a survey on designing metrics suite to asses the quality of ontology
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Abstract---With the persistent growth of the World Wide Web,
the difficulty is increased in the retrieval of relevant information for a
user’s query. Present search engines offer the user with several web
pages, but different levels of relevancy. To overcome this, the
Semantic Web has been proposed by various authors to retrieve and
utilize additional semantic information from the web. As the
Semantic Web adds importance for sharing knowledge on the internet
this has guide to the development and publishing of several
ontologies in different domains. Using the database terminology, it
can be said that the web-ontology of a semantic web system is
schema of that system. As web ontology is an integral aspect of
semantic web systems, hence, design quality of a semantic web
system can be deliberated by measuring the quality of its web-
ontology. This survey focuses on developing good ontologies. This
survey draws upon semiotic theory to develop a suite of metrics that
assess the syntactic, semantic, pragmatic, and social aspects of
ontology quality. This research deliberates about the metrics that may
contribute in developing a high quality semantic web system.
Keywords--- Quality Metrics, Web ontology, Semiotic Metrics,
Semantic Quality, Domain modularity.
I. INTRODUCTION
EMANTIC Web is nothing but the extension of the
present web in which the web resources are prepared with
formal semantics about their interpretation for the machines.
These web resources are combined in the form of web
information systems, and their formal semantics are usually
characterized in the form of web-ontologies. By means of the
database terminology, it can be said that the web-ontology of a
semantic web system is representation of that system [11].
Design quality of a semantic web system can be calculated by
computing the quality of its web-ontology because web
ontology is the integral element of semantic web systems [25].
The main concern is that when the design of a web-ontology is
completed, it is suitable time to assess its quality so that in
case, the design is of low quality, it can be enhanced before its
instantiation. This helps in saving of considerable amount of
cost and effort for developing high quality semantic web
systems. Metrics are considered as the appropriate tools for
estimating quality. This survey focuses on several metrics for
web ontology quality evaluation.
II. LITERATURE SURVEY
Ahluwalia et al., [1] presented a Semiotic Metrics Suite for
Assessing the Quality of Ontologies. Table 1 shows some of
the metrics for quality evaluation [1, 3].
As a decisive construct, overall quality (Q) is a subjective
function of its syntactic (S), semantic (E), pragmatic (P), and
social (O) qualities [1] (i.e., Q = b1×S + b2×E + b3×P +
b4×O). The addition of weight is equal to 1. In the absence of
pre-specified weights, the weights are assigned to be equal.
Syntactic Quality (S) evaluates the quality of the ontology
according to the way it is written. Lawfulness is the extent to
which an ontology language’s rules have been obeyed. Not
every ontology editors have error-checking capabilities;
however, without correct syntax, the ontology cannot be read
and used. Richness is nothing but the proportion of features in
the ontology language that have been used in ontology (e.g.,
whether it includes terms and axioms, or only terms). Richer
ontologies are more valuable to the user (e.g., agent).
Semantic Quality (E) estimates the meaning of terms in the
ontology library. Three attributes are used here are
interpretability, consistency, and clarity. Interpretability dealswith the meaning of terms (e.g., classes and properties) in the
ontology. In the real world, the knowledge provided by the
ontology can map into meaningful concepts. This is
accomplished by checking that the words used by the ontology
be present in another independent semantic source, such as a
domain-specific lexical database or a comprehensive, generic
lexical database such as WordNet. Consistency is nothing but
whether terms having a consistent meaning in the ontology.
For example, if an ontology claims that X is a subclass of Y,
and that Y is a property of X, then X and Y have incoherent
meanings and are of no semantic value. For example,
ontological terms such as IS-A is often used inconsistently.
Clarity is the term which determines whether the context of terms is clear. For example, if ontology claims that class
“Chair” has the property “Salary,” an agent must know that
this illustrate academics, not furniture.
Pragmatic Quality (P) deals with the ontology’s usefulness
for users or their agents, irrespective of syntax or semantics.
