MANAGING USER INTERACTION IN AN

ONTOLOGY-BASED SYSTEM

Anna Goy and Diego Magro

Dipartimento di Informatica, Università di Torino, C.Svizzera 185, Torino, Italy

Keywords: User interaction, User interfaces, Ontology, Semantic Web, e-Business.

Abstract: In this paper, we present an approach to the management of user interaction within an ontology-based

system. In particular, we discuss the case of ARNEIS, an “intelligent” web-based repository of software

solutions, that enables software houses to upload a description of their software solutions for business

automation, and small-to-medium sized enterprises to find software products or services supporting their

activities. To this purpose, as argued in the paper, a particularly meaningful field is Customer Relationship

Management, that is thus exploited as a test bed domain. The functionality of ARNEIS is based on an

ontological representation of the domain knowledge, which represents the shared conceptual vocabulary to

express software descriptions and technological requirements. In this paper we describe our proposal for the

management of a user-friendly interaction enabling software houses to upload the semantic representation of

the description of their offers. The approach we propose within the ARNEIS scenario represents an example

of a more general solution to face the issue of how to build formal representations of resources in an

ontology-based IR system.

1 INTRODUCTION

In order to be competitive in the globalized market

Small-to-Medium sized Enterprises (SME) have to

exploit the support provided by innovative

technological solutions to their business. On their

side, Information and Communication Technologies

(ICT) relies more and more on Internet and Web

technologies: web-based solutions are available for

almost any kind of application, supported by

innovative technologies at different levels, ranging

from Cloud Computing infrastructures (Creeger,

2009; Dikaiakos et al., 2009), to Web Services

(Alonso et al, 2004), to Semantic Web (Antoniou

and Van Harmelen, 2004).

However, the technological support is not

enough: in order to be competitive, SME should also

take care of new business and management

approaches. An example is the way in which they

handle their relationships with customers. The new

market requires personalized approaches to the

single customer, as well as flexible offers, that need

to be updated rapidly. Moreover, in order to be

aware of the market and customer behavior trends,

data about offers, sales, communications with

customers, and so on have to be elaborated very

rapidly, to support management and marketing deci-

sions. For these reasons, SME pay every day more

and more attention to the principles of Customer

Relationship Management (Freeland, 2005) and to

those ICT products and services supporting it. The

key feature of the CRM approach is a one-to-one

marketing perspective, i.e. establishing personalized

relationships with the single customer, by producing

personalized offers, pricing, after-sale services, etc.

Moreover, CRM is a field in which technological

innovation could bring great benefits since it

requires the processing, integration, and analysis of a

huge amount of heterogeneous knowledge (about

customers, sales, communications, etc.); it also

requires effective, fast and integrated

communication tools.

Within this scenario, we think that there are two

main issues that play a major role:

(1) A clean, complete, and sharable ontology

(Guarino, 1997) defining the concepts related to

CRM is of paramount importance, in order to

support the mentioned knowledge management

activities, as well as Enterprise Application

Integration; see, for instance, (Benjamins, 2008).

(2) SME would get great benefits from a web-based

service supporting an intelligent matching between

supply and demand for CRM-related tools.

194

Goy A. and Magro D..

MANAGING USER INTERACTION IN AN ONTOLOGY-BASED SYSTEM .

DOI: 10.5220/0003296401940201

In Proceedings of the 7th International Conference on Web Information Systems and Technologies (WEBIST-2011), pages 194-201

ISBN: 978-989-8425-51-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Moreover, the shared knowledge mentioned in (1)

can be exploited in a cross-enterprise scenario like

the one outlined in (2). Thus, we designed an

architecture for a web-based intelligent system

supporting SME in finding suitable software

solutions for their business, and we chose CRM as a

testbed for this architecture.

