(1) The document discusses the Semantic Web, ontologies, and ontology learning. It defines the Semantic Web as an extension of the current web that gives information well-defined meaning. (2) Ontologies are formal specifications of concepts and relations that provide shared meanings between machines and humans. (3) Ontology learning is the automatic or semi-automatic process of extracting ontological concepts and relations from text to build or enrich ontologies. The document outlines methods for ontology learning and its applications.

1. Semantic Web, Ontologies,
and Ontology Learning: Introduction
Ismael Ali
iali1@kent.edu
Doctoral Seminar Talk – CS @ Kent State University
December 11, 2015

2. (1) Semantic Web, (2)Ontologies,
(3) Ontology Learning

3. Outline:
• Semantic Web (SW)
– What is SW (Web 3.0)?
– Why to have SW?
• What are the Ontologies?
• Definition and Purpose of Learning Ontologies
• How people did Ontology Learning from Text?

4. Semantic Web (Web 3.0):
Definition

5. What Is the Semantic Web?
"The Semantic Web is an
extension of the current web in which
information is given well-defined meaning,
better enabling computers and people to work in cooperation.”
The Semantic Web
Tim Berners-Lee, James Hendler and Ora Lassila
Scientific American, May 2001

6. Web 1.0 and Web 2.0: Minimal machine-processable information dumb links
http://www.w3.org/2005/Talks/0623-sb-IEEEStorConf/Overview.html#(1)

7. Web 3.0, Semantic Web: Enhancement of the current web
More machine-processable informations

8. Web 3.0, Semantic Web: Enhancement of the current web
More machine-processable informations

9. Web 3.0, Semantic Web: Enhancement of the current web
More machine-processable informations

10. Web versions and their technologies

11. Semantic Web Applications
Main Application Domains
• Web Applications, Enterprise Integration,
Data Management, Life Sciences, Business
(Personal And Consumer Information)
Growing Industry
• Google (Knowledge Graph), Facebook (OGP),
IBM, Oracle, Adobe, Nokia, HP, many others

12. Semantic Web
Components and Technologies:
By Examples

13. Technologies which give semantics (meanings) to the data on the web
Semantic Web Stack:
Components and Technologies

14. Facts:
title(W3school.com,W3School.com)
author(W3school.com,“Jan Egil Refsnes”)
For example; here SW tells the machine (i.e. Web Crawler):
(1) what is the title of this webpage (as a Resource on the web 2.0),
(2) who is the author (by RDF data),
(3) what does it means by having a “title” and “author” (by ontologies
describing the RDF data).
SW Stack: Example

15. Facts:
title(W3school.com,W3School.com)
author(W3school.com,“Jan Egil Refsnes”)
For example; here SW tells the machine (i.e. Web Crawler):
(1) what is the title of this webpage (as a Resource on the web 2.0),
(2) who is the author (by RDF data),
(3) what does it means by having a “title” and “author” (by ontologies
describing the RDF data).
SW Stack: Example
Note:
Since these semantic data are
made for machine consumption,
they are embedded (coded) in
background (i.e. XML/RDF
format) and do not need to be
showed to the user

16. RDF Data Model
The core structure of the RDF
data is a set of triples, each
consisting of a subject, a
predicate and an object.
(W3school.com, title, W3School.com)
(W3school.com, author,“Jan Egil Refsnes”)
Facts:
title(W3school.com,W3School.com)
author(W3school.com,“Jan Egil Refsnes”)

17. Facts:
title(W3school.com,W3School.com)
author(W3school.com,“Jan Egil Refsnes”)
Using SW Techs for
Data representation
and serialization

18. Facts:
title(W3school.com,W3School.com)
author(W3school.com,“Jan Egil Refsnes”)
Using SW Techs for
Data representation
and serialization
Not done yet!
Now the machine has
the facts about the webpage represented
and serialized in an exchangeable format
Where are the meanings (semantics)?!

19. Facts:
title(W3school.com,W3School.com)
author(W3school.com,“Jan Egil Refsnes”)

20. http://dbpedia.org/ontology/author

21. http://dbpedia.org/”ONTOLOGY”/author

22. Ontology: Definition

23. Ontology
• Originally, the term "ontology" comes from the field of
philosophy that is concerned with the study of being or
existence. In computer science the ontology is defined as the
formal and explicit specification of a shared
conceptualization
[Guarino et al., 2009]:
– Formality: Ontologies to be implementable and coded by the
computers.
– Explicitness: Ontologies primitives namely concepts, relations
and other components of ontology are defined explicitly.
– Shared: Same domain ontologies are being used voluntarily by
semantic web applications as standard schemata for the
terms, concepts, and relations
– Conceptualization: Ontologies are abstract models consisting
of concepts that are relevant to the real world.

