=Paper=
{{Paper
|id=Vol-152/paper-7
|storemode=property
|title=An Architecture for Natural Language Dialog Applications in Data Exploration and Presentation Domain
|pdfUrl=https://ceur-ws.org/Vol-152/paper8.pdf
|volume=Vol-152
|dblpUrl=https://dblp.org/rec/conf/adbis/LaukaitisVBA05
}}
==An Architecture for Natural Language Dialog Applications in Data Exploration and Presentation Domain==
https://ceur-ws.org/Vol-152/paper8.pdf
An Architecture for Natural Language Dialog
Applications in Data Exploration and Presentation
Domain
Algridas Laukaitis, Olegas Vasilecas, Raimondas Berniunas,
and Eimontas Augilius
Vilnius Gedimino Technical University
algridas@isl.vtu.lt
Abstract. In this paper we present architecture and its implementation for the
natural language dialog (NLD) applications in data exploration and presentation
domain. Presented architecture can be integrated as a part of corporate informa-
tion delivery web portal to bring new modalities for user interfaces. The archi-
tecture is based on software agent's paradigm and supports mobile as well as
stationary agents. On this NLD architecture we implemented open source pro-
ject for data exploration when the system user explores corporate data in the
terms of machine human dialog. The following well know toolboxes has been
integrated in this project: GATE - general architecture for natural language
processing, IBM natural language understanding (NLU) toolbox, JOONE - neu-
ral network toolbox, Aglets - mobile agents framework.
1 Introduction
Data environments are becoming more and more complex as the amount of informa-
tion a company manages continues to grow. Information delivery web portals have
emerged as the preferred way to bring together information resources. Using informa-
tion delivery web portal, your organization's employees, customers, suppliers, busi-
ness partners, and other interested parties can have a customized, integrated, person-
alized, and secure view of all information with which they need to interact. But one of
the big challenges remains for organization: it is how to teach employees or custom-
ers to use and understand complex database environment without involving experts
and IT resources which are costly and time consuming. One of the solutions it to use
natural language database interfaces.
From the early 80's and 90's there was many efforts involved in the research of
natural language use for information extraction from data base management systems
(DBMS). Natural language database interfaces (NLDBIS) are systems that allow
users to access information stored in a database by formulating requests in natural
language. For example a NLDBI would typically be able to answer questions like the
following.
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136
"Show me the latest prices of IBM shares"
The system that supports (NLDBIS) functionality automatically would translate
user sentences to adequate SQL script, query some DBMS and return results to the
user. NLDBIS have received particular attention within the natural language process-
ing community (see [2] for reviews of the field), and they constitute one of the first
areas of natural language technology that have given rise to commercial applications.
Some successes have been achieved and some commercial applications emerged but
the NLP techniques have not become a popular approach for DBMS interfaces. As
was mentioned by researchers in [2, 3, 7, 8, 22, 28, 33] this is due to:
1. Graphical and menu driven interfaces achieved the level of sophistication
that many data analyst can do analysis without deep knowledge of some
data queering language (e.g. SQL). On the other side NLP techniques has
not been able to deliver interfaces of adequate sophistication.
2. Most research and achieved results reports on the possibility to generate
only one data queering script (in most cases this was one SQL sentence)
generated from one natural language sentence. They do not support com-
plex dialog, which is the most usual case in real life when we want inter-
actively to build adequate request.
3. Most systems are commercial products [2] and because they are close sys-
tems there is difficulties in extending such systems. And we think that
only open source projects can bring more attention from researchers to
NLDBIS field.
In most available systems only system administrator are able to parameterizes the
system. There are no available systems in which learning process will be integrated in
user's daily work life. We think that resent advances is building personal assistants in
such fields like an adaptive information research from internet [5] or personalized
learning knowledge maps [24] will renew researches interest in (NLDBIS) field.
Our approach in this paper was the use of dialog instead of one sentence and on
the other hand we do not look at NLP techniques as the one exclusive solution to
query databases, instead we look at it as supplementary technique and as a part of
multi-modal interface. To tackle mentioned problems we propose our system Jmin-
ingDialog, which is constituent part of our open source information delivery web
portal JMining. We have no intention to describe JMining architecture and informa-
tion delivery portals in details and for details about it implementation as open source
project we refer to [15] and [16].
