specification of a shared conceptualization”
classical dictionary definition of Ontology may be: "The
branch of metaphysics that deals with the nature of being".
It begins with Aristotle attempt to classify things in the world.
term “Ontology” has been introduced to the information
sciences and research fields during the 1990’s by several
Artificial Intelligence (AI) research communities. AI
researchers adopted the term “Ontology” mainly to
describe what they though would be (from the stand point of
computational aspects) a proper representation of the world in a
program code. It has recently been used in several other information
technology fields such as intelligent information integration,
information retrieval on the Internet, and knowledge management.
are of basic interest in many different fields, largely due to what
they promise: a shared and common understanding of some domain that
can be the basis for communication ground across the gaps between
people and computers. They (Ontology approaches) allow for sharing
and reuse of knowledge bodies in computational form. As many
traditional activities are changing their manner in the world of
today due to the availability of information brought by the
World-Wide-Web (WWW), Ontologies are likely to change more when the
knowledge is structured in machine readable way, and the abstracts
concepts it contains are shared (See Semantic
Web for more
information in this direction) .
document attepts to brings a short and only a brief survey of the way
experts define Ontology, the different types of Ontologies in use for
various areas and how they may be applied to different fields. A
special focus is given in the following on the issue of Information
Retrieval (IR), and how presently used IR methods may interact
with Ontology based concepts. Finally, a short list of computer
languages, recently aknowladged in the context of creating an
Ontology structure, or performing semantic query based on Ontologies,
document is arranged with its main body offering a general
description of each of the issues discussed, along with hyper-links
to some specific examples found either on internal documents or the
WWW. Some other documents are in their original form and some have
undergone some processing in order to focus on the relevant issues.
the many definitions have aroused for Ontology the following is
recommended by [(Gruber, 1993; Borst, 1997) and
Ontology is a formal, explicit specification of a shared
"conceptualization" is an abstract model of a phenomenon,
created by identification of the relevant concepts of the phenomenon.
The concepts, the relations between them and the constraints on their
use are explicitly defined. "Formal" means that Ontology is
machine-readable and excludes the use of natural languages. For
example, in medical domains, the concepts are diseases and symptoms,
the relations between them are causal and a constraint is that a
disease cannot cause itself.
an Ontology is a "shared conceptualization" states that
Ontologies aim to represent consensual knowledge intended for the use
of a group. Ideally the Ontology captures knowledge independently of
its use and in a way that can be shared universally, but practically
different tasks and uses call for different representations of the
knowledge in an Ontology.
is sometimes confused with taxonomy, which is a classification
of the data in a domain. The difference between them is in two
Ontology has a richer internal structure as it includes relations
and constraints between the concepts.
Ontology claims to represent a certain consensus about the knowledge
in the domain. This consensus is among the intended users of the
knowledge, e.g. doctors using a hospital Ontology regarding a
certain disease, artists relating to historical art and so on.
Ontologies aim to represent a form of common agreement regarding the
knowledge they represent, they are often created in a cooperative
process involving different people, sometimes at different places.
Ontologies are divided to types in accord with the degree of
generality of the principles they contain.
Roles of Ontologies
stated above, Ontologies play a dominant roles in a growing number of
different fields. A few general examples may be reviewed here:
natural-language applications, Ontologies are used for:
the database and information retrieval areas, Ontologies are
the process of retrieval. More on this issue is found in Ontologies
and semantic information retrieval,
and also below.
the problem of heterogeneous information sources that utilize
different representations. Mapping each source's data scheme to the
Ontology allows the user a unified view of the information
regardless of its actual source. The HIDE
is an example for such a use, and the InfoMaster
system may be viewed as a restricted form of Ontology. SEMEDA
is an application to molecular biology data and a good example of
the considerations behind choosing an Ontology which best represents
the knowledge at hand.
possible application of Ontologies is intra-organization
communication and knowledge management. Providing terms,
relations and constraints, an Ontology is equipped to allow an
accurate and common means of communications between organization
a similar setting, management of "corporate memory" –
the essentially important knowledge body that companies and
organizations possess and rely on for reaching correct decisions –
is a rapidly developing field. These Memories are likely to evolve to
large collections of diverse knowledge
patterns, making access and distribution of the knowledge
difficult. Ontologies may be of great use in structuring and defining
the knowledge, and in supporting extraction of relevant elements.
good example is the Tool
for Clinical Data
which presents a system for clinical-data management designed for
hospitals. Other commercially successful implementations are found in
different corners of the Knowledge Management field.
are of knowledge engineering is concerned with methodical
building of large scale Knowledge Based Systems which have at least
two basic components: domain knowledge and problem-solving knowledge.
