Building Customer Complaint Ontology: Using OWL to Express Semantic Relations ，Zhang Jinlong

Building Customer Complaint Ontology:
Using OWL to Express Semantic Relations
，
Yan Yalan Zhang Jinlong
School of Management, Huazhong University of Science and Technology, Wuhan, P.R.China, 430074
Abstract: Customer complaint is an important kind of information coming from customers and it is a
primary measure of customer dissatisfaction. In order that complaint information could be understood
and processed by machine automatically, more semantic descriptions are needed. Ontology has become
a promising technology to express semantics and OWL (Ontology Web Language) has the ability to
express semantic relations in building complaint ontology. In this paper, we put forward a framework of
customer complaint ontology after considering capabilities to express semantic relations provided by
OWL. Then we use Protégé-2000 to build OWL representations of the customer complaint ontology and
make some analyses. Our exploration might not standardize customer complaint ontology. We explore
with the intention to help people better understand the rationality in expressing concepts and relations
between the concepts in customer complaint management in order that OWL customer complaint
ontology and other domain ontology are improved and standardized as soon as possible.
Keywords: Customer complaint ontology, Framework, OWL, Semantic relations
1 Introduction
Customer complaint is an important kind of information coming from customers and it is a primary
measure of customer dissatisfaction.
Customer complaint management plays an important role in retaining old customers and even
obtaining new customers. It is also an important content of Customer Relation Management (CRM). For
a company, there exist many kinds of customer complaints. For example, some complaints are about
logistic service, some about post sale service, some about quality, some about attitude. The resolution of
the complaint might be economic compensation, improving service and so on. If a customer complaint
is about quality and not resolved quickly, the company would not only lose this customer but also suffer
from losing other customers. Companies should distinguish light complaint and strong complaint and
take respective measures. Moreover, complaints own many properties such as hasNumber, hasProblem,
hasResolution, madeBy. Customers are the origin of the complaint and own many properties such as sex,
age, marriage status, number of children, income, affiliation, telephone, email, and so on. How to
manage all the information of properties; how to define relations of complaints and properties and
relations of different kinds of complaints is the problem that should be solved in implementing CRM
strategy.
Customer complaint information should be shared among departments within a company and even
among companies within a supply chain. Best-practice companies have realized the importance of
sharing information across the company so that other employees and/or business units can benefit from
the same information. In establishing an information-sharing platform, many technologies have been
used. For example, customer complaint information could be stored in database or data warehouse. In
database, each property of complaint could be defined as a field. According to each field, given
complaint could be retrieved very quickly. Generally speaking, database could manage large number of
complaints with high efficiency. In data warehouse, many kinds of data mining technologies such as
time series, regression, principal components, canonical correlation, K-Means cluster, hierarchical
cluster could be used to analyze and discover some rules and relations about complaints. Moreover,
technologies such as COM Component Object Model
CORBA Common Object Request Broker
Architecture and Agent, have also been developed to solve the technical problem of information sharing
[1]
.
）
（
）、
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Though these technologies behaved very well in information sharing by the mode of message
exchanging, objective or component inter-referred and inter-operation among different system they
remain syntax sharing basically and cannot describe customer complaint information semantically as
well as express implied axiom, facts, judgments and rules relevant to customer complaint knowledge
already existed in the information system [2]. As a result the structured information inferred from
knowledge base or other information sources is unavailable as yet. Of course some new and useful
knowledge about complaint can hardly produced or induced. More important, information sharing not
based on semantic relations often leads to people confused by same conception with different name or
title in same or different context when discussing something about business [3]. In order that complaint
information could be understood and processed by machine automatically, more semantic descriptions
are needed. Ontology has become a promising technology to express semantics [4].
As a substitute for the mode of information sharing, ontology based on semantic relation has gained
more attention from researchers [5]. Ontology is a concept in philosophy. In the fields of artificial
intelligence and Web, ontology is the description about domain concepts and their relations. Ontology is
the theory about objects and their ties. It provides standards for differentiating kinds of objects (concrete
and abstract, existent and non-existent, realistic and ideal, independent and dependent) and their ties
(relations and dependency). Ontology is formal structure to support knowledge sharing and reusing. It
could be used to express explicitly the semantics of structured and semi-structured information in order
to support information acquiring, maintaining and accessing automatically. Ontology provides methods
to solve the heterogeneous expression of Web resource. The domain model hidden in ontology could be
regarded as providing a general semantic structure for information. Since this mode of information
sharing can provide a uniform communication platform as well as avoid the mistake to the same
concepts with different name or title in different context much efforts have been made to design this
effective tool to mediate information sharing in terms of this mode within or beyond one company.