On the basis of this architecture, we developed

ARNEIS (Advanced Repository for Needs of

Enterprises and Innovative Software), a prototype

implementation of a web-based repository of

software solutions, that exploits Web Services and

Semantic Web technologies. ARNEIS users are: (1)

ICT companies (i.e., software houses) that offer

software solutions for business automation, but can

be in trouble in getting in contact with their potential

customers; (2) SME that aim at finding software

products or services supporting their business and

management activities, but lack the know-how to

find the right ICT solution that fits their needs.

Such an intelligent repository has to be equipped

with a large and detailed knowledge base, e.g. an

ontology, which represents the shared conceptual

vocabulary; such an ontology is the basis for the

matching algorithm used to suggest SME the most

suitable software solutions, given their needs.

Moreover, ARNEIS requires a web-based user

interface (UI) enabling ICT companies to describe

their software solutions and SME to express their

requirements and needs and to find suitable software

solutions supporting their business.

We claim that both these goals, i.e. building the

CRM ontology and defining the system UI, have to

be based on a domain analysis that takes into

account how users, both from ICT companies and

from SME, talk (and think) about CRM activities

(see Section 3).

The CRM ontology developed for the ARNEIS

project is described in (Magro and Goy, 2008a;

Magro and Goy, 2008b), while this paper focuses on

the definition of the UI enabling the users to interact

with it. In particular, Section 2 briefly describes the

system architecture and Section 3 reports the main

results of the domain analysis; Section 4 focus on

the UI management: it provides a brief survey of the

relevant related work in the field, it discusses the UI

design choices and it describes the mechanism for

UI management. Section 5 briefly discusses some

open issues and concludes the paper.

2 ARNEIS ARCHITECTURE

The basic architecture of the ARNEIS system is des-

cribed in (Goy et al., 2008); Figure 1 shows a

simplified version of it.

Figure 1: ARNEIS system architecture (a simplified

version taken from (Goy et al., 2008c)).

It is a standard three-tiers architecture, where the

presentation layer is represented by a standard web

browser.

The application logic is contained in the ARNEIS

core, which includes three main components: the UI

Manager, the Knowledge Manager, and the

Matching Engine.

The UI Manager generates the UI taking as input

the concepts, properties and relations provided by

the Knowledge Manager and represented in the

Ontology; moreover, it collects information

provided by the user and forwards it to the

Knowledge Manager, that builds the semantic

representation of software descriptions and SME

requirements.

The Knowledge Manager mediates the

interaction between the UI Manager and the data

layer (its role will be discussed in more detail in

Section 4.3). Moreover, it dialogs with the Matching

Engine (whose description is out of the scope of this

paper), by providing it the semantic representations

on the basis of which the Matching Engine will

calculate the correspondence between SME

requirements and software house offers.

The data layer contains the Knowledge Base,

whose main components are the Ontology,

representing the system semantic competence about

the domain (CRM), and the semantic representation

of software descriptions and SME requirements

(Semantic Descr KB). Both the Ontology and the

Semantic Descr KB are written in OWL

(http://www.w3.org/2004/OWL/).

The OWL domain Ontology is based on a CRM

Reference Ontology (Guarino, 1997), described in

(Magro and Goy, 2008a; Magro and Goy, 2008b),

which in turn is based on DOLCE (http://www.loa-

cnr.it/DOLCE.html). The CRM Reference Ontology

basically models: (1) business activities (e.g., sales,

offers, communications, appointments, etc.); (2)

business relationships which the company is

involved in (e.g., relationships with actual or

potential customers); (3) the knowledge that the

MANAGING USER INTERACTION IN AN ONTOLOGY-BASED SYSTEM

195

company has on (or derives from) business activities

and relationships; (4) software supporting business

activities and knowledge management.

3 DOMAIN ANALYSIS

Given the architecture described in the previous

section, which is the information needed by the UI

Manager to generate the UI? And which kind of

information should be elicited from the user in order

to build a structured and formal representation of the

software solutions offered?

To answer this question, we carried out a

detailed domain analysis, in order to understand how

users (both software houses and SME) talk about

CRM.

