A CASE STUDY ON THE APPLICATION OF THE MAAEM

METHODOLOGY FOR THE SPECIFICATION MODELING OF

RECOMMENDER SYSTEMS IN THE LEGAL DOMAIN

Lucas Drumond, Rosario Girardi and Adriana Leite

Department of Computer Science, Federal University of Maranhão, Avenida Dos Portugueses, São Luís, Brazil

Keywords: Recommender Systems, Information Filtering, Semantic Web, Multi-Agent Systems.

Abstract: Recommender systems have been target of continuous research over the last years, being used as an

approach to the information overload problem. The Semantic Web is a new generation of the Web which

aims at improving the effectiveness of information access on the Web by structuring its content in a

machine readable way. Agents have been also object of active research on the software engineering field

considering the high level of abstraction for software development provided by the multi-agent paradigm.

This paper describes the modeling of Infonorma, a multi-agent recommender system for the legal domain

developed under the guidelines of MAAEM, a methodology for multi-agent application development, which

is also evaluated here.

1 INTRODUCTION

Recommender systems (Adomavicius and Tuzhilin,

2005) (Ziegler, 2004) are a particular type of

filtering applications. They help users to deal with

the problem of information overload. In content-

based approaches (Adomavicius and Tuzhilin, 2005)

(Balabanovic and Shoham, 1997) they provide the

users with recommendations of items that are similar

to the ones that they preferred in the past. This is

achieved by measuring the similarity between

information items representation and user profiles.

Most of the content-based filtering algorithms

use statistical-based methods to measure similarity

between user models and information item

representations (Adomavicius and Tuzhilin, 2005).

Those methods do not consider any kind of semantic

processing, which is the reason why ambiguity

problems are faced by content-based filtering

systems.

The Semantic Web (Antoniou and Van

Harmelen, 2004) is a new generation of the Web in

which data is structured in such a way that it can be

machine readable and exhibited in a user-friendly

way. This is done with the use of ontologies

(Gruber, 1995) and standard technologies defined by

the World Wide Web Consortium (W3C). With a

semantically structured representation of Web data,

recommender systems can use semantic-based

similarity measures in order to improve their

effectiveness.

Agent-oriented Software Engineering approaches

the increasing complexity of computing systems that

must operate in open and quickly changing

environments by improving our ability to model,

design and implement complex systems (Jennings,

2000).

MAAEM (“Multi-agent Application Engineering

Methodology”) (Lindoso and Girardi 2006) is a

software development methodology for multi-agent

application engineering based on the reuse of

software artifacts developed in a Domain

Engineering process guided by the MADEM

(“Multi-agent Domain Engineering Methodology”)

methodology (Girardi and Marinho, 2007).

Infonorma (Drumond, Girardi, Lindoso and

Marinho, 2006) is a multi-agent recommender

system for the legal domain that recommends legal

normative instruments to users according to their

particular interests. The information items of

Infonorma are represented as instances of the

ONTOJURIS ontology, which is written in OWL

according to the W3C recommendations. The system

was modelled under the guidelines of the MAAEM

methodology and this experience has contributed for

its evaluation.

155

Drumond L., Girardi R. and Leite A. (2007).

A CASE STUDY ON THE APPLICATION OF THE MAAEM METHODOLOGY FOR THE SPECIFICATION MODELING OF RECOMMENDER SYSTEMS

IN THE LEGAL DOMAIN.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - SAIC, pages 155-160

DOI: 10.5220/0002389201550160

 SciTePress

This paper focuses on the requirements

specification of Infonorma and the Application

Analysis phase of the MAAEM methodology. The

description of the architectural and detailed design

as well as the implementation of Infonorma is not

provided here due to space restrictions.

This paper is organized as follows. Section 2

introduces the main modeling concepts of MAAEM

and briefly describes its tasks and products in the

context of a multi-agent application engineering

process. Section 3 provides an overview of the

system specification, describing the tasks carried out

and their respective products. Section 4 analyzes

related work on multi-agent and Semantic Web

recommender systems. Finally, Section 5 concludes

this paper with some remarks on further work being

conducted.

2 AN OVERVIEW OF THE

MAAEM METHODOLOGY

Multi-agent Application Engineering (MaAE)

(Jennings, 2000) is a process for the development of

specific applications through the reuse of software

artifacts produced in Multi-agent Domain

Engineering (MaDE) (Girardi and Marinho, 2007), a

complementary and interdependent process.

