Develop a semantic annotation framework to annotate semantically some datasets using several ontologies

1. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
TRAVAUX PRATIQUE ENCADRES
Theme : Develop a semantic annotation framework
to annotate semantically some datasets using several
ontologies
Présenté par : SONFACK SOUNCHIO Serge
Encadrant : Dr Pierre LARMANDE
3 octobre 2018 1 / 13

2. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
1 Introduction
2 Project
3 Theoretical Solution
4 Practical solution
5 Achievements
6 Demo
7 Conclusion
3 octobre 2018 2 / 13

3. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
Introduction
Internet, the Web and semantic Web has brought us in the
world of data and sharing, collaborating are important factors
of our growth today.
One of the key issues related to this fact is the understanding
of these data.
Figure – A world of data and collaboration
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4. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
Projet
AgroLD projet[1] is a RDF knowledge base designed to
integrate data from various publically important plant centric
data sources such as Gramene, Oryzabase, TAIR.
In order to improve solutions in this erea of crops, researchers
have to understand data coming from various fields, which are
highly distributed
Figure – Understanding data from other domain
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5. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
Semantic annotation
Annotation : Annotation is a set of explanatory notes that
accompanies a text[5] .
In information science, an annotation is a graphic or textual
information linked to a document or simply placed on top of it.
Semantic : Using semantic web technologies
URI :Uniform Resource Identifier
(a) Concept matching (b) Semantic annotation
Figure – Annotation system
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6. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
Concepts
Ontology :is a formal, explicit specification of a shared
conceptualisation.
Knowledge base system
RDF : Resource Description Framework
Figure – Architecture
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7. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
Architecture
GUI : User interface - API : Extending software
Query processing - Storage and Data access
Document management
Figure – Architecture
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8. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
How it works
Storing Ontology =
RDFLib RDF extration (from URL or owl file) to JSON
format
Elasticsearch Indexing - Type - Documents
Query processing
regular expression
stopword removal
tokenize
stemming
Access Data (access)
constructed query
Elasticsearch search API
Access Data ( process result) using string similarity
algorithm to classify the result
API Python Flask REST API
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9. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
Tools
To achieve this work, many tools were used :
Programming language : Python ;
Python Framework : Flask[4]
CSS Framework : Bootstrap
NLP : Python NLTK[2]
Query Ontology : Python RDFLib
Data storage : Elasticsearch[3]
Versioning system : Git + Github
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10. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
Achievements
Storing Ontologies in document base management system
Concepts matching
In local DMS
Online
Search concepts using ID
Semantic annotation
API
To be done : Storing annotation ( In one or many graph )
Figure – Work done
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11. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
(a) Home page presenting API
(b) User interface
Figure – Some views
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12. Introduction
Project
Theoretical
Solution
Practical
solution
Achievements
Demo
Conclusion
Reminder
We were asked to develop a software that will be used to
annotate using ontologies.
Work done
We develop a software for semantic annotation using Python
RDFLib library, web technologies and Elasticsearch for indexing
data extracted from ontologies.
Future
See how to integrate Machine Learning in other to make the
software intelligent and more accurate
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13. Annexe
For Further Reading
Références
[1] Agrold :home.
http://volvestre.cirad.fr:8080/agrold/. (Accessed
on 10/03/2018).
[2] Natural language toolkit — nltk 3.3 documentation.
https://www.nltk.org/. (Accessed on 10/03/2018).
[3] Open source search & analytics · elasticsearch | elastic.
https://www.elastic.co/. (Accessed on 10/03/2018).
[4] Welcome | flask (a python microframework).
http://flask.pocoo.org/. (Accessed on 10/03/2018).
[5] Yongxin Liao, Mario Lezoche, Hervé Panetto, and Nacer
Boudjlida. Why, where and how to use semantic annotation
for systems interoperability. In 1st UNITE Doctoral
Symposium, pages 71–78, 2011.
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