We analyzed two types of information sources:

(1) Documents (e.g., brochures, white papers),

produced by ICT companies, describing their CRM

software tools. (2) Interviews with (a) salesmen

from ICT companies, aimed at eliciting the way they

describe software solutions for CRM for SME; (b)

managers of SME, in order to understand which

concepts and terms they use to think about their

activities related to customer management.

The analysis of documents and interviews

provided us with the following outcomes:

(1) We identified the main concepts, properties and

relations involved in the description of CRM

activities in SME, which represent the basic

requirements for building the CRM Ontology.

(2) We identified a set of dialog topics, i.e. concepts

that emerged to be the keys of the description of

CRM activities. Within the system, these concepts

can be represented as templates, i.e. general

conceptual patterns that are instantiated with more

specific concepts in the different

documents/interviews, by means of various

linguistic forms. Each template has a corresponding

formal semantic representation in terms of the

Ontology. Basically, each description of a software

solution (or a SME requirement) is an instantiation

of a number of such templates (see Section 4.3).

4 USER INTERACTION

As mentioned in Section 2, the system needs a

formal representation of the descriptions of software

solutions and of technological requirements (both

based on the terms defined in the system Ontology)

in order to find the software solutions best matching

the SME requirements. Since it is unrealistic to ask

users to write descriptions in a formal Semantic Web

language, such as OWL, the generation of a user-

friendly UI turned out to be an issue of major

importance.

4.1 Related Work

The importance of the UI to access complex

knowledge bases is a topic largely studied in various

research fields. Many authors recognize that,

recently, there has been an increase of interest in the

design of UI for systems based on semantic

technologies (see, for instance, the SWUI workshops

series). The main issue within this field is, quite

obviously, the fact that the user interacting with a

formally encoded knowledge base (e.g., an

ontology) should not be exposed to the details of

such a formal representation. In other words,

“semantic technologies should be invisible to users”

(Benjamins, 2008), p. 76.

The most traditional approaches facing this issue

are devoted to support user queries aimed at

searching for information encoded in formal

knowledge bases (databases or ontologies). These

approaches are mainly based on the translation of

keywords (provided by the users) into formal queries

(being SQL, or DL, or other formal languages); see,

for instance, (Tran et al., 2007b).

Among many approaches to build UI to access

knowledge bases, some studies propose to use NL-

based UI; e.g. (Bernstein and Kaufmann, 2006),

(Cimiano et al., 2008), (Damljanovic et al., 2010).

Other approaches propose graphical (web-based) UI;

e.g., (Thoméré et al., 2002).

(Bhagdev et al., 2008a) and (Bhagdev et al.,

2008b) describe two tools supporting the user

interaction with formal knowledge bases: (a) K-

Forms is a tool enabling users to define knowledge

structure and content through a user-friendly form-

based UI; the tool then translate such a knowledge

into a formal (RDF/OWL) representation; (b) K-

Search is a tool that supports knowledge search and

sharing.

In all the mentioned approaches the user input is

a query aimed at extracting information from a

(formal) knowledge base. In ARNEIS, the two types

of users interacting with the system have different

goals: the goal of a user from a software house is to

provide the system with a description of a software

solution, hoping that it will match some user

requirements; the goal of a user from a SME is to

“explain” the system the SME technological needs

in order to find a suitable software solution. Thus, in

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ARNEIS, in both cases, the user input is a complex

description (of a software product or of a set of

requirements) based on an existing ontology. In

order to elicit this kind of information the system

requires a UI that is more complex than the UI

typically provided by the previously mentioned

approaches, where the user simply provides the

system with a query.

Other works concerned with user interaction with

formal representations are authoring tools: in these

cases the UI is aimed at enabling the user to design

and populate a complex knowledge structure such as

an ontology (e.g. Protégé:

http://protege.stanford.edu/). With respect to these

tools, ARNEIS has, not only a different goal, but

also different types of users: in fact, ARNEIS users

are not expert in knowledge representation and they

could find it difficult to interact with an authoring

system, even if provided with a smart (maybe

graphical) UI.

