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.     

GENETIC ALGORITHMS FOR DECISIONAL DNA: SOLVING SETS OF EXPERIENCE KNOWLEDGE STRUCTURE

Set of Experience Knowledge Structure (SOE) has been shown as a tool able to collect and manage explicit knowledge of formal decision events. This structure, after being homogenized and mixed, offers a set of possible solutions that, probably, could be improved. The purpose of this article is to show a search process for improved optimal solutions by implementing Evolutionary Algorithms—EA (Genetic Algorithms—GA). Afterward, according to the user's priorities, a unique optimal solution is chosen. Subsequently, such holistic improved SOE is stored as an experienced decision, feeding a knowledge repository of Decisional DNA that would be a useful technology within many different intelligent systems and platforms, including the Knowledge Supply Chain System (KSCS).     

Stream Reasoning to Improve Decision-Making in Cognitive Systems

Abstract

Cognitive Vision Systems have gained a lot of interest from industry and academia recently, due to their potential to revolutionize human life as they are designed to work under complex scenes, adapting to a range of unforeseen situations, changing accordingly to new scenarios and exhibiting prospective behavior. The combination of these properties aims to mimic the human capabilities and create more intelligent and efficient environments. Contextual information plays an important role when the objective is to reason such as humans do, as it can make the difference between achieving a weak, generalized set of outputs and a clear, target and confident understanding of a given situation. Nevertheless, dealing with contextual information still remains a challenge in cognitive systems applications due to the complexity of reasoning about it in real time in a flexible but yet efficient way. In this paper, we enrich a cognitive system with contextual information coming from different sensors and propose the use of stream reasoning to integrate/process all these data in real time, and provide a better understanding of the situation in analysis, therefore improving decision-making. The proposed approach has been applied to a Cognitive Vision System for Hazard Control (CVP-HC) which is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) and has been designed to ensure that workers remain safe and compliant with Health and Safety policy for use of Personal Protective Equipment (PPE).     

A Semiautomatic Experience-Based Tool for Solving Product Innovation Problem

ABSTRACT

In this paper, we present the idea of Smart Innovation Engineering (SIE) System and its implementation methodology. The SIE system is semiautomatic system that helps in carrying the process of product innovation. It collects the experiential knowledge from the formal decisional events. This experiential knowledge is collected from the group of similar products having some common functions and features. The SIE system behaves like a group of experts in its domain as it collects, captures, and stores the experiential knowledge from similar products as well as reuses this experiential knowledge that ultimately enhances the innovation process of manufactured goods. Moreover, with SIE in hand, entrepreneurs and manufacturing organizations will be able to take proper, enhanced decisions and most importantly at appropriate time. The system gains expertise each time a decision is taken and stored in the form of set of experience that can be used in future for similar queries. Implementation of the SIE system using Set of Experience Knowledge Structure and Decisional DNA for case study suggests that the SIE system is capable of capturing and reusing the innovation-related experiences of the manufactured products. The case study confirmed that the SIE system can be beneficial for entrepreneurs and manufacturing organizations for efficient decision making in the product innovation process.     

Experience-Driven Model of Decision-Making Processes in Project Teams

This article presents a model of decision-making processes in project teams. Project teams constitute a specific type of organization appointed to implement a project. Decisions made by project teams result from the methods of project management and best management practices. The authors have undertaken the task of formalizing these processes using the classical method of constructing decision trees. It has been established that this method cannot be fully exploited because the decision-making paths need changing (the decision tree must be dynamically generated after every decision). Therefore, the authors applied a method for collecting experiences, the Set of Experience Knowledge Structure (SOEKS), to support the structure of a decision tree. This method was used to support decision-making processes regarding the selection of methods and technologies of project management and to classify the location decisions resulting from exceeding levels of particulate matter.     

Applying Decisional DNA to Internet of Things: The Concept and Initial Case Study

In this article, we present a novel approach utilizing Decisional DNA to help the Internet of Things capture decisional events and reuse them for decision making in future operations. The Decisional DNA is a domain-independent, standard and flexible knowledge representation structure that allows its domains to acquire, store, and share experiential knowledge and formal decision events in an explicit way. We apply this approach to our current work—SmartBike, a sensor-equipped bicycle built under the concept of Internet of Things. By using Decisional DNA and machine learning algorithms, the SmartBike is able to distinguish its user's patterns based on past riding data. The presented conceptual approach demonstrates how Decisional DNA can be applied to the Internet of Things and bring to them intelligence required by forthcoming semantic networks.     

