8 min read

Blog thumbnail
Published on 11/15/2022
Last updated on 06/18/2024

The Tacit Knowledge Blog Series 5/6


From Tacit Knowledge to Explicit Knowledge

Tribal knowledge is an alternative term widely employed in the industry to denote the tacit knowledge of some senior staff subject matter experts (SME) who have gained profound and critical expertise on some equipment or methods. It represents how people act unconsciously and intuitively and is associated with action since it reflects knowing-how more than knowing-that. The Six Sigma Business Dictionary describes tribal knowledge as "any unwritten information not commonly known by others within a company. This term is used most when referencing information that may need to be known by others to produce a quality product or service". Tacit knowledge is a subset of institutional knowledge, which comprises all documented (explicit) knowledge and undocumented (tacit) knowledge in an organization. It brings decades of hands-on experience without direct instruction, self-study, or help from others. In this sense, it belongs to the company. Still, it is subjective and stored within the heads of the experienced workforce, never transformed into the company knowledge base, and quantifiable only indirectly as a significant loss when senior workers leave or retire or are laid-off and cannot get replaced by new hires with comparable skills [2]. Tacit knowledge is a social attribute in the sense that it is not possible to capture the tacit knowledge —from workers' heads to corporate databases— without considering the social, cultural, legal, and sociological contexts of the data collection and representation.

How to Capture the Tacit Knowledge of Experts

Traditionally, knowledge elicitation has been considered an extraction process to transfer knowledge from one individual to another one using, for example, internal reports or one-on-one mentoring. This approach works well when the knowledge is documented and made explicit. But when much of the knowledge individuals possess is tacit, these simple approaches do not work as expected. Not to say of the barriers and resistance of workers when asked to transfer their knowledge just before leaving the company. That happens because workers are not motivated enough to share their knowledge extra time. They want to get knowledge but not to share, perceive it as an occupational threat, or do not want to communicate their skills to others. Institutional knowledge is created by the continuous social interaction of tacit and explicit knowledge through dialogue and debate. The tacit knowledge is the industrial culture, the occupational traditions, and the cultural values of the workforce that uses, develops, administers, and operates the technology. It is unique to each organization and represents 80-90% of its knowledge. It is also very reductive to think that institutional knowledge belongs exclusively to one group of experts or to a single individual as competence, skills, expertise, or know-how. It is far better to consider tacit knowledge as the result of social accomplishments of constructing and reconstructing new expertise, promoting collaboration towards innovations, and a more robust company culture. This blog describes the two-stage process described in Tacit knowledge elicitation process for industry 4.0 through socialization and externalization using a cognitive pipeline for capturing explicit knowledge complemented by a cooperative role game to capture tacit knowledge. I will focus on problem-solving knowledge, which is about capturing the domain knowledge of workers in an industrial structure. Still, the approach can be easily extended to other working environments if a rich ontology is available.

