Today, we can find many search engines which provide us with information
which is more operational in nature. None of the search engines provide domain
specific information. This becomes very troublesome to a novice user who wishes
to have information in a particular domain. In this paper, we have developed an
ontology which can be used by a domain specific search engine. We have
developed an ontology on human anatomy, which captures information regarding
cardiovascular system, digestive system, skeleton and nervous system. This
information can be used by people working in medical and health care domain.Comment: Proceedings of 5th CSI National Conference on Education and Research.
  Organized by Lingayay University, Faridabad. Sponsored by Computer Society of
  India and IEEE Delhi Chapter. Proceedings published by Lingayay University
  Pres

Joshi, Nisheeth

Mathur, Iti

Vashisth, Archana

English

arXiv

Today, we can find many search engines which provide us with information which is more operational in nature. None of the search engines provide domain specific information. This becomes very troublesome to a novice user who wishes to have information in a particular domain. In this paper, we have developed an ontology which can be used by a domain specific search engine. We have developed an ontology on human anatomy, which captures information regarding cardiovascular system, digestive system, skeleton and nervous system. This information can be used by people working in medical and health care domain

CogPrints Cognitive Sciences Eprint Archive

OntoAna: Domain Ontology for Human Anatomy

Cognitive Sciences ePrint Archive

OntoAna: Domain Ontology for Human Anatomy 
 
Archana Vashisth, Iti Mathur, Nisheeth Joshi 
Department of Computer Science 
Apaji Institute 
Banasthali Univeristy 
Rajasthan, India 
archana.sharma3010@gmail.com, mathur_iti@rediffmail.com, nisheeth.joshi@rediffmail.com 
 
   
Abstract- Today, we can find many search engines which provide 
us with information which is more operational in nature. None of 
the search engines provide domain specific information. This 
becomes very troublesome to a novice user who wishes to have 
information in a particular domain. In this paper, we have 
developed an ontology which can be used by a domain specific 
search engine. We have developed an ontology on human 
anatomy, which captures information regarding cardiovascular 
system, digestive system, skeleton and nervous system. This 
information can be used by people working in medical and health 
care domain. 
 
Keywords: Ontology Creation, Protégé, Taxonomies, Concept 
Hierarchies, Human Anatomy. 
 
I. INTRODUCTION 
 
These days, ontologies are being considered as the most 
important knowledge representation technique because they 
can semantically capture information. That’s why they are 
being termed as the backbone of knowledge systems. A large 
number of applications are using knowledge databases for 
searching information in a particular domain, on a particular 
topic [1].  
 
The term ontology has been taken from the Greek words 
ontologia which means talking about beings. It is just a term 
in philosophy & which means “theory of existence”. This term 
was first coined by Plato in 3rd century B.C. Later Aristotle 
(his student) shaped the logical background of ontologies and 
introduced notions like category, subsumption, genus and 
subspecies. Aristotle’s ideas represent the conceptual 
foundations of object oriented systems of today. Moreover, he 
developed a number of inference rules, called syllogisms, 
which are being used by a number of logic-based reasoning 
systems. 
 
In today’s era of computer science, one does not talk about the 
ontology as the science of existence anymore. However we 
consider ontology as a formal specification of concepts and 
thus creating a collection of such concept hierarchies, as 
described by Gruber [2]. Thus, ontology is knowledge 
representation technique which represents conceptual 
information in a particular domain, describing it in a 
declarative manner and in turn clearly separating it from 
procedural aspects. By declarative we mean that an ontology 
should be formal (should be machine readable), explicit (all 
concepts and constraints are explicitly defines), shared (should 
capture consensual knowledge accepted by stakeholders 
involved) and conceptual (describe abstract model phenomena 
in a real world & identify relevant concept of those 
phenomena). Thus, Ontology defines the formal, explicit, and 
shared representations of concepts, objects, and property 
which define relations between them.  
 
According to Guranio [3], “An Ontology is generally regarded 
as a designed artifact consisting of a specific shared 
vocabulary used to describe entities in some domain of 
interest, as well as a set of assumptions about the intended 
meaning of the terms in the vocabulary.” 
 
