CHAPTER 1
INTRODUCTION
The key to this goal is the Semantic Web and its technologies. Tim
Berners-Lee coined the vision of a Semantic Web as an extension of the current
World Wide Web that does not only provide information at the syntactic level to
human users, but also at a machine-understandable, semantic level to machines.
[1]
In the Semantic Web, background knowledge about the meaning of web
resources can be stored as machine-processable (meta-) data. Services for
finding, integrating, or connecting information may be based on these semantic
descriptions.[1]
This case study subject is developing next-generation Historical castles
information system using semantic web technologies. If you want to search
something about castles over Besparmak Mountains, you can find a lot of
information. But that knowledge is isolated from each other, and most of them
don’t include detail information as well. The aim of this study is how to gap
between two isolated data may be narrowed. The key to this aim is the semantic
web and its technologies, which extension of the current World Wide Web that
does not only provide information at the syntactic level to human users, but also
at a machine-understandable, semantic level to machines.
In my case study, I mention that historical castles over Besparmak
mountain information systems by semantic web technologies. I have developed
ontology of castles. Which is contains castle classes and their properties. My
system includes two different parts, first one is the ontology part and the second
one is the semantic search part. In the first part, the castle information, the
relationships between them and their properties are defined. Some semantic
search have done to find a castle with desired feature. For example, if somebody
wants to find knowledge about castles, they can reach easily all kind of
1
information’s. Or someone can interest about the castle which has an entrance
from the east they can easily find.
The organization of the rest of the report as follows. In the second part
provides detailed information about the implementation of the case study, and
comparison between the semantic search and regular search. Conclusions are
presented in chapter 3.
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CHAPTER 2
DETAIL INFORMATION ABOUT THE CASE STUDY
This chapter gives detail information about semantic information system
historical castles over Besparmak Mountains. Firstly I am going to explain the
platform that I used, and second part I will explain my ontology structure, and end
the end I am going to compare SW search and SQL search.
2.1 Platform, Tools and Technology
In my case study, I used Protégé 2.1.2 which is an integrated tool used by
domain experts and system developers to develop knowledge-based systems.
Protégé allows the user to construction a domain, customize data entry forms
and end enter data.[2]
Basically protégé has three different tabs, which are Classes Tab,
Instance Tab and Forms Tab. Classes Tab is an ontology editor which you can
use to define classes and class hierarchy, slots and slot-value restrictions,
relationships between classes and properties of these relationships. Protégé
generates a default form for acquiring instances based on the types of the slots
that you specified. By using the Forms Tab, you can change the default form by
rearranging the fields on the screen, changing size, label, and other properties
for any slot. The Instances Tab is a knowledge-acquisition tool which you can
use to acquire instances of the classes defined in the ontology. [2]
Also, it is a platform that can be extended with graphical widgets for
tables, diagrams, animation components to access other knowledge-based
systems embedded applications. In addition it is a library which other applications
can use to access and display knowledge bases.
In addition to the Classes Tab, Instance Tab and Forms Tab, I used
Algernon Tab and ezOWL Tab as well.
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The Algernon tab of Protégé was used for the semantic search part, which
is a Protégé tab plugin. Algernon supports forward and backward rule-based
processing of frame-based knowledge bases, and efficiently stores and retrieves
information in ontologies and knowledge bases.[3]
ezOWL is a Visual Semantic Web Ontology Editor. Also ezowl Plugin is a
Visual OWL eitor for protégé.[4]
2.2 Ontology Structure
There are eight different classes, which are castle, construction, contain,
distance, modify, usage, view and include in my ontology. Also, there are seven
different relations (object property) between these classes.
Castle is the main class in the Historical Castles over Besparmak
Mountains. It has a lot of properties, such as, height, position, name,
name_means, other_name, city, entrance, first_referance and section.
Modify is the one of the other class in this case study, which is include
modify purpose, year and by properties. Also distance is the one of the other
class name. It include distance from city center to castle, its properties are,
distance girne (d_girne), magusa, lefkosa and guzelyurt.
Some times different castle include different rooms type, for example
some of them include royal place, dormitories, stooge, cistern, barn and/or
kitchen but some of them don’t include, so I create a class for this kind of things,
which name is contain. Their attribute names are courtyard, kitchen, royal place,
cistern, barn, dormitories and stooge.
You can see related full ontology diagram in an appendix.
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2.3 Search

Listing all instance of castles

Find a castles which is higher than 720m

Find a castles which entrance is east
5

Find a castle usage type

Find a castle which is used as a prison

Find a castle which has a royal place
6

Find a castle which are located less than 40 km distance from the girne
city center

Find a castle which are located in Girne

Find a castle’s modified date, which are the located less than 40 km from
Lefkosa
7

Find a castle, which has a royal place, and located less than 36 km from
the Girne

Find a castle, which are modified in 12. century and located less than 70
km from the Lefkosa

Find a castle name, castle height and check if they have a east coast view
which are modified in 11. century
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2.4 COMPARE SW SEARCH AND SQL SEARCH
In a previous section I have done some searches used by protégé tools.
And I decided to do same search using SQL for comparing performance and
strength. When I tried to do same searches I realized some implementations
were not possible. Even some of them possible at this time the search way was
very complicated, so I realized that SQL is less powerful than semantic search,
because SQL search environment doesn’t contain enough feature.
I used run ontology test, which is inside the protégé, for validation and it
run correctly.
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CHAPTER 3
CONCLUSION
In this project, I implemented semantic information system historical
castles over Besparmak Mountains.
I have developed ontology of castles. Which is contains eight different
classes and their properties. My system includes two different parts, first one is
the ontology part and the second one is the semantic search part. In the first part,
the castle information, the relationships between them and their properties are
defined. Some semantic searches have done to find a castle with desired
feature.
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REFERENCES
1) Alexander Maedche and Steffen Staab “Applying Semantic WebTechnologies
for Tourism Information Systems”
2) http://protege.stanford.edu
3) http://algernon-j.sourceforge.net
4) http://iweb.etri.re.kr/ezowl
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APPENDIX
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