reality ▫Definitive classifications of objects & their relationships Other definitions ▫Computer science definition SCOTT ▫Types of ontologies ▫Gruber definition ▫OWL-specific ontology definitions

22. 3.1.1 Computer Science Definition Popularized by AI community Tbox ▫Terminogical components ▫Equivalent to “schema” ▫Define concepts ▫Semantic Web equivalent  Ontology Abox ▫Assertional components ▫Equivalent to “records” ▫Assert facts ▫Semantic Web equivalent  Individuals

23. 3.1.2 Types of Ontologies Many types ▫Domain ontologies ▫Metadata ontologies (Dublin Core) ▫Method/task ontologies Many ways to classify ontologies ▫Formality ▫Regularity ▫Expressiveness Taxonomy is not an Ontology ▫Hierarchy of concepts related with IS-A relationship ▫Can’t express complex relationships, or same level

24. 3.1.3 Gruber Definition “Formal specification of a conceptualization” – T. Gruber An ontology is a ▫Formally-described ▫Machine-readable ▫Collection of terms & their relationships ▫Expressed in a language ▫Stored in a file

25. 3.1.4 OWL-Specific Ontology Def’n Web Ontology Language (OWL) ontology ▫“An OWL-encoded, web-distributed vocabulary of declarative formalisms describing a model of a domain” Domain ▫A specific subject area or area of knowledge ▫Typically the focus of a particular community of interest Encode a model of the domain, not all of it

26. 3.2 Ontology Features Communicate a common understanding of a domain Declare explicit semantics Make expressive statements Support sharing of information

27. 3.2.1 Domain Understanding Provided by communities of interest ▫Example: restaurant association describes relationships between food items Ontology formally documents one common understanding of a domain ▫Reduces misunderstanding Shared and common understanding communicated between humans and software systems

28. 3.2.2 Explicit Semantics Semantics ▫Formal descriptions of terms and relationships ▫Traditionally coded into the software or schema ▫Document concepts using modeling primitives and semantic relationships ▫Make assumptions explicit ▫Reduce ambiguity ▫Enable interoperability Must be described formally to be processed

29. 3.2.3 Expressiveness “Extensiveness” of the ontology Must be expressive enough to ▫Represent formal semantics ▫Have reasoning properties to support inferencing Support canonical granular representations Limited to keep reasoning ▫Decidable ▫Scaleable

30. 3.2.4 Sharing Information OWL-compliant software can ▫Manipulate information internally ▫Interoperate with other software ▫Do semantic mapping between information sources Need to have a shared language and access to information

31. 3.3 Ontology Development Issues Authoring ontologies ▫Can be developed by anyone, but ▫Better if developed by consensus-based standards development groups ▫Vertical ontologies describe a domain ▫Horizontal ontologies span domains and describe basic concepts Separating ontologies from individuals ▫Usually a good idea ▫Sometimes not possible Committing to an ontology ▫Makes applications easier to understand, modify, reuse

32. 3.4 Describing Semantics Defining information representation building blocks Describing relationships between building blocks Describing relationships within building blocks

33. 3.4.1 Building Blocks Three basic blocks ▫Class constructs ▫Property constructs ▫Individual constructs Together, they describe a model of a domain Each type requires ▫A computer-understandable representation ▫Identifiers for referencing these representations

34. 3.4.1.1 Class Construct Similar to ▫“Class” in OO terminology ▫“Table” in relational DB terminology Group or set of objects with similar properties or characteristics (explicit or implicit) in common General statements can be made that apply to all members of the class Examples ▫Food ▫Menu Item ▫Person

35. 3.4.1.2 Property Construct Similar to ▫“Accessor method” in OO terminology ▫“Columns” or “fields” in relational DB terms Binary association that relates an object (instance) to a value Examples ▫Price ▫Size Unlike OO accessors, properties can be associated with multiple unrelated classes!

36. 3.4.1.3 Individuals Similar to ▫“Objects” in OO terminology ▫“Rows” or “records” in relational DB terminology Represent class object instances in the domain ▫Physical things ▫Virtual concepts Unlike objects, Individuals have no functionality Examples ▫KnightOwlRestaurant ▫Order456 Difference b/w individuals & classes not always clear Literal values (“1”, “A”) are special case of individuals

37. 3.4.2 Relating Constructs Need to describe relationships between building blocks “is an instance of” ▫Individual to Class “has value for” ▫Individual to Property Restrictions ▫Between Class and Property

38. 3.4.2.1 Relate Individuals & Classes Individuals are members of classes “Membership” or “is an instance of” relationship Must be explicitly stated Examples ▫“KnightOwlRestaurant” is an instance of “Restaurant” class ▫“Mark” is an instance of “Person” class

39. 3.4.2.2 Relate Individuals & Properties Individuals have attributes described by properties “has value for” relationship Example ▫“KeyLimePie” individual has value “$2” for the property “price” ▫“Mark” individual has value “34” for the property “age”

40. 3.4.2.3 Relate Classes & Properties Classes can restrict use of Properties in individuals ▫“IsBrotherOf” property range restricted to “Male”s Properties can be used to define Classes by defining membership in the class ▫Individual is member of class “Boy” iff Individual is in “Male” class and “Age” property value <= 18. Restrictions can constrain Property values ▫To be of a certain class (range) ▫To only describe particular classes (domain)

41. 3.4.3 Semantic Relationships in Blocks Must be able to describe semantic relationships within classes, properties, and individuals Synonymy Antonymy Hyponymy Meronymy

42. 3.4.3.1 Synonymy Relation Connects concepts with similar meaning ▫equals() in Java – same meaning, different instance Stricter form is equivalence (identical) ▫== in Java – same instance Class to Class ▫Noodles & Pasta; Soda & Pop Instance to Instance ▫Knight Owl Restaurant & franchiseProperty123 Property to Property ▫Cost & Price Allows merging concepts & linking heterogeneous knowledge bases =

