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1. AN OVERVIEW ON ONTOLOGY
LEARNING ALGORITHM AND
ITS FUTURE RESEARCH SCOPE
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Tutors India
Group www.tutorsindia.com
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2. TODAY'S OUTLINE
INTRODUCTION
ONTOLOGIES LEARNING SYSTEMS
FUTURE SCOPE
CONCLUSION

3. INTRODUCTION
The high manual cost of ontology construction,
the constant change in science and knowledge
in general, the enormous amount of existing
text with numbers growing exponentially, and
the extensive need for a variety of ontology
type resources such as vocabularies,
taxonomies, and formal taxonomies are all
driving forces behind Ontology Learning.
Contd...

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Because of their widespread usage in Internet-based applications, ontologies have earned a
lot of popularity and recognition in the semantic web. In all artificially intelligent systems,
ontologies are frequently regarded as a valuable source of semantics and interoperability.
CONTD...

5. The exponential growth of unstructured data on the internet has made automated
ontology extraction from unstructured text a hot topic in study.
Several approaches based on a variety of techniques (machine learning, text
mining, knowledge representation and reasoning, information retrieval, and
natural language processing) are being presented to automate the process of
ontology collection.
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7. ONTOLOGIES LEARNING SYSTEMS
In addition to the approaches utilized by each system in terms
of the related goals to be performed, an overview of the
system in terms of its creators, the purpose behind the
ontology learning algorithm, and its application areas is
provided.
ASIUM is a semi-automated ontology learning system . The
goal of this method is to extract semantic knowledge from
texts and utilize it to transfer knowledge from one domain to
another.
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CONTD...

8. ASIUM performs ontology learning tasks using linguistics and statistics-based
approaches, such as preprocessing texts and identifying sub categorization
frames, extracting words and form ideas, and creating hierarchy.
Text-to-Onto is a semi-automated system that is part of the KAON
infrastructure for ontology maintenance. KAON is a complete set of tools for
creating and managing ontologies.
Text-to-Onto performs ontology learning tasks such as preparing texts and
extracting words, creating ideas, constructing hierarchy, identifying non-
taxonomic connections, and labeling non-taxonomic relations using linguistics
and statistics-based approaches.
CONTD...

9. TextStorm/Clouds, a semi-automated ontology
learning system, is part of the Dr. Divago idea
exchange and generating system.
The goal of this method is to create and develop a
domain ontology that can be used in Dr. Divago to
find resources in a multidomain environment and
make musical compositions or graphics.
TextStorm/Clouds performs ontology learning
tasks such as preprocessing texts and extracting
words, creating hierarchy, identifying non-
taxonomic connections, labelling non-taxonomic
relations, and extracting axioms using logic and
linguistics-based approaches.
CONTD...

10. SYNDIKATE is a self-contained automated ontology learning system. SYNDIKATE
performs ontology learning tasks such as extracting words, creating ideas,
constructing hierarchy, finding non-taxonomic connections, and labelling non-
taxonomic relations entirely using linguistics-based approaches.
Under the Federated European Tourist Information System6, OntoLearn is part of
a project to build an interoperable infrastructure for small and medium companies
in the tourism industry (FETISH). OntoLearn performs ontology learning tasks
such as preparing texts and extracting words, generating ideas, and constructing
hierarchies using linguistics and statistics-based approaches.
CONTD...

11. CRCTOL is a system for building ontologies from domain-specific documents
that stands for concept-relation-concept tuple-based ontology learning.
CRCTOL performs ontology learning tasks such as preparing texts, extracting
words and creating ideas, constructing hierarchy, and identifying non-
taxonomic connections using linguistics and statistics-based approaches.
The OntoGain system, developed by the Technical University of Crete, is
aimed at the unsupervised extraction of ontologies from unstructured text.
In two distinct fields, namely the medical and computer science
domains,OntoGain was compared to Text2Onto, the successor of Text-
Against-Onto.
CONTD...

12. To conduct ontology learning tasks such as preparing texts, extracting words
and creating ideas, constructing hierarchy, and discovering non-taxonomic
connections, OntoGain employs linguistics and statistics-based approaches
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13. There are numerous important issues that will likely define future research directions in
this area [ (1) the problem of authority, noise and rationality in Web data for ontology
learning; (2) the combination of social data into the learning procedure to include
consensus into ontology structure; (3) the plan of new techniques for manipulating the
structural richness of collaboratively maintained Web data; and (4) the representation of
ontological entities as lattices]. (5) the suitability of present techniques for learning
ontologies for different writing systems (e.g., alphabetic, logographic); (6) the
competence and robustness of present techniques for Web-scale ontology learning; (7)
the growing importance of ontology mapping as more ontologies become available; and
(8) the extensibility of existing lightweight ontologies to formal ones.
FUTURE SCOPE

14. CONCLUSION
Ontology learning techniques and applications is a
growing topic of study that aims to make the process
of ontology engineering easier.
Another key purpose for OL is to make it easier to
keep ontologies up to current. The assessment of
ontologies is an unresolved subject, and numerous
innovative techniques have been presented. In the
OL field, a variety of methods and tools are being
developed.
CONTD...

15. There is no one approach that will be effective by itself; instead, a
combination of them is advised based on the application problem.
Web-scale, open heterogeneous data repositories, social networks, formal
languages, and cross-language learning are some of the open research
topics connected to ontology learning.

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