Instead, we describe architecture of natural language dialog and natural language
understanding (NLU) modules for building small web databases queering applica-
tions using only natural language. For those modules we propose an architecture that
is based on stationary and mobile intelligent agents. Stationary agents are used when
amount needed to support communication between distributed agents are small and
interchange of short messages is enough. The pluses are that message passing pro-
vides platform- and language-independence as well as separation of transport and
content information. The use of mobile agents in architecture is reasoned by the ap-
proach, which argues that knowledge consists largely of a personal, stored locally
data files. Mobile agents can travel to various hosts where local knowledge is stored
and gather necessary information that meets user request.
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The paradigm of agents is a very promising approach to overcome some of the
problems connected with heterogeneity on the side of the data sources as well as on
the side of the users. As agents should operate autonomously and can be loosely cou-
pled, they are well suited for the integration of distributed heterogeneous data
sources, building unifying wrappers around them. This becomes especially beneficial,
if agents can learn to extract information from an information source automatically
(see for example [10] and [25]). On the side of the users, the paradigm of personal
information agents offers a way to encapsulate the interests, the knowledge as well as
the preferences of individual users. Personal agents can take the role of mediators
between users and information sources, as well as between users among each other
(see also [10] and [30]). Furthermore we present an agent architecture consisting of a
set of asynchronously operating agents. This architecture enables us to perform so-
phisticated data and interaction analysis, without loosing the property of short re-
spond times essential for interactive work in real-time. Based on the paradigm of
mobile agents, we present a model for expressing knowledge that has been acquired
continuously by individuals and groups of users and for using this as a means for
semantic identification of various elements to build necessary web applications.
In our architectural implementation we used several toolboxes that are well estab-
lished between academic and industry institutions. For natural language understand-
ing we used IBM NLU toolbox [11, 12] as an example of an agent, which represents
some kind of black box, i.e. we give input for an agent and get the answer without
knowing algorithms and other implementation methods. As supplement to the NLU
agent based on IBM NLU toolbox we build second type of NLU agents that are based
completely on open source projects: general natural language architecture GATE [6]
and JOONE neural networks toolbox [14]. Both technologies combined implements
hybrid neural network NLU agents.
The contribution of this paper is threefold: Firstly, we introduce architecture of NL
dialog for information delivery web portals. Proposed architecture is characterized by
its flexibility to extend and a possibility to build complex information delivery web
portals communicating with machine using natural human language. Secondly, we
investigate two types of agents for distributed NL dialog systems: stationary agents
that communicate by sending messages and mobile agents that move their code and
data to remote hosts and locally solves adequate tasks and returns to their master host
with the solutions. Thirdly, all presented concepts are implemented as Java open
source project. We present discussion about open source projects and importance for
support for such projects from academic environment. Our research shows that until
now there was no open source project in natural language interfaces with information
delivery portals and we think that our project can fulfill such gap.
2 General architecture
The architecture supports coordinated distribution of natural language dialog man-
agement and understanding agents and their integration with information delivery
web portal components. Figure 1 shows basic components of this architecture. Below
follows description of those components and their interconnection.
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Fig. 1. General architecture of integration between information delivery web portal and natural
language agents.
Personal assistant - it is an agent that hides all infrastructures behind information
delivery portal and it's NLP components and uses multi-modal interface to communi-
cate with the user. At presents architectural implementation it is possible to use
HTML input forms with active hyperlinks and in addition forms with standard natural
language dialog interface. At its present implementation personal assistant is far away
from passing Turing test but we see it evolution in the future as becoming more intel-
ligent and with ability to communicate with the user in more like human-expert way.
Currently the most research in personal assistants has been done to help users search
and gather information from unstructured data sources [5, 24] i.e. Internet, papers
collections etc. We think that in the future personal assistant will integrate possibility
to research structured data sources (e.g. databases) with unstructured data sources like
Internet. In current personal assistant edition there are no speech to text converter
(keyboard and mouse are only available options) or speech synthesizer but we think
that such modalities are very important to imitate truly intelligent behavior and we
will consider them in future implementations.
Dialog management – represents two sets of agents: state space dialog manage-
ment agents and form based dialog management agents. The state space dialogue
strategy is a mapping from a set of states (which summarize the entire dialogue) to a
set of actions (such as identification of tables and database queries). The state space
is defined by the collection of all variables that characterize the state of the dialogue
system at a certain point in time. To avoid combinatorial explosion the designer of the
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system must consider how on the one hand to limit the number of variables and the
number of values assigned to variables and on the other hand how to use enough
variables so that to cover particular domain with various dialog flow possible paths.