II. ONTOLOGY DEVELOPEMNT TOOLS 
 
Some of the popular ontology development tools are Protégé, 
OntoEdit, WebODE, and Ontolingua. Ontologies can be 
created through any of these ontology editors. Using these 
tools we can create, browse, codify, and modify ontologies 
and in turn support ontology development and maintenance 
task. These editors vary in usability, modeling, scalability etc.  
 
Among these Protégé, is the most popular editor, as it is being 
used by majority of developers throughout the world. It is an 
open source, free tool developed by Stanford University. Java 
developers can use the ontologies developed through this tool. 
Moreover, they can directly use Protégé APIs can call the 
environment directly. 
 
III. LITRATURE REVIEW 
 
Development of computational ontologies was first introduced 
by Hearst who started working in the areas of concept 
annotation. Even today, his seminal work on lexico-syntactic 
patterns[4] is very relevant for annotation based knowledge 
applications. Hearst work has been refined and reused by 
several researchers who have applied different approaches. 
For example Poesio et al extended Hearst patterns for 
anaphora resolution [5] and using machine learning 
approaches in identifying patterns[6]. Eezioni [7] and Markert 
[8] have shown how these ontologies can be used on the 
Internet by using search engine APIs. Some researchers have 
also applied Lexico-syntactic patterns in the identification of 
other lexical relations like part-of relations [9] and causal 
relations [10][11] Cederberg and Widdows have shown that 
the precision of Hearst patterns can be improved by filtering 
the results of pattern matching using Latent Semantic Analysis 
[12]. Morin and Jacquemin[13] and Ravichandran and Hovy 
[14] has addressed the automatic generation of patterns via a 
similarity based approach where patterns are represented as 
vectors and if are found to be somewhat similar, are grouped 
together. This approach is more generalized then what was 
proposed by Hearst. 
 
In literature, we can find systems which have been developed 
using these techniques like OntoLT [15] which is an ontology 
learning plugin  for Protégé Ontology editor. This system 
annotates parts of speech chunks, and grammatical relations 
using a parser. OntoLearn [16] is another system where terms 
are extracted for a certain domain from a domain-specific 
textual corpus. This tool has become one of the most 
important tools in automatic creation of Ontologies through 
text.  
 
IV. PROPOSED WORK 
 
In order to develop ontology for human anatomy, we divided 
our work into four major sub area – namely cardiovascular 
system, digestive system, skeleton structure & nervous 
system. For each sub-system we defined classes, subclasses, 
disjoint classes, and equivalence classes.  
 
For skeleton structure sub-system, we defined subclasses as 
front view, back view, human skull(side view), skull(above 
view), skull(inside view), human joint & mechanical 
equivalent, knee(skeleton view), hip(skeleton view), 
elbow(skeleton view), elbow(ligament view), 
shoulder(skeleton view), hand(bones ligament muscles) & 
teeth etc. 
  
For nervous sub-system, we defined subclasses like human 
facial nervous, nerve cell, tongue(taste areas), ear(cut view), 
skin (cut view), eye’s rode & cones(cut view), brain(cut & 
surface view) etc. 
 
For digestive sub-system, we included subclasses like mouth, 
human spleen, human stomach and gallbladder, liver, human 
intestine & human throat etc. 
  
For cardiovascular sub-system we included subclasses like 
human heart picture (surface view), heart (cut view), kidney 
blood filtering, Lungs (cut view), coronary bypass (heart & leg 
view) & heat pacemaker etc. 
 
3.1 Methodology 
In this section we have shown a detailed description of phases 
for development of our ontology on human anatomy. 
 
3.1.1 Specification 
In this phase we collected information on our identified sub-
systems from books, journals and web search engines. We did 
a comprehensive study of all the finer points. When we had 
certain doubts, we contacted different experts in the respected 
domains. Once all the information was collected, we 
ascertained the domain and scope of our ontology. 
 