MAAEM is a methodology for analysis, design and

implementation of multi-agent applications through

the reuse of software artifacts such as domain

models, multi-agent frameworks, pattern systems

and software agents. MAAEM also supports the

development of applications from scratch, without

reuse, as is the case of the development of

Infonorma.

The ONTORMAS (“ONTOlogy driven tool for

the Reuse of Multi-Agent Software”) ontology

works as a modeling tool and a storage repository

for products constructed on the Multi-agent Domain

Engineering and Multi-agent Application

Engineering processes. MAAEM products are

represented as instances of ONTORMAS ontology.

For the specification of an application, MAAEM

focuses on modeling goals, roles, activities and

interactions of entities of an organization. Entities

have knowledge and use it to exhibit autonomous

behavior. An organization is composed of entities

with general and specific goals that establish what

the organization intends to reach. The achievement

of specific goals allows reaching the general goal of

the organization. Specific goals are reached through

the performance of responsibilities that entities have

by playing roles with a certain degree of autonomy.

Entities playing roles have skills on one or a set

of techniques that support the execution of

responsibilities in an effective way. Pre-conditions

and post-conditions may need to be satisfied

for/after the execution of an activity. Knowledge can

be consumed and produced through the execution of

an activity.

For the specification of a design solution, roles

are assigned to agents structured and organized into

a particular multi-agent architectural solution

according to non-functional requirements.

Table 1 summarizes modeling phases, respective

tasks and modeling products of MAAEM.

Application analysis is performed through the

following modeling tasks: concept modeling, goal

modeling, role modeling and role interaction

modeling. An Application Specification is the

product of this phase and it is composed of each one

of these task products: concept model, goal model,

role model and role interactions model, respectively.

The concept modeling task aims at performing a

brainstorming of concepts involved in the

application and their relationships, representing

them in a concept model. These concepts can also be

selected for reuse from a domain model, if one is

available. This starts from an informal analysis of

the application requirements. These concepts are

refined in the subsequent modeling tasks.

Table 1: Modeling phases, tasks and products o

MAAEM methodology.

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The purpose of the goal modeling task is to

identify the goals of the system, the external entities

which it cooperates with and the responsibilities

needed to achieve them.

In the role modeling task, the responsibilities

identified in the goal model, are assigned to roles as

well as the used and produced knowledge, imposed

pre-conditions, post-conditions and required skills.

Once the roles present in the application are

selected, adapted and composed, it is needed to

establish how the internal entities playing the roles

interact with each other and with external entities,

which is done in the role interactions modeling task.

The product of this task is a set of role interaction

models, one for each specific goal.

The user interface prototyping task is developed

in parallel with the goal modeling, role modeling

and the role interactions modeling tasks. Its product

is a set of user interface prototypes.

The design phase, supported by the ADEMAS

technique, approaches the architectural and detailed

design, defining a solution to the requirements

specified in the analysis phase.

Application implementation approaches the

mapping of design models to agents, behaviours and

communication acts, concepts involved in the JADE

framework, which is the adopted implementation

platform.

3 APPLICATION

SPECIFICATION

3.1 Concept Modeling

Infonorma is a system that provides its users with

personalized recommendations of legal normative

instruments. Each legal user has a profile composed

by his/her own interests and identification. This

profile is represented by a user model as an instance

of the ONTOJURIS domain ontology.

Recommendations are based on legal normative

instruments, the information items. Each one of

these instruments has two main characteristics: the

type and the category or legal branch in which it is

classified. These characteristics are also part of the

interests of legal users. The system monitors a

legislative repository, a kind of information source

composed by normative instruments.

Each legal normative instrument, as well as each

legal user model, is represented as an instance of the

ONTOJURIS ontology. ONTOJURIS is a domain

ontology used to represent the structure of legal

normative instruments. This internal representation

is compared to the interests of the users in order to

generate the recommendations.

All those concepts are represented in the Concept

Model shown in Figure 1.

3.2 Goal Modeling

After applying the guidelines of MAAEM regarding

the definition and representation of goal models in

multi-agent systems to the area of content-based

filtering, particularly for the legal domain, the

following goals are captured:

 General Goal

 Provide personalized legal-normative

recommendations

 Specific Goals

 Model legal users

 Content-based filter of new legal

information

 Deliver recommendations

The complete goal model of the system is

depicted in Figure 2. It is possible to realize that the

general goal of the system is achieved by three

specific goals, and, as requirements for the

fulfilment of the specific goals, there is a set of

responsibilities which need to be exercised in order

to achieve the specific goals. Besides that, there are

also external entities to the system: a Legislative

repository and a Legal user. The legal information

for content-based filtering users is obtained from a

Legislative repository and the external entities

representing legal users receives filtered items and

provide some explicit information about their

interests.

