1. GloServ: Global Service Discovery Architecture Knarig Arabshian and Henning Schulzrinne IRT internal talk April 26, 2005

2. Overview Motivation Overview of OWL Architecture Registration Querying Future Work

3. Motivation Why Global Services Ubiquitous computing is becoming prevalent in today’s society Traveler visiting a new city wants to know all classical music events. Doctor visiting a hospital wants to know medical services in this hospital. Visitor in starbucks wants to know if it offers local internet TV. What are the Challenges? Service description and querying Server bootstrapping on global scale Service discovery should be global

4. OWL Overview OWL: Web Ontology Language Developed by World Wide Web Consortium (W3C) Approved as a standard for the Semantic Web Why OWL? Service description: creating a service classification ontology Server bootstrapping: using service classification to map services to servers Three sublanguages of OWL OWL Lite OWL DL OWL Full

5. OWL Sublanguages OWL Lite Least expressive of the three sublanguages Supports a classification hierarchy (like RDFS) Supports core constraints of classes and properties. OWL DL Extends OWL Lite to include description logics (disjointness, union, intersection, etc.) Supports maximum expressiveness All conclusions are guaranteed to be computable and decideable (finishing in finite time) Includes all OWL language constructs OWL Full Similar to OWL DL Main difference: a class can be treated simultaneously as a collection of individuals and as an individual in its own right Choose OWL DL for GloServ: A service class represents a collection of individuals Use OWL DL reasoners such as Racer to check for the soundness of OWL documents

6. Characteristics of OWL Classes Contain individuals, which are instances of the class and other subclasses Set operators on classes Union, intersection and complement Equivalence, disjointness, enumeration Property Binary relation that specifies class characteristics Two types of properties: datatype and object properties Datatype properties: Relations between instances of classes and RDF literals or XML schema datatypes (string, integer, etc.) Object properties: Relations between instances of two classes Logical capabilities of properties: transitive, symmetric, inverse and functional.

7. OWL Examples Object Property Ontology Mapping Intersection Disjointness

8. Composing Ontologies Create a primitive tree which is a hierarchical tree of primitive concepts Resides on the top level of the ontology Constructed so that each concept has only one parent and disjoint siblings Primitive skeletons distinguish two types of concepts: Self-standing concepts: concepts include “things” that are part of the physical world such as “animals” or “organizations” Partitioning concepts: values that partition self-standing concepts such as “small,medium, large” By using primitive skeletons, the evolution, sharing and re- use of ontologies is greatly simplified. Once primitive skeleton is formed, descriptions and definitions are created to express the relations between those primitives.

9. Primitive Skeleton Original Ontology

10. GloServ Architecture Hybrid hierarchical and peer-to-peer architecture Hierarchy High-level services established hierarchically Primitive skeleton ontology used for separating high-level services Each node will know about other high-level servers by looking up primitive ontology model Disjoint servers are handle service classes that are completely unrelated to each other Peer-to-Peer Servers who hold information about the same or equivalent service class connected to each other in a peer-to-peer network Load distribution during query processing Faster querying when the data is distributed according to content and each server handles a set of information.

11. GloServ Architecture

12. Elements within a GloServer Service Classification Ontology Not prone to frequent changes--distributed and cached across the GloServ network Each high level service will have a set of properties that will be inherited by all of its children. Additional properties may exist for particular service type Thesaurus Ontology maps synonymous words to each of the service terms in the service classification ontology greater degree of accuracy in finding the correct server P2P Data structure (CAN lookup table) Constructed according to the data in each class (class instances) Each instance represents a registered service. Connects servers of the same type to each other in a peer-to-peer network. Novel mapping algorithm combines benefits of OWL and CAN to map content of service instances to nodes in a peer-to-peer network.

13. Server Bootstrapping 2)Map the word “inn” to “hotel” 1)Query for “inn” comes in 4)Send the query to the closest high-level server that is known 3)Look up the domain of the equivalent server or closely related server in the primitive skeleton ontology

14. CAN: Content Addressable Network Structured peer-to-peer network Uses key-value pair D-dimensional space divided amongst nodes Each node is aware of its logical neighbors Key is hashed to a point P in D-dimensional space Host at point P provides value for the key

15. Mapping OWL to a CAN OWL instances may have: l mandatory object properties m optional object properties n data properties (mandatory or optional) All possible property combinations: Converting OWL instances to vector keys 1 2 3

16. Mapping Vector Keys to CAN CAN is most appropriate peer-to-peer network for exact and approximate matching Generated vector keys distributed in a CAN Instead of using random keys for each dimension, use the generated keys by using a property per dimension for the d -dimension key

17. GloServ Querying When the correct gloserver is contacted, it obtains the user query. Query input done either through user form, or by automatically filling out an OWL ontology skeleton Anticipate that GloServ will be used in context- aware and pervasive computing environments where a user’s preferences are detected and user input relied on Query propagation either depends on user satisfaction or automatically travels to all possible routes up to a threshold value

18. GloServ Querying Exact matching Populated properties of the query are analyzed Exact query combination is generated If user is querying for Sports activity in Arizona then vector key is generated and mapped to server handling these properties. Approximate matching Keys of geographically nearby locations and related activities to sports are generated All combinations of these are queried for to give the user an approximate result. Related keys obtained by finding all classes that are related to the property value

19. GloServ Registration Keys generated same way as in querying Registration information distributed to nodes carrying related information. If registration node not leaf node reference to the registration instance propagates to other related servers (node’s children or related siblings) Related servers are determined by observing service classification ontology

20. Related classes in ontology BackPackersDestination and BudgetHotelDestination are related siblings (not disjoint) The class Destination specifies possible travel destinations Subclasses: BackPackersDestination and BudgetHotelDestination asserted necessary and sufficient conditions of BackpackersDestination class: Necessary and sufficient conditions of BudgetHotelDestination are: These related classes will have access to each other’s information

21. Conclusion and Future Work GloServ is a hybrid hierarchical and peer-to- peer global service discovery system It uses OWL for service classification, server bootstrapping, service querying and registration Extend GloServ to be used in context-aware environments Discover user’s context and provide appropriate services Design extensions to GloServ that monitor users and service usage