1. KNOWLEDGE REPRESENTATION Ontologies Communication – Network Management Technologies Rashid Mijumbi Barcelona, April 2011

2. Data and Information Models Definition  A model is a representation of the entities in a managed environment.  Provides a common terminology for representing management information, relationships, constraints, rules, and operations to specify data syntax for a chosen domain of discourse DM 1 DM 3 IM 1 DM 2 Conceptual/Abstract model for designers and operators – High Level Representation Concrete model (for implementors) – Low Level Representation – More Details

3. Data Models (1) The “Necessary Evil” IPsec VPN MPLS VPNMPLS – TEMPLS – QoS Specific Device Model Specific Device #1 Specific Device #2 Specific Device #3 Specific Device #4 Translation Layer N Different Technologies Atleast N*M translations needed M Devices  Heterogeneity in systems makes different Data Models a necessity

4. Data Models (2) Problems Data harmonisation problem in Data Models Billing Application Customer Name: rashid.mijumbi Fault Management Application Customer Name: mrashid Security Application Customer Name: rmijumbi

5. Information Models (1) Abstraction, Data Harmonised (no Conflicts) Information Model Standards – Based Data Model Vendor – Based Data Model 1 : N 1 : M Enterprise wide managed objects define data Platform, language and protocol dictate vendor-independent possibilities Vendor implementations dictate working implementation

6. Information Models (2) Router Configuration Example CISCO Juniper Router(config)# router bgp autonomous-system Router(config-router)# neighbor { ip-address | peer-group-name} remote-as number Router(config-router)# neighbor ip-address activate routing-instances { routing-instance-name { protocols { bgp { group group-name; { peer-as as-number; neighbor ip-address; } } } } } -Different Languages -Different Semantics -Different programming models DEFINING BGP PEERS

7. Ontologies (1)

8. Ontologies (2)  Ontology refers to the shared understanding of some domain of interest which may be used as a unifying framework – Uschold and Gruininger (1996)  An ontology is an explicit specification of a conceptualisation. – Gruber 1993  Ontologies offer a formal mechanism for defining an understanding of data  Ontological Commitments  Ontology Requirements: Clarity, Coherence, Extensibility, Minimal encoding bias, Minimal ontological commitment

9. Ontology Languages  An ontology language is made up of three components  syntax,  semantics (model theory),  proof theory.  The syntax of an ontology language is itself divided into three areas  Logic lexicon, non-logic lexicon and Grammar.  By Syntax  CycL and KIF are examples of languages that support expressions in first-order logic.  By Structure  These languages use a markup scheme to encode knowledge, most commonly XML.  Ontology Inference Layer (OIL), OWL.

10. Ontology Tools  Ontology development tools  Ontology development tools can be further distinguished as: those that are independent of an ontology language, and those that are tightly dependent on one.  Protégé, Ontolingua.  Ontology merging tools  PROMPT, Chimaera.

11. Semantic Web (1) User lives in Barcelona and wants to buy a car locally. He can afford up to £500. He wants a red car. Hospitalet €400 maroon Old banger Ford Escort UsedCars Website User  A new form of web content that is meaningful to computers - Berners-Lee 2001 This is because computers cannot process the semantics that are associated with web content

12. Semantic Web (2) Ontologies: Define relationships: relationship between, say, a postcode, a town, a suburb, etc Mapping Service User Wordnet Ford New Cars BCN Cars UsedCars Website