Some approaches to Semantic Search propose to

build ontology-based Information Retrieval (IR)

systems, i.e. systems in which both resources and

user queries are represented in a formal semantic

language. For example (Tran et al., 2007a) proposes

a resource model based on various integrated

ontologies, expressed in OWL, enabling resources

(documents) representation in terms of ontology

elements (i.e., entities and axioms). As discussed by

the authors, the issue of how to obtain formal se-

mantic representations of resources is still open.

Some steps have been made towards the automatic

extraction of such representations from (textual)

documents (e.g., Banko et al, 2007), but the results

in this field are still poor. As an alternative, “a

manual approach can be undertaken” (Tran et al.,

2007a), p. 65. In particular, we think that a manual

approach actually makes sense in those cases in

which resources are not already available as

documents. For instance, in a scenario as the one

described above, a software house may have only

simple brochures describing its software products; it

seems to be reasonable to provide it with a tool

supporting the construction of a formal semantic

representation of their software products, which can

be handled as resources by an ontology-based IR

system. Our proposal is as a step in this direction.

In the following section we will describe how we

faced the issue of the design of the user interaction

in ARNEIS. However, the proposed approach aims

at providing a general solution to solve the problem

of how to build formal representations of resources

in an ontology-based IR system.

4.2 User Interface

Concerning the type of UI most suited to our system,

we chose on-line forms, since they are very familiar

to web users; see (Bhagdev et al., 2008a). However,

usually, users from SME, interacting with ARNEIS

Figure 2: “(Dynamic) acquisition of data from (another) enterprise application” template.

MANAGING USER INTERACTION IN AN ONTOLOGY-BASED SYSTEM

197

in order to look for software solutions supporting

their CRM activities, are not technical experts and

thus they can be in trouble even in filling in large

on-line forms, possibly requiring technical skills in

order to be completed. Thus, we are studying the

possibility of implementing different types of UI for

these users: a Natural Language (NL) UI, in which

the SME users can freely express their requirements,

and a UI based on a graphical representation of

business processes (BP), based on the idea that

business processes could be the form in which

companies think about their business and

management activities.

These two alternative UIs are a work in progress.

The form-based UI, instead, can be suited for ICT

company users, which are probably skilled enough

about the functional and technological aspects of

their software products or services. Moreover, such

users are probably also more used to web-based

interaction, often consisting in a sequence of on-line

forms. Finally, such users are probably also more

motivated to complete a possibly boring task, since

they are describing the software solutions they offer,

and this can be viewed as an effective promotion of

their products and services.

Thus, in the following, we will concentrate on

the form-based UI devoted to the acquisition of

descriptions of software solutions by ICT

companies.

4.3 Managing User Interaction

We claim that the design of the user interaction

enabling ICT company users to provide a description

of their software solutions could only be based on an

analysis of the way in which users talk about CRM.

For this reason, in the domain analysis phase,

besides Ontology requirements, we also identified

dialog topics, representing the key concepts of the

descriptions of software solution supporting CRM

activities (see Section 3).

In order to clarify how dialog topics are

exploited in the system, let’s look at an example.

One of the templates representing dialog topics we

extracted from our analysis corresponds to the

concept of “(dynamic) acquisition of data from

(another) enterprise application”, which means that

the described CRM software application can acquire

data by directly communicating with another

application (within the system, templates do not

have a linguistic form, but only a formal, OWL, one.

For the sake of readability, here we provide also a

rough linguistic “translation”). Such a general

concept is instantiated in different

documents/interviews with more specific concepts

by heterogeneous linguistic expressions: specific

instances of this template include more specific

concepts in place of “data” and in place of

“enterprise application”; for example “real time

acquisition of customers info from Ms Outlook”, or

“(management of) product records acquired from

ERP software”.

We decided to represent templates as ontological

concepts belonging to an Application Ontology

(Guarino, 1997), specific for ARNEIS, and linked to

the upper CRM Ontology.

A template represents a structured concept in

which there are some variables (slots). By default,

such variables are filled in by generic concepts (e.g.

“data”), that can be replaced by more specific

concepts (e.g. “product data”).