Ontologies are mainly used to analyze, model and implement the domain
knowledge, but also affect problem-solving knowledge. How different
types of Ontologies take part in the process of domain model
construction is addressed in Types
are two main classes of Ontologies: the first would be the one that
is employed to explicitly capture "static knowledge" about
a domain, in contrast to Ontologies (the second) that provide a
reasoning point of view about the domain knowledge (problem solving
the first class a distinction between types is made on the basis of
the level of generality, as summarized in the table below:
to Represent knowledge relevant to a certain domain type, e.g.
medical, mechanical etc.
be applied to a variety of domain types. Mereology (Part-Whole
theory) Ontologies are applicable to many technical domains. Also
called "super theory" and "core technology".
formulate general representation entities without defining what
should be represented. The Frame Ontology is a well known
the problem solving knowledge class, two types may be found:
terms specific for particular Tasks.
terms specific to particular Problem Solving Methods.
and tasks are two distinct terms in knowledge engineering. A task
refers to a type of problem while a method is a means of solving the
problem. Thus, a task may be associated with several different
methods which are in turn composed of subtasks. For more information
in this regard the reader is referred to Knowledge
Engineering: Principles and methods.
type of Ontology is the Application Ontology. The Application
Ontology is a combination of the Domain and Method Ontologies that
includes all the knowledge – static and problem solving –
needed for the modeling of a particular domain.
Creation and Design of Ontologies
are basically two ways to create an Ontology. The first and the most
obvious one is to build an Ontology from "scratch", i.e. to
define classes, relations instants and so on. Examples may be found
for Clinical Data,
a Protein Ontology.
interesting attempt at semi-automatic Ontology creation is described
The approach presented is based on the fact that the web site
services share similar structures and functionality as the underlying
software. They apply a grammar-sensitive tool to recognize verb-noun
pairs in the software documentation that are suspected to have a
similar expression as the related action (e.g. "Get-Username"
or "Delete-Entry"), and after applying certain filters,
Ontology concepts are attributed to the most significant pairs. Thus,
the Ontology engineer has a "head start" in creating the
Ontology. Some general terms defined in that paper which relate to
retrieval success and Ontology overlap are summarized in Appendix A.
second way is to combine available Ontologies in several forms. The
most frequently used forms are:
of one Ontology into another. The result is that the classes,
relations and axioms of both Ontologies are found in the unified
Ontology. Name conflicts are likely to rise and must be resolved,
either manually or using an Ontology engineering tool.
An Ontology is applied to a restricted subset of what it was
originally intended for. A simple example is the combination of an
Ontology of rules for dealing with real numbers with an Ontology for
integer number arithmetic. The rule '+' as may be defined in the
first Ontology is only applied on the subset of integers.
General Ontologies sometimes require refinement in order to be
applicable to specific needs. The KACTUS project
was concerned with constructing large Ontologies for technical
devices through incremental refinement of general Ontologies into
way an Ontology is created, some design principles are mentioned in
related texts in order to optimize its use:
– Small units make understanding the structure and reuse
coherence – self consistency of the structural
– Ontologies are often enhanced by adding single concepts or
classes, as a necessary evolution. Easing this process should be a
definitions on natural categories – Makes it simple and
principle of minimal commitment brings again the generality issue.