The set of actions describes what the system can do, i.e. the set of functions the sys-
tem can invoke at any time (e.g. play a certain prompt, query a database, identify the
set of entities, etc.). The strategy is a mapping between the state space and the action
set. For any possible state the strategy prescribes what is the next action to perform.
As a result of the action and its interaction with the external environment (e.g. user,
database, etc.) the system gets some new observations (e.g. database entities, attrib-
utes, etc.). The new observations are registered and modify the state of the system.
This process continues until a final state is reached (e.g. the state with legitimate
SQL, XML script) [20]. The frame-based systems use templates, i.e., collections of
information as a basis for dialogue management. The purpose of the dialogue is to fill
necessary information slots, i.e., to find values for the required variables and then
perform a query or similar operation on the basis of the frame. We use frame-based
approach when we identify entities and we want the user to fill entities attributes. The
dialog manager communicates with two other modules from the system: natural lan-
guage understanding agents to get semantic representation of user utterance (e.g.
identify entities, attributes, relationships between entities i.e. to cover all elements
from entities relationships diagram) and with metadata module where databases
metadata and the information delivery portal knowledge base are stored.
Natural language understanding (NLU) agents – Agent receives text input en-
tered by the user and produces the set of possible actions (e.g. identified entities) with
weights that represents the probability of correct (by means of the user understand-
ing) entity identification. We identify two types of agents by their entities identifica-
tion possibilities: one type of agents uses only current text input without using dialog
history another one uses all information of current dialog state i.e. it uses all history
of current dialog. In our current implementation first type of agents is IBM NLU
toolbox and the second one is hybrid neural network NLU agent. More on the men-
tioned agents implementations see below.
Information delivery portal – is the Internet/Intranet based system for queering
corporate databases, analysing retrieved data and presenting results to the user in
graphical and textual templates. Information delivery web portal can be used without
NLP techniques but in this paper we concentrate on natural language user interface
modalities and their integration with IDP. In our system NLU components are able to
map user utterance to semantic concepts that represents three types of scripts: SQL
script for queering relational databases, simple script to modify HTML document and
script to modify XML document generated by IDP. More on the details see in the
section 3.
Information storage – is a black board for storing various information units that
are used later by other system modules. It is used as the communication media be-
tween agents. In our implementation we used a hash-map as the container to store all
objects by various agents.
Natural language processing agents – implements various elements from natural
language processing area: named entities recognition, co-reference resolution, tokeni-
sation, sentence splitting, gazetteer lookup, etc.
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Learning agents – ensures that the system learns from data presented for learning
as well as from dialogs with users.
Evaluators - are used for the particular type of agents. This means that different
evaluators evaluate different aspects of agents from different viewpoints. For exam-
ple, an evaluator may use the dialogue history to determine which dialogue strategy
should be used (i.e. which kind of dialogue agent should be selected), while another
evaluator may establish which agents is more suited to bring the answer for the user.
Like in [31] our evaluators give scores for agents using a scale between [0,1].
3 Problem Domain - information delivery portal
There are many commercially successful information delivery web portal products
that are available in the market. Figure 2 presents architecture of IDP implemented by
our previous project JMining [15, 16] and many IDP providers implement similar
three tier architecture. We have no intention to describe this architecture in details
and for details about used in this paper IDP JMining we refer to [16] or SAS [29],
Oracle [26], Microsoft [23], Information builders [13], etc. for details of some com-
mercial implementations. Instead, in this section we describe architecture of middle
tier that is based on atomic applications container and it’s interconnections with
agents of natural language dialog management.
As mentioned above one of the biggest problems with NL dialog systems is the
number of states. Reduction of this number is one of the key problems in any dialog-
based systems. And it is why we used JMining IDP and it’s fundamental idea of an
atomic applications container.
Fig. 2. Information delivery portal three-tier architecture.
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IDP JMining is implemented as database and platform independent. Data base sys-
tems are accessed by one of the following protocols: ODBC, JDBC or XML. The
JMining is server-based application written completely in Sun’s Java programming
language. Because the JMining modules are written in Java, they can run on any
server platform that supports a Java Virtual Machine. Data used by the portal: ac-
count credentials, access controls, demographics, personalization parameters, and
configuration information can be stored within an X500 directory services database
accessible through LDAP (Light-weight Directory Access Protocol). All those data
set can be stored into metadata storage of our dialog management system and then
accessed and manipulated by other system of the dialog management. By such ap-
proach we achieve that such users as system administrators can manipulate (retrieve,
modify or create new) some objects stored within LDAP server during NL conversa-
tion with the system. Next we describe mentioned fundamental idea of used IDP,
which is call atomic applications container.