43. 3.4.3.2 Antonymy Relation Opposite meaning Stricter form is disjointness Establishes dichotomy of meaning b/w terms Class to Class ▫Regular Price Menu Item & Sale Price Menu Item Instance to Instance Property to Property ≠

44. 3.4.3.3 Hyponymy Relation Specialization & generalization Creates taxonomic hierarchies Also called ▫“is-a” ▫“inheritance” ▫“subsumption” Transitive downward Better for permanent relationships Class to Class ▫Spaghetti “is-a” Pasta ▫New York Style Pizzeria “is-a” Italian Restaurant “is-a” Restaurant Property to Property ▫salePrice “is-a” price Δ

45. Meronymy/Hyponymy Relation Aggregation & composition Also called ▫“part-of” ▫“component of” Mereology (part-whole theory) Holonymy (whole-part theory) Closely related to “ownership” Transitive downward Class to Class ▫Meatball “part-of” Spaghetti and Meatballs Dish ▫Fork “part-of” Place Setting Individual to individual ▫Drink Order 321 “part-of” Restaurant Bill 789

46. 3.4.4 Semantics Summary Building BlocksRelationships ConstructDescription A group or set of individual objects with similar characteristics Associates attrib/value pairs with individuals, restricts classes Represents a specific instance object of a class FunctionalityRelationshipSummary Relating blocks to each other Individuals to Classes Membership Individuals to Properties Attribute values Classes to Properties Restrictions Describing relationships SynonymySimilarities AntonymyDifferences HyponymySpecialization MeronymyPart/whole HolonymyWhole/Part

47. 3.5 Ontology Languages Formal, parseable, & usable by software Define semantics in context-independent way Support some level of logic expression OWL based on: ▫Frame-based systems ▫Description logics

48. 3.5.1 Frame-based Systems Modeling primitives called “frames” (classes) Properties (attributes) are called “slots” Property values are called “fillers” Same slot name usable with different classes ▫Can specify different range & value restrictions

49. 3.5.2 Description Logics (DLs) Modeling primitives called “concepts” (classes) Properties (attributes) are called “roles” DLs also called “terminological logics” or “concept languages” Balance expressiveness with “decidability” ▫Whether software can reach a conclusion or not DL concepts defined by their objects’ membership constraints ▫Used to automatically derive classification taxonomies (hierarchies)

50. 3.5.2 Descriptions Logics cont’d DLs can specify ▫Class constructors ▫Property constructors ▫Axioms relating classes & properties Allow composite descriptions ▫E.g. restrictions on relationships between objects Use first-order logic Still decidable Support efficient inferencing

51. 3.6 Ontologies Summary Various definitions (AI, Gruber, OWL) Purposes ▫Communicate specification of domain ▫Declare explicit semantics ▫Support information sharing Different types; taxonomies most common Divided into Tbox & Abox ▫Tbox: schema, definitions of concepts ▫Abox: records, defintions of individuals/objects

52. 3.6 Ontologies Summary cont’d Building blocks ▫Class, Property, Individual Relationships between different block types ▫Membership, Attribute Values, Restrictions Relationships between same block types ▫Synonomy, Antonymy, Hyponymy, Meronymy, Holonymy Ontologies described using formal languages

53. Chapter 4

54. 4 OWL Introduction OWL Features Semantic Web’s Layered Architecture

55. 4.1 OWL Features Primary goals ▫Intuitive for humans, minimal investment ▫Expressive, with explicit semantics for software Can define and/or extend ontologies Supports scalability (needs some work) XML-based annotations Makes statements/assertions about classes, properties, & individuals Additional facts derived via inferencing

56. 4.2 Layered Architecture Applications } Implementation Layer Ontology Languages (OWL Full, OWL DL, and OWL Lite) } Logical Layer RDF SchemaIndividuals } Ontological Primitive Layer RDF and RDF/XML } Basic Relational Language Layer XML and XMLS Datatypes } Transport/Syntax Layer URIs and Namespaces } Symbol/Reference Layer

57. 4.2 Layered Architecture cont’d Layers illustrate rough dependencies ▫Each layer uses features of lower layers Implementation Layer ▫Provides specific applications Logical Layer ▫OWL supports formal semantics and reasoning Ontological Primitive Layer ▫RDFS defines vocabulary ▫Individuals defined in RDF

58. 4.2 Layered Architecture cont’d Relational Language Layer ▫RDF’s simple data model & syntax for making statements ▫Serialized as  RDF/XML or  N-triples Transport/Syntax Layer ▫Define primitive datatypes ▫Provide encoding format Symbolic/Reference Layer ▫Identify and reference classes, properties, and individuals

59. 4.3 Technology Support for Layers Symbol/Reference Layer ▫Provides identifiers & references to objects described in ontologies and instance files Transport/Syntax Layer ▫XML used to serialize OWL syntax ▫XMLS defines standard datatypes Basic Relational Layer ▫RDF makes statements using Attribute/Value pairs to describe objects

60. 4.3 Tech Support for Layers, cont’d Ontological Primitive Layer ▫RDFS provides basic vocabulary describing  Classes and subclasses  Properties and subproperties ▫Instances & property values specified by RDF & XMLS Logical Layer ▫OWL dialects (Full, DL, Lite) enhance RDFS Implementation Layer ▫Applications built using OWL Additional layers being considered for rules & trust

61. 4.4 OWL Introduction Summary Web Ontology Language (OWL) ▫Defined by the W3C ▫Used to make statements about  Classes  Properties  Individuals ▫Designed as a layered architecture built on  URIs & Namespaces  XML & XMLS  RDF & RDFS