The less there is Ontological commitment, the more general the
Ontology it is, and therefore easier to reuse. However, the usability
– reusability trade-off exists, and generalizing the Ontology
beyond a certain point may reduce its effectiveness in representing
the knowledge it was intended to represent. Thus, perhaps a better
choice of words is "Optimal Ontological Commitment", when
all parties understand the merits of generalization.
detailed guide to the basics of Ontology designing may be found found
Ontologies and semantic information retrieval
accelerated processes of digitalization and globally connected
databases sprouting that are occurring in recent years have changed
the focus of the information problems. It is no longer difficult to
find information and gain knowledge about a certain topic, but
rather to select from the huge heap of information the most relevant
elements only. Search engine traditionally utilize a syntactic
approach, searching for keywords, and performing operations on
their abundance in order to rank the information elements. These
methods suffer from problems such as vocabulary inconsistency –
a situation in which a certain information object contains relevant
information but is not retrieved because it uses different words to
describe it – and its "opposite" in which irrelevant
information is retrieved due to similarity of words.
however, a new approach is emerging – the semantic
approach. This approach aims to use meta-data – data about data
– in order to answer the users' requirement in a more
satisfactory way for Data Retrieval and navigation
can be very useful in improving the process in two ways:
allows to abstract the information and represent it explicitly-
highlighting the concepts and relations and not the words used to
can possess inference functions, allowing more intelligent
retrieval. For example a "basketball player" is also a
"professional athlete", and an Ontology that defines the
relations between these concepts can retrieve one when the other is
Ontologies are to satisfy the demands of information retrieval (IR)
needs, then, a large number of detailed Ontologies must be created,
and methods for semi-automatic and automatic Ontology creation
are heavily researched.
& AI (see
hyperlink) gives a summary on how Ontologies and Ontology-based
methods may interact with traditional IR methods. Specifically,
Co-Occurrence Theory utilized in keyword-based searches may be
applied to the semi-automatic creation of Ontologies. The basic idea
behind the theory is that words that co-occur often, have a strong
relation between them and the relation between the two concepts
embodied in these words may be weighted by their co-occurrence
statistics. In the specific example the Salton
important measure of co-occurrence which is not biased by naturally
high occurrence of certain keywords, was used as the weights for
mentioned before, describes an attempt to use the already available
text documentation in software programming interface (API) codes in
order to automatically create domain Ontologies. Here, the authors
utilize a grammatically sensitive system which can locate verb-noun
pairs in the documentation that hypothetically resemble an action
performed by the software. For example "Add-Data" and
"Get-Username" are two such verb-noun pairs. After
filtration of significant pairs, a domain Ontology may be built based
on these significant pairs as concepts, manually at first but
semi-automatically ideally. Some basic indices for assessing
the success of such a retrieval process, and very generally
any retrieval process, are brought in the article and summarized in
other side of the coin is, of course, actually retrieving data,
information and knowledge using Ontologies. Two examples of such
projects are the SHOE (click here
for website and
for descriptive PDF article)
and the Ontobroker
projects. The SHOE method adds Ontological annotation to existent web
pages and thus when a user fills a query he/she is presented with
available contexts making the search much more specific. Ontobroker
also introduced an inference engine that is able to perform useful
functions on the formal semantics, after several translation steps.
Such inference functions allow retrieving subclasses of a queried
concept and making implicit information more explicit and accesable.
precise information is naturally requires a certain degree or means
for ranking the relevance of information available at each source.
Information Retrieval Model
deals with this issue together with the issue of incomplete knowledge
bases (KBs). The approach adopted there assumes that any semantic
retrieval algorithm must be robust enough to deal with the
incompleteness of KBs and Ontologies as they are not yet sufficiently
developed to rely upon absolutely, and so they suggest incorporating
a keyword based "Plan B" retrieval method in the overall
system. Their semantic ranking method, brought here as an example for
such methods is usually based on two steps:
documents with a weighted annotation, descriptive of the
importance of the annotated instance is to the text. So, each
instance is thus weighted:
The weight of instance i in document d.
number of occurrences of i in d.
Maximum number of occurrences of any instance in d.
- Overall number of documents in the search space.
number of documents annotated with i.
idea is that some annotations may bring us closer to the goal of
evaluating the concepts in the document.
evaluating the relevance of documents a similarity index is
calculated based on the annotations:
- The vector of annotation weights related to the document.