Fig.1 Structure of Human Anatomy Classes 
 
3.1.2 Conceptualization 
Once the specifications of domain and scope of our ontology 
was completed, we started the construction of concepts 
(classes) for our ontology sub-systems. In this we created the 
human anatomy as the super class and all the sub-systems as 
its sub classes. At times we came across classes with same 
properties and relations which were available in different sub-
systems. These classes were marked at equivalence classes. 
Moreover, we also came across some of the classes which had 
some of the same properties across sub-systems. These classes 
were marked as disjoint classes. Figure 1, illustrates these 
concepts. 
 
3.1.3 Relation 
Once we defined concepts, classes and object were defined; 
we started the construction of relations between them. Here 
we used term property for defining the relation. Here we 
defined the internal structures of the classes through various 
properties like term property which defined the relations, 
object property which defined the relation between two 
relations.  
 
The object property represented binary relations between 
individuals, for example, Hascardio_vascular_system was 
linked with cardio_vascular system in the body class. Figure 2, 
shows this property as defined in our ontology.  
 
Various values of the classes were also defined through data 
type property, where we defined the type of value a class can 
accept. We also used the annotation property for annotating or 
describing classes and sub-classes. This acted like a label 
which can help others understand the concept and utility of the 
classes and sub-classes. 
 
 
Fig. 2 Object property of system 
 
 
Fig.3 System structure 
 
 
3.1.4 Structural view of the system 
In this phase, we defined the graphical representation of the 
our ontology. As we have used Protégé 4.1 for the 
development of our ontology, we used Onto Graf, which is a 
built-in visualization tool for Protégé 4.1. Lower versions of 
Protégé do not have this feature, for this one needs to 
download GraphViz from sourceforge.net and link it with 
OntoViz visualization tool of Protégé. Figure 3 shows the 
structural view of our four major sub-systems. 
 
 
Fig. 4 Create Instances 
 
3.1.5 Creation of instances 
In this phase, we created the individual instances of the 
classes. In order to differentiate classes according to their 
components, we used individual properties and relations for 
each instance. Figure 4 shows the instance diagrams.  
 
While clicking on the active ontology tab, we can view the 
summary of our ontology which displayed the no. of super 
class, subclasses, disjoints classes of our ontology being used. 
Figure 5 shows the diagram of our active ontology. 
 
V. EVALUATION 
 
We needed to verify whether we had created the correct 
ontology or not. For this we used the DL Query feature of the 
of the Protégé, through which we checked each relation, 
property and in turn the classes. In this, we configure the 
reasoner for description logics, determining classes, instances, 
domain & range. Then, we used the query tab of the Protégé. 
Here we wrote the class name and when the query was 
executed, protégé retrieved the query in terms of classes, 
individuals, super class, domain and range. As all the desired 
relations and properties were retrieved, we were assured that 
our ontology has been created without any conflicts and 
errors. Figure 6 shows the diagram of query execution. 
 
 
Fig.5 Active Ontology 
 
 
Fig 6 Query Execution 
VI. CONCLUSION 
 
We have the shown the creation of an ontology for human 
anatomy. In this ontology we mainly used four sub-systems – 
cardiovascular system, digestive system, skeleton structure & 
nervous system. We have provided a detailed description of 
the creation of this ontology by explaining various phases of 
development. We have also verified the ontology by executing 
each class and their properties. In future wish to add more part 
of human body & reuse this ontology. 
 
REFERENCES 
 
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from Knowledge Base Systems,  In Proc. of International 
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[2]. T. Gruber, Towards principles for the design of 
ontologies used for knowledge sharing. In Formal 
Analysis in Conceptual Analysis and Knowledge 
Representation, Kluwer, 1993. 
[3]. N. Guarino, Concepts, attributes and arbitrary relations: 
some linguistic and Ontological criteria for structuring 
knowledge bases. Vol 8, Data and Knowledge, 1992. 
[4]. M. Hearst, Automatic acquisition of hyponyms from 
large text corpora. In Proceedings of the 14th 
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