24. Different variation of ontologies

25. Different variation of ontologies
logical languages are more eligible for the formal,
explicit specification, and, thus, web ontologies

26. Example: Basic Ontology Components
are Concepts and Relations
http://www2002.org/CDROM/refereed/232/
Here we have set of concepts
with simple relation

27. Maedche, A.: Ontology Learning for the Semantic Web. Kluwer Academic Publishing
(2002)
Ontology in more formally way:

28. Maedche, A.: Ontology Learning for the Semantic Web. Kluwer Academic Publishing
(2002)

29. Ontologies (meanings) help:
(1) Inference and
(2) Consistency checking
THE TRUTH ABOUT TRIPLESTORES - Ontotext

30. Ontology Learning
from text

31. What is Ontology Learning?
• It is a automatic or semi-automatic process of
extracting ontological primitives from input data
in order to:
– Building an ontology from scratch (from data sources)
– Enriching existing ontology (integrate, match, or map
existing ontologies to get new ontologies)
* Ontological basic primitives are:
Concepts and Relations

32. Ontology Learning: General Architecture
Ontology
Learning
Domain Ontology
Domain Corpus

33. Ontology Learning: General Architecture
Pre-processing and
Transformation
Ontological
Information
Extraction
Ontological
Analyses and
Discovery
Ontology
Construction
Supportive Knowledge and Algorithms
Ontology
Learning
Domain Ontology
Domain Corpus

34. Types Of Inputs For Ontology Learning
L. Drumond and R. Girardi. A survey of ontology learning procedures. In F. L. G. de Freitas, H. Stuckenschmidt, H. S. Pinto, A. Malucelli, and Ó. Corcho,
editors, 3rd Workshop on Ontologies and their Applications, volume 427 of CEUR Workshop Proceedings, 2008.

35. Detailed Steps For Ontology Development
Buitelaar, P., Cimiano, P., Magnini, B.: Ontology learning from text: An overview. ontology learning from text: Methods, evaluation
and applications. Frontiers in Artificial Intelligence and Applications Series 123 (2005)
Layer Cake: Architecture for Ontology Learning from Unstructured Text
- Approaches to extract ontology components from unstructured text:
- Terms
- Synonyms
- Concepts
- Taxonomy (Concept Hierarchies)
- Relations
- Axioms and Rules

36. Why to learn (extract) ontologies?
- Applications -
• Knowledge management [Brewster et al., 2002]
• Information retrieval [Zhang et al., 2006]
• Information searching [Gulla et al., 2007]
• Semantic annotation [Mokarizadeh et al., 2010]
• Automated translation [Navigli et al., 2003]
• Information extraction [Li et al., 2007]
• Many more...

37. Ontology Learning:
Methods
from text

38. Ontology Learning As an
Interdisciplinary Field
Semantic Web
Natural Language
Processing,
Linguistics
Machine
Learning
Information Retrieval
Text mining
Info Extraction
Ontology
Learning

40. Maedche, A.: Ontology Learning for the Semantic Web. Kluwer Academic Publishing (2002)

41. References
 The Semantic Web. Scientific American, Berners-Lee, T., Hendler, J.,
Lassila, O., 2001
 “What is an ontology?”, Guarino, N., Oberle, D., and Staab, S., 2009
Handbook on Ontologies. Springer, Berlin, Germany
 Ontology Learning and Population from Text: Algorithms, Evaluation
and Applications. Cimiano, P., Springer, 2006
 Ontology Learning for the Semantic Web. Maedche, A., Kluwer Academic
Publishing, 2002
 A survey of ontology learning procedures. L. Drumond and R. Girardi.,
2008
 Ontology learning from text: An overview, Buitelaar, P., Cimiano, P.,
Magnini, B., 2005
 Perspectives on Ontology Learning, J Lehmann, J Völker - 2014
 Natural Language Processing with Python, Steven Bird, Ewan
Klein, Edward Loper, 2009

42. Thank you.