Applying ontology on the Web facilitates the development of Semantic Web. Building complaint
ontology on the Semantic Web would facilitate machine to process and share complaint information
automatically without human interference. OWL (Ontology Web Language) has the ability to express
semantic relations in building complaint ontology.
2 Web Ontology Languages
XML (eXtensible Markup Language) has brought hope to Semantic Web. XML plus XML Schema
specifies the syntax, structure and data type, but lacks semantic constraints. Tim Berners-Lee, creator of
Semantic Web, considers the objective of Semantic Web as creating representative languages and
describing information in the machine understandable form. He summarizes the functional framework
of Semantic Web as metadata layer, schema layer and logical layer [6].
In metadata layer, data model only includes resources and properties, and RDF (Resource
Description Framework) is regarded as the popular data model in this layer. RDF is a language for
representing information about resources in the World Wide Web. It represents metadata of Web
resource, such as title, author, modifying date of Web content, copyright and register information of Web
documents, language, format, content items, and etc [7]. RDF is intended for situations in which this
information needs to be processed by applications, rather than being only displayed to people. RDF
provides a common framework for expressing this information so it can be exchanged between
applications without loss of meaning. Since it is a common framework, application designers can
leverage the availability of common RDF parsers and processing tools. The ability to exchange
information between different applications means that the information may be made available to
applications other than those for which it was originally created [8].
In schema layer, Web ontology language is introduced, used to describe concepts and properties.
RDF Schema is regarded as the best candidate. RDF Schema does not provide a vocabulary of
application-specific classes and properties. Instead, it provides the facilities needed to describe such
classes and properties, and indicates which classes and properties are expected to use together. RDF
Schema could be regarded as a light weighted Web ontology language. The RDF Schema facilities are
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themselves provided in the form of an RDF vocabulary; that is, as a specialized set of predefined RDF
resources with their own special meanings [9].
In logical layer, more powerful Web ontology languages are introduced. These languages provide
richer modeling sets mapping to the influential expressive logics. OIL Ontology Inference Layer 2000
and DAML-OIL Darpa Agent Markup Language-Ontology Inference Layer 2001 were once popular
languages in logical layer. At present, OWL is widely accepted. OWL is designed to apply in not only
presenting information but also processing the content of information. OWL facilitates machine
interpretability of Web content more greatly than that supported by XML Schema and RDF Schema by
providing additional vocabulary along with a formal semantics such as classes stated to be disjoint from
each other, cardinality, properties stated to be symmetric, two properties stated to be equivalent,
enumerated classes [10][11].
Building ontology on the Web is the core drive to push the development of Semantic Web. XML
Schema, RDF Schema and OWL could be regarded as Web ontology languages with increasing
capabilities to express semantics, meeting the needs of knowledge processing and knowledge
management in different periods.
（
（
， ）
， ）
3 A Framework of Customer Complaint Ontology
hasCity
hasZipcode
hasEmail
domain
domain
domain
domain
Contact
range
range
domain
hasName
hasCountry
Country
hasContact
Customer
subClassOf
Economic_Compensation
hasNumber
domain
Logistic_Service
{China, America,
France, Germany,
England, Australia,
Japan, Korea}
owl:oneOf
owl:equivalentClass
range
madeBy
hasResolution
range
domain domain domain
range
Problem
hasProblem domain
subClassOf
subClassOf
subClassOf
Attitude
subClassOf
Quality
Post_Sale_Service
someValuesFrom
hasResolution
Resolution
subClassOf
Improving_Service
Complaint
subClassOf
subClassOf
No_Response
Strong_Complaint
Light_Complaint
domain
domain
domain
hasProblem
subClassOf
hasProblem
domain
hasResolution
someValuesFrom someValuesFrom allValuesFrom
Logistics_Service
Post_Sale_Service
owl:unionOf
Improving_Service
Economic_Compensation
owl:unionOf
owl:intersectionOf
Quality
No_Response
Attitude
owl:intersectionOf
owl:unionOf
Figure 1 A framework of customer complaint ontology
In Figure 1, we put forward a framework of customer complaint ontology. We would not hope to
put this ontology as a standard ontology in complaint management, since the standardization of
complaint ontology relies on the cooperation and endeavors of many groups. In CRM, there exist many
semantic relations that need to be expressed so that machine could understand and process. How to
change statements expressed in natural languages to statements of formal descriptive logic is the key
task in ontology building [12]. In the framework of complaint ontology, we have considered capabilities
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to express semantic relations provided by OWL.
OWL classes are interpreted as sets that contain individuals. They are described using formal
(mathematical) descriptions that state precisely the requirements for membership of the class. Properties
are binary relations on individuals, i.e. properties link two individuals together. Individuals represent
objects in the domain that we are interested in.
