Cesar Sanin

Experience-Based Knowledge Representation: SOEKS

When managers make decisions, they use previous, similar, or equal experiences to help themselves in a new decision-making situation. Thus, keeping record of previous decision events appears to be of the utmost importance as part of the decision making process. For us, every formal decision event has to be collected and stored as experienced knowledge, and any technology able to do this will allow us to improve the decision-making process by reducing decision time, as well as by avoiding duplication in the process. However, one of the most complicated issues about knowledge is its representation. Developing a knowledge structure that stores and administers experience from the day-to-day decision processes would improve decision-making quality and efficiency. We are proposing such a knowledge structure and have named it set of experience knowledge structure. A set of experience knowledge structure (SOEKS) is a combination of organized information obtained from a formal decision event. Fully applied, the set of experience knowledge structure would advance the notion of administering knowledge in the current decision-making environment.

Decisional DNA: A multi-technology shareable knowledge structure for decisional experience

Knowledge representation and engineering techniques are becoming useful and popular components of hybrid integrated systems used to solve complicated practical problems in different disciplines. These techniques offer features such as: learning from experience, handling noisy and incomplete data, helping with decision making, and predicting capabilities. In this paper, we present a multi-domain knowledge representation structure called Decisional DNA that can be implemented and shared for the exploitation of embedded knowledge in multiple technologies. Decisional DNA, as a knowledge representation structure, offers great possibilities on gathering explicit knowledge of formal decision events as well as a tool for decision making processes. Its applicability is shown in this paper when applied to different decisional technologies. The main advantages of using the Decisional DNA rely on: (i) versatility and dynamicity of the knowledge structure, (ii) storage of day-to-day explicit experience in a single structure, (iii) transportability and shareability of the knowledge, and (iv) predicting capabilities based on the collected experience. Thus, after analysis and results, we conclude that the Decisional DNA, as a unique multi-domain structure, can be applied and shared among multiple technologies while enhancing them with predicting capabilities and facilitating knowledge engineering processes inside decision making systems.

Application of a Multi-domain Knowledge Structure: The Decisional DNA

Knowledge engineering techniques are becoming useful and popular components of hybrid integrated systems used to solve complicated practical problems in different disciplines. Knowledge engineering techniques offer features such as: learning from experience, handling noisy and incomplete data, helping with decision making, and predicting. This chapter presents the application of a knowledge structure to different fields of study by constructing Decisional DNA. Decisional DNA, as a knowledge representation structure, offers great possibilities on gathering explicit knowledge of formal decision events as well as a tool for decision making processes. Its versatility is shown in this chapter when applied to decisional domains in finances and energy. The main advantages of using the Decisional DNA rely on: (i) versatility and dynamicity of the knowledge structure, (ii) storage of day-to-day explicit experience in a single structure, (iii) transportability and share ability of the knowledge, and (iv) predicting capabilities based on the collected experience. Thus, after showing the results, we conclude that the Decisional DNA, as a unique structure, can be applied to multi-domain systems while enhancing predicting capabilities and facilitating knowledge engineering processes inside decision making systems.

EXTENDING SET OF EXPERIENCE KNOWLEDGE STRUCTURE INTO A TRANSPORTABLE LANGUAGE eXTENSIBLE MARKUP LANGUAGE

ABSTRACT Some of the most complicated issues about knowledge are its acquisition and its conversion into explicit knowledge. Therefore, among all knowledge forms, storing formal decision events in a knowledge-explicit way is considered an important development. Set of an experience knowledge structure is a vehicle able to acquire explicit knowledge of formal decision events. The purpose of this article is to show an effective form of transformation of a set of experience into a shareable and understandable shape able to travel among different systems. A transportable set of experience could be applied in many technologies, and in consequence, it can advance the notion of administering knowledge in the current decision-making environment.

TOWARDS THE CONSTRUCTION OF DECISIONAL DNA: A SET OF EXPERIENCE KNOWLEDGE STRUCTURE JAVA CLASS WITHIN AN ONTOLOGY SYSTEM

In this article, we present a Java class and an ontology system implementation for the exploitation of embedded experiential knowledge that can be used in several domains. We support this approach on three concepts: Set of Experience Knowledge Structure (SOEKS), a tool able to collect and manage explicit decisional knowledge; Decisional DNA, a structure for decisional knowledge akin to human DNA; and a group of ontologies for ubiquitous applications called SOUPA (Standard Ontology for Ubiquitous and Pervasive Applications). The SOUPA is extended with the Set of Experience Knowledge Structure (SOEKS), enhancing the decisional experience used to assemble Decisional DNA with ontology characteristics for ubiquitous and pervasive applications. Additionally, we propose a SOEKS Java class created for the support and easy implementation of applications using the extended SOUPA which will allows the construction of a Decisional DNA repository useful within many different intelligent systems and platforms.

Virtual Engineering Object / Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0

Abstract This paper reviews the theories, parallels and variances between Virtual Engineering Object (VEO) / Virtual Engineering Process (VEP) and Cyber Physical System (CPS). VEO and VEP is an experience based knowledge representation of engineering objects and processes respectively. Cyber–physical systems (CPSs) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. The analysis of basic concepts and implementation method proves that VEO/VEP is a specialized form of CPS and it can play a vital role in the structure building of Industry 4.0. Integration of the two models may result in intelligent machines and advanced analytics.

REFLEXIVE ONTOLOGIES: ENHANCING ONTOLOGIES WITH SELF-CONTAINED QUERIES

In this article, we introduce the concept of reflexive ontologies. A reflexive ontology is a description of the concepts and relations in a domain with self-contained queries. This approach presents several advantages; (1) the speeding of the query process; (2) the addition of extra knowledge about the domain extending it with queries and answers; and (3), the self-containment of the knowledge structure. We present a framework that can be used to extend any existing ontology with the reflexivity approach. Additionally, as case study, we test the architecture with a previously presented knowledge structure called Set of Experience Knowledge Structure (SOEKS).

Virtual Engineering Object VEO: Toward Experience-Based Design and Manufacturing for Industry 4.0

In this article we propose the concept, its framework, and implementation methodology for Virtual Engineering Objects (VEO). A VEO is the knowledge representation of an engineering object that embodies its associated knowledge and experience. A VEO is capable of adding, storing, improving, and sharing knowledge through experience. Moreover, it is demonstrated that VEO is a specialization of a Cyber-Physical System (CPS). In this article, it is shown through test models how the concept of VEO can be implemented with the Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). The test model confirmed that the concept of VEO is able to capture and reuse the experience of engineering artifacts, which can be beneficial for efficient decision-making in industrial design and manufacturing.

USING SET OF EXPERIENCE KNOWLEDGE STRUCTURE TO EXTEND A RULE SET OF CLINICAL DECISION SUPPORT SYSTEM FOR ALZHEIMER'S DISEASE DIAGNOSIS

In this article we present an experience-based clinical decision support system (CDSS) for the diagnosis of Alzheimers disease, which enables the discovery of new knowledge in the system and the generation of new rules that drive reasoning. In order to evolve an initial set of production rules given by medical experts we make use of the Set of Experience Knowledge Structure (SOEKS). An illustrative case of our system is also presented.

Using XML for implementing set of experience knowledge structure

Among all knowledge forms, storing formal decision events in a knowledge-explicit way becomes an important development. Set of experience knowledge structure can help in achieving this purpose. Set of experience has been shown as a shape able to acquire explicit knowledge of formal decision events. However, to make set of experience knowledge structure practical, it must be worldwide transportable and understandable. The purpose of this paper is to show an effective form of transformation of the set of experience into a shareable and understandable shape able to travel among different systems and technologies.