Modeling Frameworks for Knowledge Engineering Approaches

Human knowledge was regarded as a transfer process into an applied knowledge base in the early 1980s as the creation of a Knowledge-Based Systems (KBS). The premise behind this transfer was that the KBS-required information already existed and only needed to be gathered and applied. Most of the time, the necessary information was gleaned through talking to professionals about how they handle particular problems. This knowledge was usually put to use in production rules, which were then carried out by a rule interpreter linked to them. Here, we demonstrate a number of new ideas and approaches that have emerged during the last few years. This paper presents MIKE, PROTÉGÉ-II, and Common KADS as three different modeling frameworks that may be used together or separately.

The New Technological Frontiers of CO2 and Hydrogen Transportation Via Pipelines

COVID-19 pandemic is accelerating the transition to decarbonized energy systems. In this context, major Operators and Contractors are bound to promote innovation and technological development. The paper describes how this is being applied to the design of offshore pipelines that are now required to transport not only Hydrocarbons but also anthropogenic CO2 and low-carbon Hydrogen. In order to evaluate all the new technical challenges presented in designing CO2 and H2 pipelines, a state of art has been carried out and is here presented focusing on all the new technical aspects associated to the main disciplines involved in the pipeline network design. Different technical aspects (such as performances evaluation of Equation of State in CCS, Design Standards application to both CO2 and hydrogen pipelines, energy capacity of hydrogen pipelines and others) have been also analytically or numerically addressed simulating credible pipeline operating scenarios. To achieve that, an intensive engineering effort is being dedicated to the development of knowledge, engineering tools, methods and procedures that will be the basis for the execution of future projects concerning H2 and CO2 transportation and storage. A particular focus has been dedicated to offshore pipeline design both for new installation and repurposing of existing ones. In parallel, the cooperation started between Operators, Contractors, Manufacturers, Institutions and Universities, as described in the present paper, acts as a "booster" for the consolidation of knowledge and for the advancing of technology to put in place to overcome those new challenges. Recommendations are made in relation to the gaps found in experimental evidence present in literature and gaps in Standards coverage for the proper pipeline design in those new scenarios.

Research on Knowledge Graphs with Concept Lattice Constraints

The application of knowledge graphs has been restricted in some domains, especially the industrial and academic domains. One of the reasons is that they require a high reliability of knowledge, which cannot be satisfied by the existing knowledge graph research. By comparison, traditional knowledge engineering has a high correctness, but low efficiency is an inevitable drawback. Therefore, it is meaningful to organically connect traditional knowledge engineering and knowledge graphs. Therefore, we propose a theory from Attribute Implications to Knowledge Graphs, named AIs-KG, which can construct knowledge graphs based on implications. The theory connects formal concept analysis and knowledge graphs. We firstly analyze the mutual transformation based on the ideas of symmetry with a strict proof among the attribute implication, the formal context and the concept lattice, which forms the closed cycle between the three. Particularly, we propose an Augment algorithm (IFC-A) to generate the Implication Formal Context through the attribute implication, which can make knowledge more complete. Furthermore, we regard ontology as a bridge to realize the transformation from the concept lattice to the knowledge graph through some mapping methods. We conduct our experiments on the attribute implication from the rule base of an animal recognition expert system to prove the feasibility of our algorithms.

Reducing the Complexity of Casual Representation in Bayesian Belief Network

Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool. Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model. Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN. Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

The Development of a MOOCs Knowledge Repository System Using a Digital Knowledge Engineering Process to Enhance Digital Entrepreneurs’ Competencies

The objectives of this research were as follows: the development of a MOOCs knowledge repository system using a digital knowledge engineering process, and a competency assessment of digital entrepreneurs engaging in a MOOCs knowledge repository system using a digital knowledge engineering process. A total of 30 people were selected as a sampling group (purposive sampling) for this study, these included Small and Medium Enterprises (SMEs) in Bangkok with expertise in Human Performance Technology (HPT), as well as the MOOCs knowledge repository system. The mean, standard deviation, percentage, and a dependent sample t-test were used in the procedure for data analysis. The research findings suggested that: (1) the overall result concerning the development of a MOOCs knowledge repository system using a digital knowledge engineering process was at the highest level (Mean = 4.89, S.D. = 0.31), and (2) the overall result regarding the competencies of digital entrepreneurs after engaging in a MOOCs knowledge repository system using a digital knowledge engineering process passed the 80% rating, according to criteria. Moreover, learners who had undertaken activities through a MOOCs knowledge repository system using a digital knowledge engineering process improved their learning outcomes with a significance level of .05 based on the research hypothesis.

Editorial

La Universidad Autónoma de Bucaramanga y los miembros del Comité Editorial de la Revista Colombiana de Computación, se complacen en presentar esta nueva edición de la Revista con un total de siete artículos de investigación, que corresponden a artículos en versión extendida enviados al 8th International Symposium on Language & Knowledge Engineering – LKE’2021, realizado el 4 de noviembre de 2021 en la Benemérita Universidad Autónoma de Puebla (BUAP). Esperamos que los artículos de esta edición sean del agrado de nuestros lectores, y que contribuyan en el desarrollo de sus investigaciones.