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2: Explicit vs. Tacit Knowledge 

2: Explicit vs. Tacit Knowledge 

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Thesis
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The objective of the research conducted was to explore how fuzzy ontologies could facilitate the exploitation and mobilisation of tacit knowledge and imprecise data in organisational and operational decision making processes. This thesis shows the benefits of utilizing all the available data one possesses, including imprecise data. By combining the...

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... Tacit requirements are hard to communicate, related to domain of the system, user's own knowledge, and may change during phases of development [19]. Eliciting tacit knowledge is similar to the process of gathering tacit requirements [20]. Figure 1 illustrates that the knowledge is divided into two categories: (i) tacit knowledge, and (ii) explicit knowledge. ...
... Tacit knowledge is regarded as hard to document, which personnel use to perform certain tasks and to take verdicts or decisions [21]. Experts distributed the knowledge of an individual with the extensively agreed division of 90% tacit and 10% explicit [20]. This division of knowledge percentage evidently creates the problem for requirements engineers to elicit the precise requirements from stakeholders. ...
... The analyst needs to be a good listener and keen observer. The analyst's assumption might work as a poison to the system, so analysts need to confirm the requirements from stakeholders by providing the prototypes [20]. ...
Article
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ABSTRACT Effective software requirements elicitation plays a vital role in the success or failure of a project. However, ambiguity in the requirement's statements indicate the presence of a tacit knowledge, which ultimately act as a root cause of critical complications in later stages of software development as user's needs might remain hidden. Additionally, the existence of numerous stakeholders escalates the problem as their perceptions may contrast mainly due to their experiences and roles in a speci c application domain. Hence, witlessness of relevant stakeholder(s) and ambiguous requirements cause the compromise for a product quality. Eventually, it paves the way towards the failure of a project. Furthermore, COVID-19 has affected all walks of life, more speci cally requirements elicitation process as it heavily depends on human-to-human interaction. Motivated by this, current study aims at identifying the requirements elicitation techniques and challenges through a systematic literature review protocol. Furthermore, we have performed an exploratory study to identify the traditional elicitation techniques that can be used speci cally for eliciting the tacit requirements. Additionally, we validate the top 15 critical challenges in a normal and pandemic scenario. To validate the result's authenticity and legitimacy, appropriate statistical tests have been applied on the obtained results. Based on the attained results, it is observed that transfer of tacit knowledge remains a most crucial challenge. To effectively handle the tacit knowledge challenge, we propose a novel conceptual model supporting COVID-19 context. Similarly, we employ expert-validation mechanism for empirically evaluation of the proposed conceptual model. Moreover, the current study provides the guidelines for the practitioners to mitigate the highlighted effects on the requirements elicitation process during current pandemic time. Finally, we believe that proposed conceptual model supports the practitioners in effectively gathering the tacit-knowledge based requirements in the COVID-19 context.
... Such fuzzy membership functions could be obtained by combining the information provided by some experts using aggregation operators extended to interval-valued fuzzy numbers [48]. The authors have developed a wine recommender system and the main reasoning task is the maximum satisfiability degree of a fuzzy concept (an aggregation of criteria) given some individual (a wine) [58]. Type-2 fuzzy ontologies have also been used for intrusion detection in financial institutions [59]. ...
... • Some references have argued that conventional fuzzy ontology reasoners can be used. The authors of [1,2,22] claim that DeLorean reasoner [11] can be used to translate fuzzy type-2 ontologies into classical ontologies and to obtain type-2 fuzzy inference results, and it is claimed in [58] that fuzzyDL reasoner [18] can be used as part of a type-2 wine recommender system. Unfortunately, this is not the case: DeLorean and fuzzyDL cannot currently represent or manage type-2 fuzzy ontologies. ...
Article
In the last years, we are witnessing an increase of real-world applications of fuzzy ontologies. Most fuzzy ontologies are based on type-1 fuzzy logic, and type-2 fuzzy ontologies have not yet received such attention so far. Furthermore, there exists an important gap between type-2 knowledge representation formalisms (type-2 Description Logics) and type-2 fuzzy ontology applications. In this paper, we propose a formal framework for type-2 fuzzy ontologies taking into account the needs of existing applications. Essentially, our approach makes it possible to manage some uncertainty in the fuzzy membership functions used in the fuzzy datatypes and in the degrees of truth of the axioms. We define a type-2 Description Logic, a reasoning algorithm, and give a Fuzzy OWL 2 specification of it.
... Recommendation systems can benefit from fuzzyDL features. In fact, fuzzyDL has been used as part of a system recommending wines [25,64,95]. The use of a fuzzy ontology makes it possible to represent wine attributes such as price, alcohol level, sugar, or acidity in a more convenient way with the help of membership functions. ...
... In [100], a fuzzy keyword ontology serves to annotate and search events in reports by superimposing a fuzzy partonomy 1 on fuzzy classifications. They also have been used for reaching consensus in group decision making [167,218], multi-criteria decision making [202], or extending information queries to allow the search to also cover related or incomplete results [160]. This results on more effective retrieval. ...
Thesis
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Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the specific activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the first time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledge-based architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the first module's output, and executes ontological inference to provide users, activities and their influence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in different environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be sufficiently simple and flexible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the final application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.
... Recommendation systems can benefit from fuzzyDL features. In fact, fuzzyDL has been used as part of a system recommending wines [25,64,95]. The use of a fuzzy ontology makes it possible to represent wine attributes such as price, alcohol level, sugar, or acidity in a more convenient way with the help of membership functions. ...
Conference Paper
In this paper we present fuzzyDL, an expressive fuzzy description logic reasoner.We present its salient features, including some novel concept constructs and queries, and examples of use cases: matchmaking and fuzzy control.
Article
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Posing research questions represents a fundamental step to guide and direct how researchers develop knowledge in research. In design science research (DSR), researchers need to pose research questions to define the scope and the modes of inquiry, characterize the artifacts, and communicate the contributions. Despite the importance of research questions, research provides few guidelines on how to construct suitable DSR research questions. We fill this gap by exploring ways of constructing DSR research questions and analyzing the research questions in a sample of 104 DSR publications. We found that about two-thirds of the analyzed DSR publications actually used research questions to link their problem statements to research approaches and that most questions focused on solving problems. Based on our analysis, we derive a typology of DSR question formulation to provide guidelines and patterns that help researchers formulate research questions when conducting their DSR projects.
Preprint
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Posing research questions is a fundamental step to guide and direct knowledge development in research. In design science research (DSR), research questions are important to define the scope and the modes of inquiry, characterize the artifacts, and communicate the contributions. Despite the importance of research questions, there are few guidelines on how to construct suitable DSR research questions. We fill this gap by exploring ways of constructing DSR research questions and analyzing the research questions in a sample of 104 DSR publications. The results show that about two thirds of the analyzed DSR publications actually use research questions to link their problem statements to research approaches and that most of the questions are aimed at problem-solving. Based on our analysis, we derive a typology of DSR question formulation to provide guidelines and patterns that help researchers formulate research questions during their DSR projects' duration.