Figure 2 shows the OWL logical representation

(generated by Protégé) of the class representing the

template corresponding to the concept of “(dynamic)

acquisition of data from (another) enterprise

application”.

In order to identify slots within a template, we

needed a way lo “label” them, possibly without

compromising the correctness of the OWL syntax.

Thus, we decided to indicate them by adding, to

each concept that represents a default slot filler, the

expression and S

, where S

is a unique identifier

(automatically generated) for that slot. Thus, for

example, in Figure 2 the expression (CRM_element

and S

) identifies a slot (labeled S

) filled in, by

default, by the CRM_element class (intuitively

speaking, CRM_element represents all the items that

are typically involved in CRM: customers, products,

orders, sales, and so on). If we aim at expressing the

acquisition of data about customers (e.g., “customers

info”), in the template instantiation, the class

CRM_element will be replaced by a more specific

one, i.e. Customer (subclass of CRM_element) and

the expression and S

will be deleted; if we aim at

expressing the acquisition of data about products

(e.g., “product records”), CRM_element will be

replaced by Product_or_service (subclass of

CRM_element). Analogously, the class

(Application_software and S

) can be replaced by

the class Ms_Outlook, or ERP.

At a first glance, this labeling solution may seem

to bring to odd meanings. In fact, from a semantic

point of view, by writing Information_object ...

refers_to some (CRM_element and S

), we are

actually saying that an Information_object refers to

something that is, at the same time, a CRM_element

and a S

(see Figure 2), which is not the meaning we

would like to express. Thus, it is important to stress

WEBIST 2011 - 7th International Conference on Web Information Systems and Technologies

198

that the expression and S

is used merely as a label,

to identify slots within a template. We chose this

solution because it preserves the syntactic

correctness of the OWL representation. Moreover, it

does not produce odd semantic results, since in

ARNEIS not instantiated templates are never used in

any form of semantic reasoning.

For each template, the UI Manager has to

generate a set of web-forms, aimed at eliciting the

information needed for filling in the slots. Since,

typically, the knowledge on the basis of which such

forms are generated does not change in time, web

forms are pre-compiled off-line; they are re-

generated only in case the knowledge bases are

modified.

In order to enable the UI Manager to generate the

web forms, the Knowledge Manager performs two

(nested) steps:

(1) For each template in the Application Ontology, it

extracts all the slots by looking for the expressions

of the form (C and S

), where C is a named class of

the reference Ontology.

(2) For each slot (identified by S

), the Knowledge

Manager extracts from the Ontology all the

subclasses of C and provides the UI Manager with

this information.

The UI Manager, in turn, for each slot, generates a

web form in order to ask the user which subclass of

C (if any) she wants to consider.

For example, Figure 3 shows the form asking the

user to select the subclasses of CRM_element.

Figure 3: web form for filling in the first slot of the

template corresponding to “(dynamic) acquisition of data

from (another) enterprise application”.

The user selects the desired subclass (C

) and the

UI Manager sends this information back to the

Knowledge Manager, that substitutes C

in place of

(C and S

) within the corresponding template slot.

The result (that refers to the example presented

above) is shown in Figure 4.

Figure 4: part of the instantiated template after the user

answer.

There are some issues to be faced in order to

make this mechanism work: (1) Which is the proper

question the UI Manager should pose to the user

(e.g., in Figure 3, “Would you like to acquire data

concerning...”)? (2) In some cases, the user may

want to specify a more specific class (e.g., she wants

to acquire data about golden customers, which are

represented, within the Ontology, by the class

Golden_customer, subclass of Customer). (3) It is

quite common that the suited value for filling in a

slot depends on the value assigned to another slot (of

the same template). In the example above (see

Figure 2), if we fill in the slot S

with Customer,

meaning that we want to acquire information about

customers, the slot S

(representing the knowledge

structure that is modified by the data acquisition)

have to be filled in with Customer_database.

Moreover, many slots within a template are usually

filled in by the same concept. For instance, in Figure

2, if we fill in the slot S

with Customer, the slots S

and S

have to be filled in with Customer too.