Three criteria are used for determining P. Accuracy is whether
the claims on ontology makes are ‘true.’ This is very tricky to
determine automatically without a learning mechanism or
truth maintenance system. Currently, a domain expert
evaluates accuracy. The measure of the size of the ontology is
A Survey on Designing Metrics suite to Asses the
Quality of OntologyK.R Uthayan G.S.Anandha Mala, Professor & Head,
Department of Information Technology, Department of Computer Science & Engineering
SSN College of Engineering St.Joseph’s College of Engineering,Chennai, India Chennai, India
[email protected] [email protected]
S
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called as Comprehensiveness. Larger ontologies are more
probable to be complete representations of their domains, and
provide more knowledge to the agent. Relevance indicates
whether the ontology satisfies the agent’s specific
requirements.
TABLE 1: DETERMINATION OF METRIC VALUES
Attributes Determination
Overall Quality (Q) Q = b1.S + b2.E + b3.P + b4.O
Syntactic Quality (S) S = bs1.SL + bs2.SR
Lawfulness (SL)Let X be total syntactical rules. Let Xb be total breached rules. Let NS
be the number of statements in the ontology. Then SL = Xb / NS.
Richness (SR)
Let Y be the total syntactical features available in ontology language.
Let Z be the total syntactical features used in this ontology.
Then SR = Z/Y.
Semantic Quality (E) E = be1.EI + be2.EC + be3.EA
Interpretability (EI)
Let C be the total number of terms used to define classes and properties
in ontology.Let W be the number of terms that have a sense listed in WordNet. Then
EI = W/C.
Consistency (EC)
Let I = 0. Let C be the number of classes and properties in ontology.
∀Ci, if meaning in ontology is inconsistent, I+1. Therefore, I = number
of terms with inconsistent meaning. Ec = I/C.
Clarity (EA)Let Ci = name of class or property in ontology. ∀ Ci, count Ai, (the
number of word senses for that term in WordNet). Then EA = A/C.
Pragmatic Quality (P) P = bp1.PO + bp2.PU + bp3.PR
Comprehensiveness (PO)Let C be the total number of classes and properties in ontology. Let V
be the average value for C across entire library. Then PO = C/V.
Accuracy (PU)
Let NS be the number of statements in ontology. Let F be the number of
false statements. PU = F/NS. Requires evaluation by domain expert
and/or truth maintenance system.
Relevance (PR)
Let NS be the number of statements in the ontology. Let S be the type of
syntax relevant to agent. Let R be the number of statements within NS
that use S. PR = R / NS.
Social Quality (O) O = bo1.OT + bo2.OH
Authority (OT)
Let an ontology in the library be OA. Let the set of other ontologies in
the library be L. Let the total number of links from ontologies in L to
OA be K. Let the average value for K across ontology library be V.
Then OT = K/V.
History (OH) Let the total number of accesses to an ontology be A. Let the averagevalue for A across ontology library be H. Then OH = A/H.
Cohesion (Coh)Coh=|SCC|
Where SCC is separate connected components
Fullness (F)
Readability (Rd)
For the purpose of evaluation, it needs some knowledge of
the agent’s requirements. This metric is coarse as it verifies for
the type of information the agent uses by ontology (e.g.,
property, subclass, etc), rather than the semantics needed for
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specific tasks (e.g., the particular subclasses needed to
interpret a user’s specific query).
Social quality (O) imitates the fact that agents and
ontologies exist in communities. The authority of an ontology
is nothing but the number of other ontologies that link to it
(define their terms using its definitions). More authoritative
ontologies indicate that the knowledge they provide is
accurate or useful. The history indicates the number of times
the ontology is accessed. Ontologies are more dependablewhen they are with longer histories.
The cohesion (Coh) of a KB is nothing but the number of
separate connected components (SCC) of the graph
representing the KB.
The fullness (F) of a class Ci is defined as the actual number
of instances that belong to the subtree rooted at Ci (Ci(I))
compared to the expected number of instances that belong to
the subtree rooted at Ci (Ci`(I)).
The readability (Rd) of a class C i is defined as the total of
the number attributes that are comments and the number of
attributes that are labels the class has.
Amjad et al., [2] provided the Web-Ontology Design
Quality Metrics. The author proposes design metrics for web-
ontology [21] by maintaining certain recommended principles
like a metric may reach its highest value for perfect quality for
excellent case and vice versa that is it may reach its lowest
level for worst case. It is supposed to be monotonic, clear, and
intuitive. It must correlate well with human decisions and it
should be automated if possible. The proposed metrics may
give notification about how much knowledge can be derived
from a given webontology; how much it is relevant to a user’s
specific necessities and how much it is effortless to reuse,
manage, trace and adapt. The metrics provided by the author
are Knowledge Enriched (KnE), Characteristics Relevancy
(ChR) and Domains modularity (DoM).
