Network Ontology
Ramesh Subbaraman
Soumya Sen
UPENN, TCOM 799
Motivation
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Semantic web – i.e. make web machine
UNDERSTANDABLE.
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Ontology: vocabulary
- semantics
- relationships
- rules
Resource Description Framework –
RDF
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- resources
- properties
- statements
Resource Description Framework
Schema – RDFS
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A schema language to model the resources
Partial Example
Axioms in RDF(S)
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We have that:
so, in RDF we add a resource
where symmetric is agreed to mean
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composition
General Axiom
Why Network Ontology?
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Ontology is the shared understanding of
what concepts or specifications mean in a
particular domain.
Presently there isn’t any formal widespread
ontology that is being used to represent
networks (some proprietary ones exist).
A formal ontology will make creating,
updating, and simulating networks more
efficient.
Possible solution:
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Develop a Network Markup language used to
represent all information needed in a network.
By creating an XML-based ontology it will provide
greater flexibility when dealing with platforms and
programming.
NML has been designed with a top-down approach.
Identification of ‘classes’ and establish relationship
between them for different scenarios.
Validating relationship with Ontology languages.
Today’s topics:
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Network markup language for Network
Planning tool.
Framework for modeling wireless networks.
Ontology for modeling Sensor Networks.
Ontology of representation of sensor network
data.
Semantic services for sensor-rich Information
systems.
Network Planning Tools
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Expert systems like CACIT, Opnet are good but they
have some drawbacks. So new expert systems that
may be developed can use ontology based on NML
for representing the network layout.
Kinds of questions asked to the user:
-What kind of company?
-How many branches? Regional/HQ/global
-Workload estimate
-description of office dimension
Output of NPT:
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Output:
-Well illustrated network layout diagram
-satellite/LAN/local loops/wireless/bluetooth
-network plans for each office and each floor,
access points, routers.
-future extension capabilities etc.
Idea of using XML ontology:
Describing and identifying the commonly
used patterns in a network and capturing
them in the XML ontology.
The representation through relations and
tags help in easy interpretation by
automated processes.
NML DTD
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A DTD was created to be used in the digital
network advisor tool and captures the
necessary parameters to fully describe all
types of networks. The fields are determined
by the user’s answers, underlying
assumptions, and calculations.
NML DTD
XML output
Graphical representation of data extracted
from XML ontology
Proposed ontology for Wireless
networks framework:
“A framework for modeling sensor
networks”- Raja Jurdak, C. Lopes, P.BAldi, OOPSLA,’04.
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Proposes a framework for sensor network modeling
based on general features identified through analysis
of existing sensor networks.
The framework facilitates the modeling of new
sensor networks by characterizing them according to
these general features and providing a set of
performance goals.
Specification of network performance requirements
helps in selecting communication protocols.
Related Literature: “Ontology driven Adaptive-sensor networks”,
S.Avancha, C.Patel, A. Joshi, UMBC.
Elements in the framework of sensor
networks:
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Topology- affects network routing, power
consumption, battery life. Includes physical
(deployment area) and logical organization of
the network as well as sensor density.
Sensor networks can have distributed or a
clustered organization where selected nodes
handle forwarding.
Elements in the framework of sensor
networks (cont..):
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Network Setting- communication media
quality (noise, spreading, attenuation, multipath fading) and operating environment
(security).
Sensor Description- resources, memory,
battery, participation, processing speed.
Data flow- event-driven/demand-driven (SQL
type query), processing architecture, system
health.
Proposed ontology for Sensor
Networks:
“A Novel Ontology for Sensor Networks
Data”- M.Eid, R.Liscano, A. El Saddik, MCRLab
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Sensor data from a large number of nodes
need to be searched efficiently. Classical
retrieval techniques had poor performance.
Brings improvement in search engine
performance by utilizing captured
relationships.
This ontology is based on the IEEE 1451.4
smart transducers template description
language.
Components of Ontology:
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Classes or concepts that may have subclasses.
Properties or relationships that describe
various features and properties of the
concepts (also called slots).
Restrictions on slots that are superimposed
on the defined classes and/or properties to
define allowed values (domain & range).
1. Identification of the initial
taxonomy:
Step: List of concepts as described by the identified
terms and form the initial class taxonomy.
2. Properties & restrictions:
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Relationships among classes are usually
referred to as properties.
Property links an individual from its domain
to an individual of its range.
Concepts can be refined by superimposing
constraints and axioms expression
relationships.
Universal restriction & Existential restriction.
2. Properties & restrictions: (contd..)
3. Consistency Checking:
2 types of Ontology Tests:
- Subsumption test (Testing Class hierarchy)
- Consistency check (logical check)
Usage of possible Ontology development tools:
Protégé. Knowledge representation language
for modeling various data types of sensor
data is OWL-DL. Validation tool: RacerPro.
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“Towards Semantic Services for
Sensor-rich Information Systems”
- J.Liu, F.Zhao, Microsoft Research (publ. IEEE 2005)
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Need to describe the architecture of a semanticservice-oriented sensor information system platform.
The key to enabling scalable sensor is to define an
ontology and associated information hierarchy for
interpretation of raw data streams.
Systems may have mobile or stationary nodes, and
products from different vendors must interoperate.
Related literature: “Semantic Agent Technologies for tactical sensor
networks”, G.Jiang, W.Chung, G.Cybenko
Application in a garage parking sensor
system
Application in a garage parking sensor
system composed of semantic services
Advantages of Ontology designs
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Sharing common understanding of the
structure of information among people or
software agents.
Enabling reuse of domain knowledge.
Making domain assumptions explicit.
Separating the domain knowledge from the
operational knowledge
Analyzing domain knowledge.