The Tacit Knowledge Elicitation Process

The knowledge elicitation process [1] consists of a set of methods to elicit the tacit knowledge of a domain expert with a mix of algorithmic techniques and a cooperative game:
  1. A neuro-symbolic cognitive framework to automatically transform heterogeneous textual inputs and domain ontologies into a knowledge graph (KG) for explicit knowledge storage. It is a hybrid neuro-symbolic system where a neural network is focused on sub-symbolic tasks that interact with a symbolic system through a conceptual layer that bridges the gap between symbolic and subsymbolic representation with an intermediate layer for sharing knowledge structures.
  2. A role game model in which human (H) and virtual (V) participants with different skill levels, from experts (E) to apprentices (A), play together to refine the KG using human cognitive processing for implicit knowledge infusion.
The primary motivation is the incompleteness and inconsistency of KGs generated using only explicit data due to the heterogeneity and uncertainty of the textual sources and the evolution and acquisition costs of data and knowledge. To infer and add missing knowledge to the KG or identify erroneous information, a KG refinement stage is necessary to improve the ontology of the KG. In our case, the availability of a richer ontology is conditioned by human experts' supervision and human cognitive processing in the role-playing game. This configuration echoes the renowned Turing's imitation game but with a significant difference. The objective is not to verify if the virtual assistant has acquired some human characteristics but rather to determine the correctness and reliability of the knowledge transferred between human and virtual agents and facilitate as much as possible the translation of SMEs' tacit knowledge into explicit knowledge through socialization and externalization. The knowledge creation framework allows the transfer of insightful knowledge from SMEs to virtual agents in an iterative learning process under the supervision of human experts. This point is particularly critical because only human agents have the wisdom required to know why and to know-how the virtual assistant may or may not be successful at specific tasks. The presence of human experts in the knowledge elicitation process will safeguard against possible collateral effects. In some instances, the tacit knowledge of SMEs can be incorrect or dangerous and shall not be exposed to the virtual assistant during the learning phase. Incorrectly used equipment, incorrectly interpreted results, or procedural shortcuts can impose product and service quality risks and negatively impact employees' and consumers' safety. This aspect requires wisdom.
Clearly, the role game is meant to facilitate the translation of workers' tacit knowledge (somatic tacit knowledge, relational tacit knowledge) into explicit knowledge recreating the social working environment in the organization. The human experts are the necessary society-in-the-loop wisdom agency to ensure that the societal contract is respected and allow that tacit knowledge is efficiently translated into relationships in the Operational KG. Therefore, the knowledge engineers will double-check the practices used by the SMEs and determine which knowledgeable facts can be kept and stored in the KG and which others must be questioned and avoided because they are dangerous, unsafe, or illegal. In this way, human apprentices can eventually interact with the cognitive system without the risk of learning uncontrolled facts that may compromise their learning experience and future productivity in the organization. The overall process for capturing tacit knowledge can be broken down into four phases: Phases I, II, and III capture tacit knowledge into a KG, while Phase IV is an operational setup-up to let the human apprentice interact with the virtual expert (the cognitive assistant). Detailed information on the approach can be found in this paper.

The Nonaka-Takeuchi Model

    The theoretical foundation of the conversion process is made possible by the application of the Nonaka-Takeuchi model, which postulates how knowledge in an organization is created through continuous social interaction of Tacit Knowledge and Explicit Knowledge, coupled with four sequential modes of knowledge conversion:
    • Socialization (from tacit to tacit),
    • Externalization (from tacit to explicit),
    • Combination (from explicit to explicit),
    • Internalization (from explicit to tacit).
    This is the celebrated SECI spiral model of knowledge creation. It perfectly justifies the role game proposed in [1] to convert tacit knowledge into explicit knowledge with human and virtual agents interacting in the knowledge transformation process. The inability to formalize tacit knowledge directly does not exclude the possibility that a virtual agent (a computer system) might perform the same tasks using alternative representations or that tacit knowledge cannot be transferred to a machine. As we have seen in blog#3, ML/DL has enabled computers to acquire tacit knowledge and store knowledge in a bottom-up process.

    What's next?

    In the next blog, I will discuss the Ethical and Societal Implications of Tacit Knowledge.


    1. Fenoglio, E., Kazim, E., Latapie, H. et al. Tacit knowledge elicitation process for industry 4.0. Discov Artif Intell 2, 6 (2022).
    2. Lartey, P. Y. , et al. Importance of Organizational Tacit Knowledge: Barriers to Knowledge Sharing. In M. Mohiuddin, M. A. Moyeen, M. S. A. Azad, & M. S. Ahmed (Eds.), Recent Advances in Knowledge Management
    Personal views and opinions expressed are those of the author.
    Subscribe card background
    Subscribe to
    the Shift!

    Get emerging insights on innovative technology straight to your inbox.

    Unlocking multi-cloud security: Panoptica's graph-based approach

    Discover why security teams rely on Panoptica's graph-based technology to navigate and prioritize risks across multi-cloud landscapes, enhancing accuracy and resilience in safeguarding diverse ecosystems.

    Subscribe to
    the Shift
    emerging insights
    on innovative technology straight to your inbox.

    The Shift keeps you at the forefront of cloud native modern applications, application security, generative AI, quantum computing, and other groundbreaking innovations that are shaping the future of technology.

    Outshift Background