Knowledge Enriched metric
The reasoning capability of a web-ontology is determined
by Knowledge Enriched (KnE) metric, and it is based on two
sub-metrics so-called Isolated Axiom Enriched (IAE) metric
and Overlapped Axiom Enriched (OAE) metric. There are
three parts in this axiom namely, predicate, resource and
object. If none of these is similar with any other axiom of
identical domain then that axiom is termed as isolated axiom.
If the two axioms have some similar parts, it is said to be
overlapped. There may be more than a few transitively
overlapped axioms in any domain. This metric determines the
percentage of IAE and OAE, and if the former is greater thanthe later one, then the web-ontology can be regarded as less
knowledge enriched. IAE is officially defined as the ratio of
total number of isolated axioms (tIAs) to the total number of
domain axioms (tDAs).
(1)
In the above equation, n is total number of sub-domains of
web-ontology. Similarly, the OAE metric is officially defined
as ratio of total number of overlapped axioms (tOAs) to the
total number of domain axioms. It can be written as follows:
(2)
In the equation given above, n is total number of sub-
domains of web-ontology. Lastly, the KnE metric is the
difference of total number of overlapped axioms and the total
number of isolated axioms. It may be written as follows:
(3)
If the resultant KnE value is positive, then the web-ontology
is more knowledge enriched, if it is zero, then the web-
ontology is average knowledge enriched, and if it is negative,then the web-ontology is less knowledge enriched.
Characteristics Relevancy metric
Characteristics Relevancy (ChR) metric gives us the
suggestion about how much a given web-ontology is close to a
user’s specific necessities and the degree of reusability of the
web-ontology. Formally, it is termed as the ratio of the
number of relevant attributes (nRAs) in a class to the total
number of attributes (TnAs) of that class. It can be written as
follows:
(4)
where n in above equation represents the total number of
classes in the provided web-ontology. ChR metric reveals the
proportion of relevant attributes in the web-ontology, and this
number gives insights how much a web-ontology is relevant.
Domain Modularity metric
Domain modularity (DoM) metric denotes the component-
orientation feature of a web-ontology. This metric specifies
the grouping of knowledge in different components of web-ontology. The webontology is best manageable, traceable,
reusable and adaptable, if it is designed in components
(subdomains). Formally, the DoM metric is given as the
number of sub-domains (NSD) contained in a webontology.
This metric also depends on the coupling and cohesion [25]
levels of sub-domains, and it is directly proportional to its
cohesion level and inversely proportional to its coupling level.
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(5)
In the above equation, DCoh indicates the level of domain
cohesion and DCoup represents the level of coupling among
sub-domains of web-ontology domain. DoM metric is a realnumber indicating the degree of partial reusability of a given
web-ontology.
Samir et al., [3] given the OntoQA: Metric-Based Ontology
Quality Analysis. The metrics presented can highlight key
characteristics of an ontology schema and also its population
and facilitate users to make an informed judgment easily. The
metrics used by the author here are not 'gold standard'
measures of ontologies. Instead, the metrics are projected to
estimate several aspects of ontologies and their potential for
knowledge representation. Rather than describing ontology as
merely effective or ineffective, metrics describe a certain
aspect of the ontology because, in most cases, the way the
ontology is built is largely dependent on the domain in whichit is designed. The metrics defined here are Schema Metrics
and Instance Metrics. The following are metrics considered by
the author:
The following are some of Schema Metrics:
Relationship Richness: The diversity of relations and
placement of relations in the ontology is defined by this
metrics. An ontology that has many relations further than
class-subclass relations is better than taxonomy with no more
than class-subclass relationships. The relationship richness
(RR) is defined as the ratio of the number of relationships (P)
defined in the schema to the sum of the number of subclasses
(SC) plus the number of relationships.
Attribute Richness: The attribute richness (AR) is defined as
the average number of attributes (slots) per class. It is given as
the ratio of number attributes for all classes (att) to the number
of classes (C).
Inheritance Richness: The inheritance richness of the
schema (IRs) is defined as the average number of subclasses
per class. The number of subclasses (C1) for a class Ci is
defined as |HC (C1, Ci)|.