In Figure 1, classes are illustrated in ellipses and properties in rectangles. OWL is
property-centricity. Each property has domain and range. Complaint is defined as a class. It has two
subclasses: Light_Complaint and Strong_Complaint. The domain of the four properties (hasNumber,
hasProblem, hasResolution, madeBy) is the class Complaint. The range of the property hasProblem is
the class Problem. The range of the property hasResolution is the class Resolution. The range of the
property madeBy is the class Customer. The class Problem has four subclasses: Logistic_Service,
Post_Sale_Service, Attitude, Quality. The class Resolution has three subclasses:
Economic_Compensation, Improving_Service, No_Response. The domain of the property hasContact is
the class Customer. The range of hasContact is the class Contact. The domain of the five properties
(hasName, hasEmail, hasZipcode, hasCity, hasCountry) is the class Contact. The range of the property
hasCountry is the class Country. Class Country is the equivalent class of an enumeration class having
individuals of China, America, France, Germany, England, Australia, Japan, and Korea.
The restriction allValuesFrom is stated on a property with respect to a class. It means that this
property on this particular class has a local range restriction associated with it. Thus if an instance of the
class is related by the property to a second individual, then the second individual can be inferred to be an
instance of the local range restriction class. The restriction someValuesFrom is stated on a property with
respect to a class. A particular class may have a restriction on a property that at least one value for that
property is of a certain type [10]. In Figure 1, the class Light_Complaint has the property hasProblem
restricted to have someValuesFrom the class Logistic_Service and Post_Sale_Service. It also has the
property hasResolution restricted to have someValuesFrom the class Improving_Service and
Economic_Compensation. Then, the two property restrictions intersect. The class Strong_Complaint has
the property hasProblem restricted to have someValuesFrom the class Attitude. Its also has the property
hasResolution restricted to have allValuesFrom the class No_Response. Then, the two property
restrictions intersect. Moreover, the class Strong_Complaint has the property hasProblem restricted to
have someValuesFrom the class Quality and this restriction and the result of upper intersection forms
the Boolean combination of unionOf.
4 Using OWL to Express Semantic Relations of Customer Complaint Ontology
Protégé-2000 is an integrated software tool used by system developers and domain experts to
develop knowledge-based systems. Applications developed with Protégé-2000 are used in problem
solving and decision-making in a particular domain. Protégé is a free, open-source platform that
provides a growing user community with a suite of tools to construct domain models and
knowledge-based applications with ontologies. The Protégé platform supports two main ways of
modeling ontologies: Protégé-Frames editor and Protégé-OWL editor. The Protégé-OWL editor enables
users to build OWL ontologies for the Semantic Web [13].
In the environment of Protégé-2000, we have built OWL ontology of customer complaint based on
Figure 1. OWL provides additional vocabulary along with a formal semantics such as disjointWith,
intersectionOf, unionOf, complementOf, oneOf, allValuesFrom, someValuesFrom, minCardinality,
maxCardinality and cardinality for the purpose of expressing more semantic relations and restrictions
such as disjoint, intersection, union, complement, enumeration, property restrictions, and cardinality.
For example, OWL allows arbitrary Boolean combinations of classes and restrictions, such as property
restriction based on unionOf and property restriction based on intersectionOf. In OWL, class could be
described by enumerating all the individuals composing the class. Classes could be stated to be
disjointing with each other. In OWL representations of customer complaint ontology, the above
examples are used.
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5 Conclusions
At present, Web ontology languages have been standardized. However, building domain ontology
based on OWL just begins and has a long way to go. In building domain ontology, people should
explore the rationality in expressing domain concepts and relations between the concepts, considering
the capabilities of OWL (not the capability of natural languages). This is not an easy thing. Indeed,
OWL has the feature and capability to express semantic relations. But, the standard of OWL is general,
not oriented to any specific domain. People who are responsible for building given domain ontology
should have a process to recognize the capability of OWL in their domain application. In this paper, we
put forward a framework of customer complaint ontology and make some analyses. Our exploration
might not standardize customer complaint ontology. We explore with an intention to help people better
understand the rationality in expressing concepts and relations between the concepts in customer
complaint management in order that OWL customer complaint ontology and other domain ontology are
improved and standardized as soon as possible. Thus, machine could understand, share and process
customer complaint information, and make some reasoning without human interference.
Acknowledgements
This paper is supported by the great project of Philosophy and Social Science Research of Ministry
of Education of China under grant 05JZD00024. This paper is also supported by Postdoctoral Science
Foundation of Huazhong University of Science and Technology.
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