Dynamic Knowledge Capitalization through Annotation among Economic Intelligence Actors in a Collaborative Environment

The shift from industrial economy to knowledge economy in today's world has revolutionalized strategic planning in organizations as well as their problem solving approaches. The point of focus today is knowledge and service production with more emphasis been laid on knowledge capital. Many organizations are investing on tools that facilitate knowledge sharing among their employees and they are as well promoting and encouraging collaboration among their staff in order to build the organization's knowledge capital with the ultimate goal of creating a lasting competitive advantage for their organizations. One of the current leading approaches used for solving organization's decision problem is the Economic Intelligence (EI) approach which involves interactions among various actors called EI actors. These actors collaborate to ensure the overall success of the decision problem solving process. In the course of the collaboration, the actors express knowledge which could be capitalized for future reuse. In this paper, we propose in the first place, an annotation model for knowledge elicitation among EI actors. Because of the need to build a knowledge capital, we also propose a dynamic knowledge capitalisation approach for managing knowledge produced by the actors. Finally, the need to manage the interactions and the interdependencies among collaborating EI actors, led to our third proposition which constitute an awareness mechanism for group work management.Annotation, knowledge representation, knowledge capitalisation, economic intelligence, collaboration, knowledge exploitation, group awareness

CO-ADMIRE : un système contextuel de recherche d'information

International audienceCette communication s'inscrit dans le cadre d'une étude sur le processus de recherche d'information multimédia pour la compréhension et la clarification de la situation informationnel et l'aide à la prise de décision. L'objectif de notre communication est de présenter le prototype de notre système Co-ADMIRE: COntext bAseD Multimedia Information Retrieval SystEm, sa modélisation et son architecture. Ce prototype de système contextuel de recherche d'information multimédia a pour objectif d'assister l'utilisateur lors de sa recherche d'information, en l'aidant à définir son besoin informationnel et à le traduire ensuite sous forme de requête. Il implémente notamment un procédé d'annotation que nous explorons dans cette communication. Ce procédé repose sur les connaissances et les compétences de l'utilisateur. Une étude de besoins informationnels des utilisateurs a été nécessaire avant la modélisation du système. Cette modélisation repose sur l'étude de l'information multimédia, de l'utilisateur et du contexte d'utilisation de l'information et des interactions de l'utilisateur avec le système

Dynamic Knowledge Capitalization through Annotation among Economic Intelligence Actors in a Collaborative Environment

International audienceThe shift from industrial economy to knowledge economy in today's world has revolutionalized strategic planning in organizations as well as their problem solving approaches. The point of focus today is knowledge and service production with more emphasis been laid on knowledge capital. Many organizations are investing on tools that facilitate knowledge sharing among their employees and they are as well promoting and encouraging collaboration among their staff in order to build the organization's knowledge capital with the ultimate goal of creating a lasting competitive advantage for their organizations. One of the current leading approaches used for solving organization's decision problem is the Economic Intelligence (EI) approach which involves interactions among various actors called EI actors. These actors collaborate to ensure the overall success of the decision problem solving process. In the course of the collaboration, the actors express knowledge which could be capitalized for future reuse. In this paper, we propose in the first place, an annotation model for knowledge elicitation among EI actors. Because of the need to build a knowledge capital, we also propose a dynamic knowledge capitalisation approach for managing knowledge produced by the actors. Finally, the need to manage the interactions and the interdependencies among collaborating EI actors, led to our third proposition which constitute an awareness mechanism for group work management

Embedded fuzzontological model for document interpretation in attribute-value-pair annotation in economic intelligent systems

Two popular views about the concept of information are the "information as a process" and "information as a product". While there has been much research about the two paradigms, one recurrent question has been where is the place of interpretation in information utilization? A growing trend is the concept of annotation which encourages individual interpretation on a subject of interest and keeps such information for future use. The problem of interpretation is not new, as it was clearly pointed out in the infological equation. With several attempt at resolving this imbroglio in place, we are of the opinion that misinterpretation results from differences in individual knowledge and cognitive ability amongst other factors. Consequently, we developed an attribute-value-pair (AVP) document representation metadata to interface directly with the ontology domain, and extend interpretation via fuzzy inference system

