INTRODUCTION TO I-SOAS
Intelligent – Semantic Oriented Agent based Search
Zeeshan Ahmed
Mechanical Engineering Informatics and Virtual Product Development Division (MIVP)
Vienna University of Technology, Getreidemarkt 9/307 1060 Vienna, Austria
Keywords: Information processing, Intelligent graphical user interface, Knowledge management, Product data
management.
Abstract: This research poster paper shortly discuss a newly proposed approach .i.e., I-SOAS towards the problems of
the development of intelligent graphical user interface, structuring of unstructured data, information
management and system data representation.
1 INTRODUCTION
There are currently four intensive existing problems
and very important aspects to be considered before
and during the development of most of the business
oriented product data management, electronic
learning and enterprise data base systems .i.e., User
System Communication, Meta Data Extraction
&Processing, Information Management and Data
Presentation. Focusing on these four aspects, we
have proposed a solution as the extended version of
already proposed and underdevelopment solution
Semantic Oriented Agent Based Search (SOAS) (A.
Zeeshan, 07-2007) (A. Zeeshan, 11-2007) .i.e.,
Intelligent Semantic Oriented Agent Based Search
(I-SOAS).
2 I-SOAS
The proposed architecture of I-SOAS consists of
four main iterative and centrally connected
components .i.e., Intelligent User Interface (IUI),
Information Processing (IP), Data Management
(DM) and Data Representation (DR) as shown in
Figure. 1.
Intelligent User Interface (IUI) is the initial
component of I-SOAS, responsible for the intelligent
user system communication. To implement IUI
system architecture for development, IUI is divided
into two main categories .i.e., Graphical User
Interface and Communication Sources as shown in
Fig.1.
In Communication Sources first the
corresponding user is identified to enable correct the
communication mode. If it is a system then
electronic data communication mode will be enabled
and if it is human then natural language based
communication mode will be enabled. Whereas the
Graphical User Interface is consists of the concept of
intelligent flexible agent based graphical user
interface to intelligently handle the user’s queries,
provide options to easily use, manipulate and
redesign user interface and perform internal
architectural component agent based
communication.
Information Processing (IP) unit is the most
important component of the I-SOAS because the
quality of the performance of I-SOAS majorly
depends on it. IP is divided into five main non-
repetitive iterative sequential steps .i.e., data reading,
tokenization, parsing, semantic modelling and
semantic based query generation as shown in Fig.1.
Data is retrieved, read and organized using Data
Reader, then whole instructions are picked and
tokenized in to the possible number of tokens using
Data Tokenizer, which are then parsed with respect
to the used natural/digital language used in
communication using Data Parser. Tokenized and
semantically evaluated information is used and
reorganized in a Meta data based semantic model by
Semantic Modeller and then in the end Semantic
Based Query Generator will generate a query to
extract desired result.
Data Management is responsible for two
functions .i.e.; Semantic based Query Processing and

Data Management. In Semantic based Query
Processor, first IP generated semantic based query is
parsed then a new SQL query is generated to run
extract the required relevant information data
warehouse. Whereas the job of Data Manager is to
manage the process of SQL query building, data
extraction and creation of new indexes and storage
based on newly retrieved information.
Data Representation is the last component of I-
SOAS consisting of six non- repetitive sequential
steps sub components .i.e., Information Retriever,
Information Reader, Information Tokenizer,
Information Parser, Information Reconstructor and
Presenter. The job of this component to extract,
tokenize, parse and reconstruct processed
information to present the final output to user in
understandable user’s used natural language format.
Figure 1: Intelligent Semantic Oriented Agent Based
Search (I-SOAS) (A. Zeeshan, 2008).
3 CONCLUSIONS
In this poster research paper we have shortly
demonstrated I-SOAS as a potential conceptual
architecture based solution towards above described
four intensive currently existing problems .i.e., User
System Communication, Meta Data Extraction,
Management and Representation. Currently we are
implementing the implementation designs of I-
SOAS. Moreover we are also finalizing the tools and
technologies for the development of I-SOAS
implementation designs. In near future, we hope to
present the implementation designs and prototype
version of I-SOAS.
REFERENCES
A. Zeeshan, D. Gerhard, 07-2007. Personal Assistant
towards Semantic Information Retrieval, In the
proceedings In the proceedings of Fifth International
Workshop on Ontologies and Semantic Web for E-
Learning (SWEL'07), 13th International Conference on
Artificial Intelligence in Education (AIED 2007),
P115, Los Angeles USA, 9–13 July 2007
A. Zeeshan, D. Gerhard, 11-2007. An Agent based
Approach towards Metadata Extraction, Modelling
and Information Retrieval over the Web, In the
proceedings of First International Workshop on
Cultural Heritage on the Semantic Web in conjunction
with the 6th International Semantic Web Conference
and the 2nd Asian Semantic Web Conference 2007,
Busan Korea, 12-15 November 2007
A. Zeeshan, D. Gerhard, 2008. Intelligent Graphical User
Interface, Information Processing and Data
Management based Solution towards Intelligent Data
Manipulation and Representation, In online I*PROMS
2008 conference presentations, 1-14 July, Cardiff
England 2008