B.1.- Scientific and technological objectives and State of the Art
Introduction.Knowledge modelling globally considered are related disciplines that support a
varied range of knowledge management applications. However the applications
in the market are normally partial adaptations of real problems over tools or
even existing systems. That is, rarely these applications have a model either for
the knowledge representation (static knowledge representation) or the
knowledge reasoning/processing) (dynamic representation).
After the evolution and expansion of web applications, models lack delaying the
real expansion of complex web applications. These models are also lacking
because none contemplates the complete bi-directional information flow, that is,
from the knowledge sources to the information requester and viceversa.
This flow apparently solved in many cases breaks completely when we want to
take into account some issues as multimodality, multilinguality and
multiplatform. These “components” once combined define a new framework
where to provide advanced information extraction techniques able to capture
information from a varied range of formats (video, voice, pictures, graphics, text)
and store them in a knowledge base in a unique support able to represent
facts, concepts, data, complex concepts (according the different types of
information) and their relations. This ontological approach that could be called
“Common conceptual Representation” (CCR) must be also expressed in a way
compatible with the existing standards for representing knowledge in a
structures form, as XML and others.
This support could be shown metaphorically speaking as a “3D extension” of
the existing mark-up languages. This CCR can be considered as the formal
support of the future “knowledge repositories” and must be independent of the
input information mode, must support complex operations as knowledge
discovery, data mining, decision support and others. For that we will define the
reasoning model based on a set of primitives that are in charge of accessing
and manipulating the knowledge represented in the CCR and predefined
complex functions for the more standard operations of data mining, text mining
video streaming, picture searching and graphics interpretation and so on and of
course new functions defined by the users. For that we will define a set of
intelligent fuzzy agents that assure that the required information is found and
accurate and it is processed according the requester needs.
This system must support requests in any mode, language and platform for the
defined scenarios. No devices are contemplated but specifications of the
accessing systems able to request information or information operations over
the knowledge systems. These specifications should be the standards for
complex communication devices or systems of the immediate future. For that
we will develop the key “user side” system that is, the MMMM System
(Multimodal, Multiplatform, Multilingual Management System).
The end-user and content provider of our consortium is almost ideal because
the environment will permit to test the system and the real usefulness of the
approach followed. A museum is also representative of new ways of information
exploitation like virtual museums (supported by these systems) and all kind of
added value services for physical or virtual visitors.
Objectives.The core of the proposal is the definition and implementation of a formal and
computational support of heterogeneous knowledge where the information
could be represented in a unique way format independent (multimedia),
language independent (multilingual) and integrating the existing mark up
languages. This ontological approach will contemplate both the storing of
information as data, pictures, sounds, texts, video, (static information) and also
the reasoning/processing models (dynamic models) to manage this information
in an effective way according the needs of users and information
exploiters(1)The conjunction of static and dynamic models give the information
the capacity to talk not about information but Knowledge. This approach will be
the base of the new storing approach called “Knowledge repositories” instead of
“information repositories”. The very scientific challenge of this proposal is the
definition of this computational support for information from many formats and
contents and the reasoning mechanism associated. This is the real concept of
Knowledge base, only when it is independent of the external state of this
knowledge. In fact we can say that the knowledge is the “knowledge repository”.
Out of the system could take as name “information”. For doing it, we will define
the Knowledge representation mechanisms under the name of “Common
Conceptual Representation” (CCR) (2) This CCR must be able to support
discrete and continuous knowledge, precise or imprecise data, that is, it must
incorporate capacity to represent fuzzy information sets and all kind of general
reasoning models and also user needs reasoning models.
However this approach is not complete at computational level if we don’t create
the mechanisms for accessing the CCR. This CCR must support the
maintenance of the system, the automatic access from different sources and
the automated knowledge extraction according also to the different standards
of external representation. The reality of this system will be shown through the
use of a set of primitives that will define in fact a standard for accessing
knowledge repositories.
We are in fact following the classic models of Knowledge Based systems,
where the Knowledge is in the Knowledge Base, and the procedures are
external. However we are adapting for representing knowledge in forms that
never were considered before.
We cannot forget the two mechanisms actuating over a Knowledge Base. The
knowledge acquisition process (extraction of information from sources) and the
knowledge extraction process (extraction of information by the user). This
terminology has been confused the last years but we will define a better
terminological approach also to avoid the classical same thing with different
names.
The characterisation of the information sources is the heterogeneity mainly
represented by information in different formats (text, video, etc..) and in different
languages. Our approach is to represent knowledge, not exactly information
and in this sense we cannot do it depending of these formats or languages. This
supposes that we will design the mechanisms for the automatic knowledge
acquisition process from different types of sources. We cannot think that these
in the future big knowledge bases can be built in a manual way. Information
exploiters have their criteria that can be supported by intelligent agents for
searching the required information and also for processing it (data mining, and
in general the Knowledge discovery associated technologies) (3) We will define
the models mechanisms, technologies and components
(among them the agents) able to find information and to download it into the
CCR. This means that we should also define the standards for automatic
knowledge acquisition from “Knowledge repositories”.
Finally, we must go to the other side, the user side, the information requester.
We find again the user side where we can deal probably with the multilingual
barrier. The Knowledge repository have the language independent approach
but the accessing systems must at least to go beyond these limits.
It is not the scope of the proposal to define and built the perfect access device,
but we must reflects the user needs also in different formats (menus, voice,
written, touch screens…) or types of entries like natural spoken language,
commands, etc, that is their combination. We must define different mechanism
because they define by themselves different approaches for the CCR. Our
approach is to define the specifications of the devices able in the future to make
almost intelligent questions to the knowledge repositories, the automatic
profiling systems, that is agents able to define criteria for accessing and
delivering information according predefined profiles able to adapt them to the
specific users. Adaptive agents mechanisms based on machine learning
technologies . We want to be realistic, our idea is to define the specifications of
the future devices, to make a good visualization of them building a complete
mock up of the devices’ functionalities and tests the user guaranteeing the user
centered approach for this work.
(1) Jesús Cardeñosa ; Luis Iraola ; Edmundo Tovar; (2001) “An intelligent
system for automatic data extraction in E-Commerce applications”.
Pacific Asian Conference on Intelligent Systems. Seoul
(2) J. Cardeñosa. (1995). “Valid: An environment for Validation of KBS”.
Expert Systems with Applications. Vol. 8, nº 3, pp. 323-331.
(3) Jesús Cardeñosa: Luis Iraola; Edmundo Tovar; (2002) “Methods for
Intelligent Information Access. A real experience” Ninth International
Conference on Information Processing and Management of Uncertainty
in Knowledge Based-Systems IPMU2002. Annecy (France). Vol. : II ;
Pag: 1187-1193: ISBN: 2-9516453-2-5