By atomic application we understand the small web application, which contains
following components: database script, user interface HTML page, data representa-
tion script (XML, XSL, etc.) and documentation page (additionally there is connec-
tion to DBMS parameters, name of the application, and parent name of the applica-
tion to organise all atomic applications in one single directory structure). Atomic
application structure in some way resemblance to well knows web applications de-
veloping technologies like Servelets, JavaServer Pages (JSP) and Active Server Pages
(ASP). With such technologies like JSP you can have the full power of general pro-
gramming language like Java. But on the other hand it is unlikely that nonprogram-
mer or person without Java knowledge can hand such technology. On the other hand
by putting more constraint on the web applications structure we achieved that non-
programmer can successfully develop web applications. Surely that doesn’t mean that
no IT skills required. The user of this IDP software actually is the user who previ-
ously used such products like Microsoft Access to develop some local based database
applications. Such user mostly has a good understanding of a database model as well
as some basic SQL knowledge (sure most often that is no need for the user to write
SQL sentences, instead it is done by interactive software wizards).
Atomic application represents one of the basic classes. Object derived form the
class (like a brick in the house) is used to build an enterprise information delivery
web solution. As mentioned above the set of such atomic applications can bring full
portal solution to some business subject. We think that the small number of compo-
nents that can be manipulated to build reliable small web application is attractive
feature for the systems number of control variables is a big constrain. Below we de-
scribe in details these components that can be manipulated by our dialog management
system.
SQL - set of SQL statements that are send to DBMS. There unlimited number of
SQL statements that can be send to SQL server within one request but the last one
must be SELECT type SQL statement. These statements are then executed in the
selected database management system to retrieve information and to display it to the
user through selected reporting template, which can have graphical or textual formats.
Also the users have the choice of modifying these SQL statements as well as report-
ing templates to create their own applications.
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HTML page - HTML document used to set user request parameters which can be
used later to form dynamic SQL statements. Even if the primary intention of this
parameter was to support dynamic SQL statements, it can be used as an independent
HTML page for other web portal need. User has choice to keep parameter values
permanently to the end of Internet session or just to the end of request implementa-
tion by web server.
Type of visualization object - used to choose selected data representation object
from web server (e.g., graphic, bar char, some form of text (XML, HTML, TXT)
layout, etc.).
XML (XSL) - Extensible Markup Language (XML) [34, 35] offers its users many
advantages, including: simplicity, extensibility, and openness. XML as the atomic
application component is used as some script for data visualisation (e.g., it can say
which column forms x or y axis in a graphic or which field represents grouping, total
variables and how they must be presented in the HTML document, etc.). From DBMS
selected data are parsed with statements that are extracted from XML document. If
the data comes from XML document (it is common situation in organizations that
some data now can be received from XML documents instead traditional of DBMS)
document can be used to transform data to HTML format.
The proposed structure of atomic application is optimal in the following way: it
contains the minimum number of components that are required for building complex
web portal. This IDP architecture is robust to some faults done by non-professional
programmers (bugs can effect only one atomic application but the whole system is
unaffected).
4 Dialogue supporting agents
The agent architecture approach to dialogue management makes it possible to use
different dialogue control models, such as state-machines and forms inside the same
system. The combination of different control models is useful when sub-dialogues are
implemented in different ways. For example, most database retrieval tasks can be
modelled efficiently by using forms, while more open-ended dialogues, such as enti-
ties identification in corporate databases may be implemented more efficiently using
state-machines.
Below in the Table 1 we describe state variables and variables values in our dialog
management system for data retrieval, analysis and presentations tasks. Because the
system is user centric orientated the values of some state space variables are nor fixed
as in [21] but has some range of flexibility.
Table 1. The state space variables.
agenda System after the greeting of the user presents agenda. Each
item of the agenda is associated with some number (e.g. 0 –
no agenda item selected, 1 – select already build atomic
application, 2 - manipulate Jmining parameters, 3 – get info
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from metadata storage, 4 – write SQL script, 5- manipulate
LDAP objects, etc.).
objects 0- no object under current dialog state, 1- user is trying to
identify corporate database to which he want establish con-
nection, 2- user name, 3- user password, 4- system is trying
to identify SQL-tables which will be used to query database,
5 – attributes for SQL script data filtering logical sentence
(where), 6 – HTML page attributes (color, title, layout of
input fields and for the future we plan enrich the set of val-
ues), 7 - XML document attributes (data presentation attrib-
utes, data grouping attributes, layout of presented data fields,
template to use).
ob- 1 – if the object under current dialog management has been
jects_confidence established, 0 -if not.
appobject 0- if no atomic application objects, 1-SQL, 2-HTML, 3-
XML, 4- visualization template. This variable is redundant
but we find that it helps control dialog flow.
confidence Like in [21] represents the confidence that the dialog man-
agement system has after obtaining a value for an attribute.
