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.

💡 Research Summary

The paper addresses the challenge of turning the tacit and explicit knowledge generated during Economic Intelligence (EI) processes into a reusable capital asset. It begins by outlining the EI workflow, which consists of five phases—translation of a decision problem, information search and retrieval, analysis, decision making, and protection of information patrimony—and identifies the three principal actors: the Decision Maker (DM), the EI Project Coordinator, and the Information Watcher. These actors collaborate to define problems, gather and validate information, and produce actionable knowledge.

Recognizing that traditional annotation tools are limited to predefined attributes and cannot easily adapt to evolving user needs, the authors propose an annotation model based on user‑defined attribute‑value pairs. This model allows annotations to be attached at any granularity (document, section, paragraph, sentence, word, image, etc.) and to be anchored either inline or as an overlay, depending on access rights. The model satisfies six functional requirements: structure, communication, anchor, scalability, reusability, and granularity. By treating each annotation as a piece of metadata, the system supports later data mining, restructuring, and knowledge elicitation without requiring schema changes.

Building on this flexible annotation layer, the authors introduce a Dynamic Capitalization (DC) framework for managing Knowledge Resources (KR). The DC cycle comprises five interrelated phases:

1. Knowledge Elicitation, Acquisition, and Validation – Actors declare KR according to their roles and contexts; subsequent communication is captured as annotations. A timestamp is attached to each entry, preserving temporal versions and preventing overwriting. The acquisition method uses a case‑based algorithm where each actor’s role serves as a case.

2. Knowledge Representation – KR are modeled using a generic conceptual schema that defines properties (e.g., decision objectives, risk factors, information sources) and relationships among them.

3. Storage with Temporal Attributes – All KR, together with their timestamps, are stored in a Knowledge Repository, enabling non‑volatile, time‑aware retrieval.

4. Exploitation of Knowledge – The EQUA2te model (Explore, Query, Analyze, Annotate) guides users in formulating queries based on repository attributes. Retrieval relies on case‑based reasoning, and retrieved KR can be re‑annotated to fit new decision contexts.

5. Validation and Evaluation – Continuous annotation‑based verification among actors ensures the reliability and consensus of the capitalized knowledge.

To support collaborative work, the paper adds a group‑awareness mechanism. It provides both synchronous (real‑time) and asynchronous (notification‑driven) annotation capabilities, integrates user profiles for access control, and visualizes annotation flows so participants can see who contributed what and when. This enhances transparency, coordination, and trust among EI actors.

The authors validate their proposals through two prototype systems. The first is deployed in a corporate EI project where decision makers declare problems, information watchers annotate and validate sources, and the coordinator oversees workflow; the system automatically captures KR and makes them searchable for future projects. The second prototype is an academic collaboration platform where multi‑disciplinary teams co‑author papers, annotate drafts, and accumulate KR that later support literature reviews and new research proposals. Both implementations demonstrate reduced decision‑making time, improved knowledge reuse, and heightened situational awareness among participants.

In conclusion, the paper contributes a novel, annotation‑centric approach to knowledge capitalization in EI contexts, coupling flexible user‑defined metadata with a dynamic, time‑aware lifecycle. It offers a practical roadmap for organizations seeking to transform collaborative knowledge creation into a strategic asset, and outlines future work on automated semantic extraction, machine‑learning‑driven annotation recommendation, and scalability testing in large‑scale enterprises.