The fulfilment of the specific goal model legal

users requires the exercise of explicit user profile

Figure 1: Concept model of Infonorma.

A CASE STUDY ON THE APPLICATION OF THE MAAEM METHODOLOGY FOR THE SPECIFICATION

MODELING OF RECOMMENDER SYSTEMS IN THE LEGAL DOMAIN

157

acquisition and the user model creation

responsibilities. Similarly, in order to achieve the

content-based filter new legal information specific

goal it is necessary performing the responsibilities

user model creation and legislative repository

monitoring. The type of the normative instruments is

explicitly specified in the ontology, but their

categories are not.

Because of that, whenever new legal information

items are identified, the system must find out in

which legal branches they can be classified

(information items classification into legal branches

responsibility) so the content-based similarity

analysis responsibility can be carried out. At last it is

necessary to exercise the filtered information

delivery responsibility.

The Legal user external entity represents the

users of Infonorma. Such users are expected to be

people interested in Law, more specifically in legal

normative instruments, such as lawyers and judges.

Such users are interested in certain legal branches

and types of legal normative instruments which are

more closely related to their work.

Originally, Infonorma used a Brazilian

government website as information source. The lack

of semantic markup of this source, entirely written in

HTML, shortened the efficiency of the system. To

overcome this problem a Legislative repository was

built in OWL, according to Semantic Web standards,

and an auxiliary application, using JENA framework

(McBride, 2002), that converts the data of normative

documents into an instance of ONTOJURIS in OWL

format was developed.

This Legislative repository contains the

normative instruments which are recommended to

the users. It is important to state that the external

entity is composed only by the instances of the legal

normative instruments. The instances of user models

are created and maintained internally by the system.

The goal of ONTOJURIS is to represent legal

normative instruments in the form of Semantic Web

documents. It also represents various legal branches

organized into a hierarchy so that the normative

instruments can be classified according to it.

A legal branch is defined by a class that has four

attributes: a name, which identifies it, a set of

weighted keywords, the specializes and the

generalizes attributes. The weighted keywords are

terms that are semantically related to the branch. For

example, Crime and Penalty are keywords for the

legal branch Penal Law. They were determined with

the aid of a domain specialist and are used to

classify a given normative instrument. The first one

(specializes) indicates the superclass (es) of a legal

branch and the second one (generalizes), the

subclass (es). Both of them have multiple

cardinality, so multiple inheritance is allowed.

The Legal Normative Instrument class has the

following attributes representing the structure of a

normative instrument: the preliminary part, which

identifies it; the normative part, in which the norms

are found; and final part with some additional

information about the normative instrument.

The User class has three main attributes. Each

user is identified by its name. The recommendations

are delivered through email messages, so it is

important to annotate the email address. And,

finally, the user interests, represented by a set of

instances of legal branches.

3.3 Role Modeling

Each one of the responsibilities identified in the

Goal Model is assigned to an internal entity playing

a role. This is expressed in a Role Model, which, due

to space limitations, is not shown here. Each role

requires, during its development, the usage and

production of certain knowledge, the fulfilment of

pre-conditions and post-conditions and specific

skills. Each skill is detailed with a brief description

and most relevant bibliographical references

describing it.

The Input Interface role is in charge of the

explicit profile acquisition responsibility. This

responsibility uses the user identification and

interests knowledge, to acquire the Legal user

profile. The profile acquisition is made explicitly

since there are not enough interactions of the user

with the system to acquire his/her profile implicitly.

On the top of that Infonorma is designed for users

who know what they want but the desired

information is not available in the moment, so it is

Figure 2: Goal model of Infonorma.

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reasonable to have the users specify their interests.

The specification of a new user profile is a pre-

condition for the explicit profile acquisition that

acquires a valid user profile.

The user modeler role is in charge of the user

model creation responsibility. When a valid user

profile is available, it creates an ONTOJURIS

instance user model by instantiating the class “User

Model” of ONTOJURIS with the information

available in the Legal user profile produced by the

first responsibility and the User model attributes

acquired from the ONTOJURIS ontology.

The Source Monitor is the role responsible for

the legislative repository monitoring, which uses the

skill information source update detection to perceive

changes in the Legislative repository to discover new

legal information items.

The information items classification

responsibility is assigned to the Classifier role that

classifies the normative instruments into one or more

legal branches assigning to each item the level of

similarity with each legal branch. This is done by

counting the keywords in the instrument and

comparing them to the keywords of each legal

branch.