Dynamic Knowledge Capitalization through Annotation among Economic Intelligence Actors in a Collaborative Environment

International audienceThe shift from industrial economy to knowledge economy in today's world has revolutionalized strategic planning in organizations as well as their problem solving approaches. The point of focus today is knowledge and service production with more emphasis been laid on knowledge capital. Many organizations are investing on tools that facilitate knowledge sharing among their employees and they are as well promoting and encouraging collaboration among their staff in order to build the organization's knowledge capital with the ultimate goal of creating a lasting competitive advantage for their organizations. One of the current leading approaches used for solving organization's decision problem is the Economic Intelligence (EI) approach which involves interactions among various actors called EI actors. These actors collaborate to ensure the overall success of the decision problem solving process. In the course of the collaboration, the actors express knowledge which could be capitalized for future reuse. In this paper, we propose in the first place, an annotation model for knowledge elicitation among EI actors. Because of the need to build a knowledge capital, we also propose a dynamic knowledge capitalisation approach for managing knowledge produced by the actors. Finally, the need to manage the interactions and the interdependencies among collaborating EI actors, led to our third proposition which constitute an awareness mechanism for group work management

Knowledge Creation and Enhancement through Collaborative Information Retrieval

International audienceThe final goal of Information Retrieval (IR) is knowledge production. However, it has been argued that knowledge production is not an individual effort but a collaborative effort. Collaboration in information retrieval is geared towards knowledge sharing and creation of new knowledge by users. While many attempts have been made in expert systems for capturing knowledge and reusing these knowledge, we discovered that their response is based on the pattern coded in the system whereas human beings, influenced by so many factors, possess some tacit knowledge which they themselves may not be aware of until they are faced with problems that will steer up that knowledge in them. This paper aims at explaining how users' tacit knowledge can be made explicit through collaboration during IR by allowing synchronous human to human interaction in information retrieval through the mediation of a Collaborative Information Retrieval System (CIRS) and through annotation of the objects in the CIRS thus enhancing knowledge creation and structuring

AMTEA: Tool for Creating and Exploiting Annotations in the Context of Economic Intelligence (Competitive Intelligence)

ISBN : 978-1-4244-8097-5International audienceThis paper presents annotation specification as attribute-value pair for representing and capturing strategic information for solving decision problems in the context of Economic Intelligence (also referred to as competitive intelligence). The aim of using this approach is to facilitate information reuse for similar problems. While most of available annotation tools allow annotator to add annotation to document of interest as atomic object, it is believed that annotation represented as an attribute-value pair will offer enhanced interpretation functionalities. Annotation specification integrating the identified needs of the annotator as well as partial implementation of our prototype called AMTEA (Annotation Model and Tools for Economic Actors) is presented. The system is designed for annotation creation and exploitation taking into consideration annotations made, decision problem being solved, and user's profile

Deep learning model for preicting multistage cyber attacks

The prediction of cyberattacks has been a major concern in cybersecurity. This is due to the huge financial and resource losses incurred by organisations after a cyberattack. The emergence of new applications and disruptive technologies has come with new vulnerabilities, most of which are novel – with no immediate remediation available. Recent attacks signatures are becoming evasive, deploying very complex techniques and algorithms to infiltrate a network, leading to unauthorized access and modification of system parameters and classified data. Although there exists several approaches to mitigating attacks, challenges of using known attack signatures and modeled behavioural profiles of network environments still linger. Consequently, this paper discusses the use of unsupervised statistical and supervised deep learning techniques to predict attacks by mapping hyper-alerts to class labels of attacks. This enhances the processes of feature extraction and transformation, as a means of giving structured interpretation of the dynamic profiles of a network.Keywords: Alert correlation, Cyberattack prediction, Cybersecurity, Deep learning, Cyberattacks, Supervised and Unsupervised LearningVol. 26 No 1, June 201