The values 0, 1, and 2 represent the lowest, middle and high-
est confidence values. The values 3 and 4 are set when sys-
tem receives “yes” or “no” after a confirmation question.
value_track Tracks whether the system has obtained a value for the at-
tribute (no=0, yes=1).
number_of_times Tracks the number of times that the dialog manager has
asked the user about the attribute.
Both types of dialog management agents can use all presented variables. Agents
that uses state space representation method uses variable to trigger next action and
move to the next state. Strategies for moving can be established from learning data.
We established 94 dialogs and used reinforcement learning (RL) [21] algorithm to
learn strategies for actions triggering. Form based approach uses variables to query
user for specific variable values. In our current form based dialog management agent
we used VoicXML [32] standard to describe simple control dialog flow based on
variables described above.
Next we present simple dialog between human and our system example end
shortly discuss how the system responds.
Human-Machine multi-modal dialog example
Dialog Description of actions generated by dialog
manager
C: Hello my name is JiMi. I am an At the first you must establish an agenda.
expert in the following areas(the User’s assistant provides a multi-framed
content of the metadata is provided HTML page where user can do database que-
in the form of hyperlinks). The last rying without using dialog or he can use two
time we used Basel 2 project data frames where on the one frame user puts his
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area. What you want to do now? request of the server in the HTML text field
and submits it to the personal assistant which
H: Assessment type. resides on the remote host. In the second
frame personal assistant brings all answers by
all agents participated at the established ses-
sion in the form of formatted HTML page.
Returned page contains direct answer from
dialog manager (it can be retrieved data or
request for some information from the user).
In addition returned answer in the form of
HTML page contains all relevant associations
with the system objects and metadata items, in
the form of HTML hyperlinks and sorted by
their relevance.
C: I have 8 items associated with System identifies the answer with the biggest
Assessment type. Can you chose confident variable value (i.e. user wants to
from the list.(the list is presented in query assessment types classification table)
separate HTML frame). and shows table content in separate frame. In
addition system provides the list of hyperlinks
H: Show me clients with the assess- of other possible actions.
ment type Operational Risk Assess-
ment.
C: You want request from tables NLU agents returns semantic objects: tables -
“Involved Party”, “Assessment”. “Involved Party” and “Assessment”, columns
Filtering will be on table “Assess- - “Assessment Type”, filtering values - “Op-
ment” column “Assessment Type” = erational Risk Assessment”, required object –
“Operational Risk Assessment”? “ SQL script” with confidents level “low”.
Please say “yes” to confirm you Representation agent builds question for the
request. system and tries to ask confirmation.
After the confirmation the system retrieves
H: yes. request results to the separate frame.
C: What do you want do next. NLU agents return semantic objects: HTML
page, action “color”, value – “red”.
H: Change the color to the red. Representation agent changes the colour of
HTML page.
C: What do you want do next. NLU agents return semantic objects: action –
“save atomic application”.
H: Save it. Representation agent sends the message to
information delivery portal to save atomic
application.
User gets the application name.
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5 JMiningDialog architecture
In this section we present more details on our dialog management that we imple-
mented on the general architecture described above. Figure 3 shows basic structure of
the system.
Fig. 3. Architecture of implemented integration between natural language agents and informa-
tion delivery web portal.
In the rest of this section we will concentrate mainly on the natural language un-
derstanding layer. As mentioned above we implemented two types of agents. The first
set of agents utilizes technologies proposed by IBM corporation: Aglets – a frame-
146
work for building mobile (and stationary but we used only mobile concept) agents
and IBM NLU toolbox for natural language applications. The system works as fol-
lows. The master aglet sends mobile agents to remote hosts where mobile agents
gather information stored locally in IBM NLU toolbox internal storage. Each agent
then returns to master agent and store returned results. Results comprises of the list of
action and level of confidence for each action. Each IBM toolbox is presented as a
black box where you put you request and get the answer. Putting in the special IBM
NLU toolbox sentences with associated actions does the learning process. The meth-
ods of IBM NLU statistical processing are not known.