- A vector resembling the query.
is the number of variables in
query which are related to instance i.
is stated in the article (see reference), this method of ranking is
an adaptation of the classic vector-space model used for similarity
evaluation. The final similarity result for the document is:
is the Ontology based similarity index and
is a keyword based similarity index.
the KB is incomplete then Ontology indices will suffer greater
errors, but the keyword based indices will be available to soften the
that Ontologies were first introduced by AI researchers, it is not
surprising that the languages used to define Ontologies are mostly
derived from the knowledge representation (KR) subfield of AI. The KR
languages were part of earlier efforts to represent the
aforementioned taxonomies, and inherently support definitions of
classes, relations such as inheritance ("is-a" e.g. enzyme
is-a protein…), properties and instances. The group of KR
languages which are relevant to Ontology representation is called
description logics. One of the first Ontology-dedicated
languages was Stanford's Ontolingua.
the area of Ontology dedicated languages is very active. Prominent
examples are OIL
– Ontology Interface Layer – which was created by
enhancing the capabilities of the pre-existent WWW frame language RDF
with formal semantics and reasoning services, and XOL
which similarly enhanced pre-existent XML. Another new development is
(a resource in the format of a PowerPoint presentation about its
the same time that efforts are concentrated towards standardization
and optimization of Ontology creation languages, some other
researchers believe that the presently used query languages are
inadequate for the task of meta-data querying and have designed
specific tools for that purose. An example is the Xcerpt
for PDF) and its
close relative Xchange.
It must be noted
that a very large information body can be found regarding these
languages or others, and evidently interest and development is
rapidly growing. Quite possibly new solutions will soon emerge, using
the existent platforms or perhaps creating different ones, and will
change the picture described above.
shown above, Ontologies – first introduced more or less two
decades ago – are now a focal point for interest and research.
Perhaps the greatest expectations from Ontology arise from the role
they are to play in the Semantic Web, the next generation of
the well known WWW. As much as the current web has changed the lives
of billions, so is the Semantic Web likely to do it again and
Ontologies are to play a key role in that change. The ability to deal
with abstract concepts through the Ontologies, rather than the "flat"
texts and keywords associated, allow capabilities for inference, for
context-related search, and ultimately, for the reuse and sharing of
knowledge that is machine processable. The possibilities of semantic
information retrieval were generally described above, with its tight
relation to Ontologies and Ontology creation (semi-automatic and
Ontologies are not restricted to global network knowledge
representation, and are applicable for any knowledge base that is
intended for shared use. An example brought here is an application of
Ontology for properly representing the critical clinical database of
a hospital and the growing understanding of the importance of
"corporate memory" assures many organizations will be
interested in similar systems for knowledge management.
conclude, it is expected that a large variety of tools and methods
that either utilize Ontologies for improved knowledge management or
retrieval, or assist in creating Ontologies more easily and
generically, will be emerging in the near future. These will take the
world toward another step in the direction of digitization of
knowledge, as machines will be able to perform basic reasoning with
it. The advances in this area will probably have a large impact on
S. Sidhu, Tharam S. Dillon,Fellow IEEE, Elizabeth Chang,Member IEEE,
a Protein Ontology Resource
Vallet, Miriam Fernández, and Pablo Castells,
An Ontology-Based Information Retrieval Model
Bry, Tim Furche, Paula-Lavinia Patranjan, and Sebastian Schaffert,
Retrieval and Evolution on the (Semantic) Web: A Deductive Approach
Jiang, Katsuhiko Ogasawara, Naoki Nishimoto, Akira Endoh, Tsunetaro
Tab: A Concept-oriented View Generation Tool for Clinical Data Using
Formal Concept Analysis
G. Marcos, H. Eskudero, C. Lamsfus , M.T. Linaza, Data
Retrieval From a Cultural Knowledge Database
Jacob Köhler and Steffen Schulze-Kremer, The
Semantic Metadatabase (SEMEDA): Ontology based integration of
federated molecular biological data sources
Heflin and James Hendler, Searching
the Web with SHOE
Ontologies from Software Documentation: a Semi-Automatic Method and
Studer, V. Richard Benjamins, Dieter Fensel , Knowledge
Engineering: Principles and methods,
Data & Knowledge Engineering 25 (1998) 161-197
Decker, Micheal Erdmann, Dieter Fensel and Rudi Studer,
Ontobroker: Ontology based Access to distributed and Semi-Structured
and AI: The role of Ontology
See Appendix A
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