The following are some of Instance Metrics:
Class Richness: The class richness (CR) of a knowledge
base is defined as the ratio of the number of classes used in the
base (C`) to the number of classes defined in the ontology
schema (C).
Average Population: Formally, the average population (P)of classes in a knowledge base is defined as the number of
instances of the knowledge base (I) to the number of classes
defined in the ontology schema (C).
Importance: The importance (Imp) of a class Ci is defined
as the number of instances that belong to the subtree rooted at
Ci in the knowledge base (Ci(I)) compared to the total number
of instances in the knowledge base (I).
Werner [4] provided a Realism-Based Metric for Quality
Assurance in Ontology Matching. There are three levels
introduced to the methodology for the measurement of quality
improvements in single ontologies. These levels are:
• Level 1: reality, consisting of both instances and
universals and also the various relations that acquire
between them;
• Level 2: the cognitive representations of this reality
personified in observations and interpretations;
• Level 3: the publicly accessible concretizations of thecognitive representations in representational artifacts of a
range of sorts, of which ontologies are examples.
Harith et al., [5] defined the metrics for Ranking
Ontologies. In this paper AKTiveRank, a prototype system for
ranking ontologies is proposed based on the analysis of their
structures. This paper describes the metrics used in the ranking
system. The ranking measures used are described below:
Class Match Measure
The Class Match Measure (CMM) is intended to estimate
the coverage of ontology for the provided search terms.
AKTiveRank looks for classes in every ontology that havelabels matching a search term either exactly (class label
identical to search term) or partially (class label “contains” the
search term).
Density Measure
Density Measure (DEM) is deliberated to approximate the
representational-density or information-content of classes and
accordingly the level of knowledge detail. DEM considers
how well the concept is additionally specified (the number of
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subclasses), the number of attributes related with that concept,
number of siblings, etc.
Semantic Similarity Measure
Similarity measures have often been used in information
retrieval systems to afford enhanced ranking for query results.
Ontologies can be analyzed as semantic graphs of concepts
and relations, and hence similarity measures can be applied to
explore these conceptual graphs. This helps in resolvingambiguities.
Henry [7, 23] described a Measurement Ontology
Generalizable for Emerging Domain Applications on the
Semantic Web. The semantic Web is considered as the next
generation Web of structured data that are automatically
shared by software agents, which apply definitions and
constraints structured in ontologies to correctly process data
from contrasting sources. One aspect needed to develop
semantic Web ontologies of emerging domains is creating
ontologies of concepts that are common to those domains.
These general ontologies can be used as building blocks to
develop more domain-specific ontologies. However most
measurement ontologies focus on representing units of
measurement and quantities, and not on other measurement
concepts such as sampling, mean values, and evaluations of
quality based on measurements. In this paper, the author
elaborates on a measurement ontology that represents all these
concepts. This paper presents the generality of the ontology,
and describes how it is developed, used for analysis and
validated.
Fensel et al., [8] provided OIL (Ontology Interchange
Language): an ontology infrastructure for the Semantic Web.
Initially, Researchers in artificial intelligence motivate the
development of ontologies [14] to facilitate knowledge sharingand reuse. Ontologies [15] play a key role in supporting
information exchange across different networks. A
prerequisite for such a role lead to the development of a joint
standard for specifying and exchanging ontologies. The
authors present OIL which satisfies such standards.
Carlos et al., [9] presented an Ontology-based Metrics
Computation for Business Process Analysis. Business Process
Management (BPM) aims to support the whole life-cycle
required to deploy and maintain business processes in
organizations. Analyzing business processes have a need of
computing metrics that can facilitate determining the health of
business activities and thus the whole enterprise. However, the
degree of automation currently achieved cannot maintain thelevel of reactivity and adaptation demanded by businesses. In
this paper the author argue and show how the use of Semantic
Web technologies can enhance to an important extent the level
of automation for analyzing business processes. The author
presents a domain-independent ontological framework for
Business Process Analysis (BPA) with support for
automatically computing metrics. In particular, a set of
ontologies for specifying metrics are defined in this paper. The
domain-independent metrics computation engine is defined
that can interpret and compute them.
Orme et al., [10] described Coupling Metrics for Ontology-
Based Systems. XML has grown to be frequent in Internet-
based application domains such as business-to-business and
business-to-consumer applications. It has moreover produced
a basis for service-oriented architectures such as Web services
and the Semantic Web, mainly because ontology data
employed in the Semantic Web [16, 17] are stored in XML.