5.2 Mobile agents role
At this part of the section we present our motivation of using mobile agents ap-
proach. Mobile agents are computational software processes capable of roaming wide
area networks (WANs) such as the WWW, interacting with foreign hosts, gathering
information on behalf of its owner and coming ‘back home’ having performed the
duties set by its user. Mobile agents may cooperate or communicate by one agent
making the location of some of its internal objects and methods known to other
agents. By doing this, an agent exchanges data or information with other agents with-
out necessarily giving all its information away [1].
The mobile agents need not be stationary; indeed, the idea is that there are signifi-
cant benefits to be accrued, in certain applications, by putting away static agents in
favour of their mobile counterparts. These benefits are largely non-functional, i.e. we
could do without mobile agents, and only have static ones but the costs of such a
move are high. For example, in our case consider the scenario when mobile agent is
requested to find some knowledge structures related to the words arrangement and
accounts from several users computers.
A static single-agent program would need to request for all files residing on the
remote knowledge sharing host, which may total to several gigabytes. Each of these
actions involves sifting through plenty of extraneous information which could/would
clog up the network.
And consider the alternative. JMiningDialog NLU module encapsulates, user sen-
tences to the entire program within an agent which consumes may be only several
kilobytes which roams the other hosts included in the knowledge sharing network,
arrive safely and queries these hosts locally, and returns ultimately to the home com-
puter. This alternative obviates the high communications costs of shifting, possibly,
gigabytes of information to user local computer. Hence, mobile agents provide a
number of practical, though non-functional, advantages, which escape their static
counterparts. So their motivation include the following anticipated benefits [1].
1. Reduced communication costs: there may be a lot of raw information that
need to be examined to determine their relevance.
2. Limited local resources: the processing power and storage on the local ma-
chine may be very limited (only perhaps for processing and storing the re-
sults of a search), thereby necessitating the use of mobile agents.
147
3. Easier coordination: it may be simpler to coordinate a number of remote and
independent requests and only collate all the results locally.
4. Asynchronous computing: you can ‘set off’ your mobile agents and do
something else and the results will be back in your mailbox, say, at some
later time. They may operate when you are not even connected.
5. A flexible distributed computing architecture: mobile agents provide a
unique distributed computing architecture which functions differently from
the static set-ups. It provides for an innovative way of doing distributed
computation.
We have used aglets mobile agents framework in our implementation. Aglets are
Java objects that can move from one host on the network to another and have all
features mentioned above. More on this techniques can be found in [18].
As the second type of NLU agents we used stationary hybrid neural networks NLU
agents that we build on JOONE neural network toolbox [14] and GATE general natu-
ral language processing toolbox. Gate has been used as NLP pre-processor and the
results converted into binary string have been presented to the neural network. More
on this techniques can be found in [17].
6 Conclusions
We presented agent based natural language dialog and understanding architecture for
data querying from database management systems and presenting it to the user. We
presented reasons why it is important to have in the future, solutions based on mobile
agent approach even if now our data amount can be solved by stationary agents ap-
proach. Our experience showed that even if we have a limited amount of the data for
teaching process, the right strategies for brief dialogs in a narrow domain can be
found. We believe that integration between agents that extract information from
Internet and others unstructured information sources and information delivery soft-
ware brings an optimal solution for companies data analysts.
Our research shows that distributed knowledge architecture is more flexible and
adaptable for such tasks then centralised solutions.
Finally we like to say several remarks concerning an open source projects. In the
past ten years, open source software has become one of the most discussed topics
among software users and practitioners. The increasing interest in open source soft-
ware has been motivated by several factors [9]: 1. The success of products such as
Linux (operating systems),Apache (http servers, etc.) , MySQL (DBMS) , GATE (
NL processing), Weka (machine learning), etc.2. The uneasiness about the Microsoft
or Oracle monopoly in the software industry 3. The increasingly strong opinion that
"classical" approaches to software development are failing to provide a satisfactory
answer to the increasing demand for effective and reliable software applications. At
the initial stage of our project we understood that the code of our project must be the
open source if we want to be successive in promoting our ideas. On the other hand
the success of our project has been determined by the fact that we used three open
sources projects in various areas: GATE in NLP, JOONE in neural networks, Aglets
148
in mobile agent processing. We hope that our paper will stimulate new research in
this software area.
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