The fifth responsibility is the Content-based

similarity analysis one. The Information Filter role

is in charge of it. The matching is performed by

comparing the type of the new information items

with the types each user is interested in. Once the

information items are classified into legal branches

the similarity analysis is performed by computing

the distance between the legal branches the user is

interested in and the legal branches the information

item is classified in.

The last responsibility is the filtered information

delivery one, assigned to the Output Interface role.

Once the information items are filtered (pre-

condition), the Output Interface produces the

personalized recommendations knowledge. They are

delivered to the user using the Electronic mail

message sending skill.

3.4 Role Interactions Modeling

Each one of the Role Interactions Model shows the

interactions related to each specific goal. The

interactions are numbered according to their

sequencing.

In the first role interaction model, related with

the model legal users specific goal, the explicit

profile acquisition, the user specifies his/her

identification and interests to the Input Interface role

that sends to the User Modeler the Legal user profile

so it can get the User model attributes from

ONTOJURIS and create the user models.

The second role interaction model is related to

the content based filter new legal information

specific goal. When any information source change

occurs, the Source Monitor informs the Classifier

about the new legal information items so that it can

perform the information items classification into

legal branches responsibility.

In order to perform the classification, the

classifier must get the ONTOJURIS legal branches.

Once the items are classified, they are sent to the

entity playing the Information Filter role that

performs the Content-based similarity analysis

responsibility. When the Information Filter role

receives those items, it requests the user models to

the User Modeler one and the ONTOJURIS legal

branches to the ONTOJURIS external entity.

At last, in order to deliver the recommendations,

the Information Filter sends to the Output Interface

the filtered information items which are sent to the

Legal User as personalized recommendations.

3.5 User Interface Prototypes

According to the goal, role, and role interactions

models, legal users interact with Infonorma for

specifying their profiles (explicit profile acquisition

responsibility). This interaction is supported by a

Web form in which the user specifies his/her

identification (login, email and password) and the

types and categories he/she is interested in.

On the other hand, Infonorma provides legal

users with personalized recommendations through

the filtered information delivery responsibility. This

can be considered as the output of the system and

takes place when the filtered information is

delivered to the user through email messages.

4 RELATED WORK

The state of art of the techniques used in

recommender systems has been moving forward in

the last years, improving considerably the

effectiveness of these systems (Adomavicius and

Tuzhilin, 2005).

A new research area, that has appeared recently,

aims at improving the effectiveness of recommender

systems using, as information source, semantically

structured documents using technologies of the

Semantic Web (Ziegler, 2004). Ontologies are the

knowledge representation structures used by

Semantic Web technologies. Work on the usage of

A CASE STUDY ON THE APPLICATION OF THE MAAEM METHODOLOGY FOR THE SPECIFICATION

MODELING OF RECOMMENDER SYSTEMS IN THE LEGAL DOMAIN

159

ontologies in user modeling and similarity analysis

in recommender systems has already been developed

(Middleton, Shadbolt, and De Roure, 2004).

A survey of current machine learning techniques

for automatic text classification that can be used to

classify information items into categories of the

taxonomy is provided in (Sebastiani, 2002).

The agent paradigm can be exploited in the

development of information filtering systems such

as recommender systems. Experiences in this area

are described in (Sheth and P. Maes, 1993).

There has been much work done in the domain

of Artificial Intelligence and Law. The development

and usage of legal ontologies to represent and access

legal information has been addressed in (Tiscornia,

2001) and (Valente, 1995). (Benjamins, Casanovas,

Breuker and Gangemi, 2005) provide an overview of

the application of Semantic Web technologies to the

legal domain.

5 CONCLUSIONS

This work described the requirements analysis of

Infonorma multi-agent system. A solution to the

requirements specified here was designed and

implemented also under the guidelines of MAAEM

methodology, although it is not described in this

paper. The next step is to carry out tests with real

legal users and define criteria for measuring the

quality of recommendations.

In the current version of Infonorma users have to

explicitly specify their interests by filling a form.

This is used to create and update the user model and

no feedback is obtained from the user. One research

issue to be addressed in the future is to combine web

usage mining techniques (Girardi and Marinho,

2007) with Semantic Web technologies to support

the implicit acquisition of user profiles and their

dynamic update through user feedback.

The case study described in this article also

contributed for the evaluation of the MAAEM

methodology application analysis phase.

Both MAAEM and ONTORMAS have proved

their usefulness for capturing and specifying

requirements of a specific application through

appropriate guidelines and representation and

decomposition mechanisms.

ACKNOWLEDGEMENTS

This work is supported by CNPq.

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