Measuring system coupling is a generally accepted software
engineering practice connected with producing high-qualitysoftware products. In many application domains, coupling can
be assessed in ontology-based systems before system
development by measuring coupling in ontology data. A
proposed set of metrics determines coupling of ontology data
in ontology-based systems [22] represented in the Web
Ontology Language (OWL), a derivative of XML.
Andrew et al., [1] define a semiotic metrics suite for
assessing the quality of ontologies. A suite of metrics
proposed here is to assess the quality of the ontology. The
metrics evaluate the syntactic, semantic, pragmatic, and social
aspects of ontology quality according to the semiotic theory.
The author operationalizes the metrics and employs them in a
prototype tool called the Ontology Auditor. A primary
validation of the Ontology Auditor on the DARPA Agent
Markup Language (DAML) library of domain ontologies
represents that the metrics are feasible and highlights the wide
variation in quality between ontologies in the library. The
contribution of the research is to afford a theory-based
framework that developers can utilize to develop high quality
ontologies and that applications can exploit to choose
appropriate ontologies for a given task. Zhe et al., [24]
provides some Evaluation Metrics for Ontology Complexity
and Evolution Analysis.
Ying et al., [12] discusses about semantic web. Presently,
computers are shifting from single, isolated devices into doorpoints to a worldwide network of information exchange and
business transactions called the World Wide Web (WWW).
For this cause, support in data, information, and knowledge
exchange has become a key issue in current computer
technology. The achievement of the WWW has made it
increasingly hard to find, access, present, and maintain the
information required by a wide variety of users. In answer to
this problem, many new research initiatives and commercial
enterprises have been provided to enhance available
information with machine processable semantics. This
semantic web will offer intelligent access to heterogeneous,
distributed information, enabling software products (agents)
[20] to intervene between user needs and the informationsources available. This paper reviews ongoing research in the
area of the semantic web [19], focusing especially on ontology
technology.
Anthony et al., [13, 18] put forward the Complexity and
coupling metrics for ontology based information. Ontologies
are greatly used in bioinformatics and genomics to
characterize the structure of living things. This research
focuses on complexity metrics for ontologies. These
complexity metrics are obtained from semantic relationships
in an ontology. These metrics will assist for selecting the best
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ontologies in several application areas, including
bioinformatics and genomics.
Dimitris N. Kanellopoulos [22] described ODELO: an
ontology-driven model for the evaluation of learning
ontologies. Trying out or renewing an existing learning
ontology [6] and providing evaluation tools to assess its
quality are fundamental steps before putting an e-learning
system online. Ontology [21, 23] evaluation is a central task
and it is typically the output of an automatic process. Thispaper put forwards an ontology-driven model, called
Ontology-Driven model for the Evaluation of Learning
Ontologies (ODELO), for the estimation of ontologies
representing learning resources with respect to several metrics.
Syntax contracts with the proper relations between signs (e.g.,
words, phrases, sentences) and the construction of new ones.
Social metrics imitate the fact that software agents and
ontologies coexist and communicate in communities.
Ontology cohesion metrics indicates the degree of relatedness
of ontology classes. ODELO is a deductive valuation model
that identifies the elements of ontological quality for learning
ontologies. In this paper the author propose a framework for
assessing the quality of learning ontologies that constitute the
basis for intelligent educational Adaptive Hypermedia (AH)
systems.
III. CONCLUSION
In this survey, the different suite of metrics for evaluating
ontologies based upon the semiotic-web is analyzed. It is
better to assess the quality of web-ontology after the design is
completed. This helps in neglecting the low quality ontology
before its development by enhancing those defects in
ontology. This helps in saving of considerable amount of cost
and effort [2] for developing high quality semantic websystems. Several metrics such as Knowledge Enriched metric,
Characteristics Relevancy metric, Domain Modularity metric,
Richness, Instance Metrics, Semantic Similarity Measure,
Density Measure, etc., are analyzed for assessing the quality
of ontology. This survey helps in choosing the best suited
metrics for assessing the quality of ontology. The different
metrics which involve different criteria for ontology are
analyzed in this survey.
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Vol. 8, No. 8, November 2010
184 http://sites.google.com/site/ijcsis/
ISSN 1947-5500