This document discusses developing an ontology-based semantic web application for the biological domain. It introduces the need for semantic technologies to help machines better understand and combine biological information from different sources. The document outlines the methodology, which involves defining concepts, properties, and relations in the biological domain to create an ontology. It also discusses implementing a semantic web application using the Jena framework to retrieve and manipulate biological data modeled with ontologies and RDF. The goal is to build a semantic search framework to improve information retrieval for biologists.
2. Semantic Web for biological sciences 2
Agenda
Introduction
Problem Statement
Motivation
Literature Review
Methodology & Procedures
Implementation & Benefits
3. Introduction
The current Web represents information
using natural language, graphics and
multimedia (Ivan Herman).
Humans can process this information easily
They can deduce facts from partial information
They can create mental associations.
They use to various sensory information.
Semantic Web for biological sciences 3
4. Introduction
Tasks often require to combine data on the
Web:
Plant flora and Gene sequencing information
may come from different sites.
searches in different digital libraries etc.
Again, humans combine these information a
tedious process.
even different terminology's are used!
Semantic Web for biological sciences 4
5. Introduction
However: machines are ignorant
To make machines intelligent ontology's lie at
the foundation which provide sophisticated
frameworks to model the knowledge of a
domain.
The Semantic Web provides technologies to
make it possible! For example:
RDF ,OWL,SPARQL,OWL-API, User-Interface.
Semantic Web for biological sciences 5
6. Semantic Web for biological sciences 6
In the Life Science domain, a number of
documents presents already large on the web
and continues to grow at an exponential rate.
Current search engines not support for
retrieving the information;
millions of web documents retrieved
also most of the data is not publically
accessible due to the concept terminology.
The problem is how to provide the better
information retrieval support to end users .
Problem Statement
7. The ontologies reviewed are as follows:
Vocabularies and Retrieval Tools in Biomedicine:
Vanopstal, Robert (2011)
The OntoSeed ontology (2007)
Creating Ontologies for Content Representation
The RiboWeb ontology for Ribosome (2003)
Semantic Web for biological sciences 7
Literature Review
8. Semantic Web for biological sciences 8
Literature Review
OntoEdit: Guiding Ontology Development by
Methodology and Inferencing. (2009) In: R. Meersman,
Z. Tari et al. (eds.) Proceedings of the Confederated
International Conferences, University of California,
Methodology for Development and Employment of
Ontology Based Knowledge Management Applications:
York Sure, (2005)
KAON - Towards a large scale Semantic Web. E.
Bozsak, M. Ehrig, S. Handschuh et al. Proceedings of
EC-Web (in combination with DEXA2002).
9. Semantic Web for biological sciences 9
Motivation
It is an effort to conceptualize a biological
knowledge base for biologist, scientist, end-
users that aim to retrieve biological
information at web scale.
Semantic data model give solutions in such
domain.
In general it has a wider applicability than
relational or object oriented databases.
10. Developing Semantic Web
Application
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“Semantic Web Applications usually make some
ontological commitments.
They need to have hard-coded knowledge about
domain ontology obtained from experts which
contains classes i.e., plants, animals, angiosperm
gymnosperm along with their relations &
associations .
The application can also operate on extensions of
these core concepts e.g., stemming from dynamic
extension ontologies about specific types.
11. Developing Semantic Web Application
Semantic Web for biological sciences 11
A Semantic Web Application is still an application
research work, thus it needs to follow good practice
from Software Engineering.
Spiral Model inspired by the famous Boehm spiral is
used in this application development.
It is an extensible search framework for semantic web
applications.
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Semantic Web Architecture
the XML layer, which
represents data
the RDF layer, which
represents the meaning of
data
the Ontology layer, which
represents the formal rules
common agreement about
meaning of data.
the Logic layer, which
enables intelligent reasoning
with meaningful data.
14. Semantic Web for biological sciences 14
Methodology & Procedures
In order to built conceptual data model.
There is a need to clearly state the elements
that are abstracted.
These elements are concepts, properties of
concepts, relations and properties of
relations.
The meaning of each relation between two
concepts must be established.
It allows semantics in applications to
automatically derive information.
15. Semantic Web for biological sciences 15
Methodology & Procedures
Table 1. Definitions and Examples of Relations
Relations Definitions Examples
C is-a C1 Every C at any time is at
the same time a C1
Apple is-a Fruit
Lion is-a Animal
C part-of C1 Every C at any time is
part of some C1 at the
same time
Heart part-of Body
Seeds part-of Fruit
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Methodology & Procedures
Table 2. Algebraic Properties of Relations
Relations Transitive Symmetric Reflexive
is-a + - +
part-of + - +
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Methodology & Procedures
Semantic Web Application
Using Jena a java framework
To access and manipulate RDF data model
The object model offers methods to retrieve
concepts
object-properties
subject-properties ,etc
the rest is conventional programming…