In the following we will explain how we faced

the mentioned issues.

(1) Natural Language Questions. The knowledge

engineer who configures ARNEIS on a new domain

is in charge of defining the Application Ontology

containing the templates. When doing this, she

identifies the slots and, for each slot, she provides a

natural language question that will be used by the UI

Manager to generate the form referring to that slot.

The link between the slot S

within the template T

) and the corresponding question is stored in a

Configuration KB which is accessed by the UI

Manager during the form generation process.

(2) Indirect Subclasses. The Knowledge Manager,

when extracting from the Ontology all the subclasses

of C (step (2) mentioned above), actually extracts

not only the direct subclasses of C, but the whole

subtree. The UI Manager, on the basis of this

information, includes a link to enable the user to

optionally expand the subtree (see “view more” links

in Figure 3): in this way she is enabled to select any

(direct or indirect) subclass she is interested in.

However, given the complexity of the Ontology,

listing all the (named) subclasses of a given class C

could result in a too long list, containing concepts

that are relevant from the formal point of view, but

not from the user perspective. For this reason, we

added to the Configuration KB, for each slot (C and

), identified by the unique label T

, a list of the

subclasses of C that should be asked to the user. For

example (the Configuration KB is an XML

document; here we show its content in pseudo-code

instead of XML for the sake of readability):

MANAGING USER INTERACTION IN AN ONTOLOGY-BASED SYSTEM

199

 {Sales_agent, Customer, ...}

The list of “subclasses to be asked” potentially

contains both direct and indirect subclasses of C.

(3) Dependencies between slots. In order to take into

account possible dependencies between slot fillers,

the Configuration KB also contains some if-then

rules representing such dependencies; for instance:

IF T

= Customer

THEN T

= Customer_database

IF T

= Customer

THEN T

= Customer

These dependency rules enable the UI Manager to

avoid asking the user useless questions: after having

asked the filler for slot S

, slots S

and S

will be

filled in automatically.

When the user has completed all the forms

proposed by the ARNEIS system, the instantiated

templates are saved in the Semantic Descr KB (see

Section 2): the set of such instantiated templates is

the OWL representation of the software solution

supporting CRM, proposed by the ICT company.

5 DISCUSSION

AND CONCLUSIONS

In this paper, we described the management of the

user interaction within the ARNEIS system, an

“intelligent” web-based repository of software

solutions, that enables ICT companies to upload a

description of their software solutions for business

automation, and SME to find software products or

services supporting their business and management

activities. The functionality of such an intelligent

repository is based on a semantic representation of

the domain knowledge, which represents the shared

vocabulary to express both software descriptions and

technological requirements. In particular, we

described the management of a user-friendly

interaction enabling software houses to upload the

semantic representation of the description of their

products and services. The approach we proposed

within the ARNEIS scenario suggests a general

solution to solve the problem of how to build formal

representations of resources in an ontology-based IR

system.

Our approach is strongly based on a domain

analysis that takes into account how users talk about

their business activities and the software

applications that could support such activities. In

fact, the mechanism described in this paper relies on

templates stored in an Application Ontology and a

Configuration KB, that have to be built by the

knowledge engineer who configures the system on a

domain (e.g., CRM). This task could represent a

considerable knowledge acquisition effort. However,

the definition of templates and Configuration KB is

typically done once and usually does not require

frequent updates. Moreover, the Configuration KB

definition can be easily supported by a user-friendly

tool that provides the knowledge engineer with

simple mechanisms supported by system defaults

(e.g., the lists of the subclasses of C that have to be

proposed to the user can be automatically built by

default including all the available subclasses; the

knowledge engineer will be enabled to simply delete

those ones she thinks are not meaningful for the end

user).

Since the knowledge acquisition effort is a key

aspect of knowledge-based systems like ARNEIS,

the development of the mentioned user-friendly tool,

supporting the knowledge engineer in the system

configuration on new domains, represents our main

future work.

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