利用Prolog实现语义网中的知识推理

元数据的交换是实现语义网应用的基础。在语义网的架构中,Ontology语言利用自己的元级描述能力来建立元数据之间的联系,从而实现语义的交换。本文分析了DAML OIL语言的知识描述能力,并对它蕴含的关于类的知识建立了Prolog的推理规则,最后结合XSBProlog推理引擎和InterProlog接口用Java语言实现了对Ontology的推理,从而实现了不同Ontology之间的元数据交换。     

DEVELOPING HETEROGENEOUS SIMILARITY METRICS FOR KNOWLEDGE ADMINISTRATION

Collecting formal decision events in a knowledge-explicit way becomes an important development in terms of knowledge administration. A Set of Experience Knowledge Structure can assist in accomplishing this purpose. However, collecting knowledge comes together with mechanisms of classifying, comparing, and selecting elements among the collected universe, i.e., the universe of formal decision events. Thus, similarity metrics play an important role in knowledge administration. The purpose of this article is to develop heterogeneous similarity metrics for set of experience knowledge structure, and within it, similarity metrics for its components: variables, functions, constraints, and rules. A comparable and classifiable set of experience would make explicit knowledge of formal decision events useful elements in knowledge administration, as well as in multiple technologies.     

Automatic ontology-based knowledge extraction from Web documents

To bring the Semantic Web to life and provide advanced knowledge services, we need efficient ways to access and extract knowledge from Web documents. Although Web page annotations could facilitate such knowledge gathering, annotations are rare and will probably never be rich or detailed enough to cover all the knowledge these documents contain. Manual annotation is impractical and unscalable, and automatic annotation tools remain largely undeveloped. Specialized knowledge services therefore require tools that can search and extract specific knowledge directly from unstructured text on the Web, guided by an ontology that details what type of knowledge to harvest. An ontology uses concepts and relations to classify domain knowledge. Other researchers have used ontologies to support knowledge extraction, but few have explored their full potential in this domain. The paper considers the Artequakt project which links a knowledge extraction tool with an ontology to achieve continuous knowledge support and guide information extraction. The extraction tool searches online documents and extracts knowledge that matches the given classification structure. It provides this knowledge in a machine-readable format that will be automatically maintained in a knowledge base (KB). Knowledge extraction is further enhanced using a lexicon-based term expansion mechanism that provides extended ontology terminology.     

A distributed implementation of a virtual machine for Java

The cluster virtual machine (VM) for Java provides a single system image of a traditional Java Virtual Machine (JVM) while executing in a distributed fashion on the nodes of a cluster. The cluster VM for Java virtualizes the cluster, supporting any pure Java application without requiring that application be tailored specifically for it. The aim of our cluster VM is to obtain improved scalability for a class of Java Server Applications by distributing the application's work among the cluster's computing resources. The implementation of the cluster VM for Java is based on a novel object model which distinguishes between an application's view of an object (e.g. every object is a unique data structure) and its implementation (e.g. objects may have consistent replications on different nodes). This enables us to exploit knowledge on the use of individual objects to improve performance (e.g. using object replications to increase locality of access to objects). We have already completed a prototype that runs pure Java applications on a cluster of NT workstations connected by a Myrinet fast switch. The prototype provides a single system image to applications, distributing the application's threads and objects over the cluster. We used the cluster VM to run, without change, a real Java Server Application containing over 10 Kloc

1 Kloc means Kilo lines of code—used to describe the size of applications in terms of source lines count.

for the source code and achieved high scalability for it on a cluster. We also achieved linear speedup for another application with a large number of independent threads. This paper discusses the architecture and implementation of the cluster VM. It focuses on achieving a single system image for a traditional JVM on a cluster while describing, in short, how we aim to obtain scalability. Copyright © 2001 John Wiley & Sons, Ltd.     

Enterprise computing: the Java factor

The ubiquitous availability of Web browsers on multiple platforms and user familiarity with browser technology provide numerous advantages: a uniform interface; support for multimedia and user interaction and collaboration; a simple communication protocol that has been implemented in all major hardware and software platforms; and support by almost all vendors who package Web engines within their products. Such factors have expedited the implementation of network centric computing as a productive infrastructure for corporate environments. The addition of Java, with its ability to build cross platform application logic into a browser, gives network centric computing the potential to better meet enterprise computing needs. Because it lets users interact with the application on the client rather than the server, Java enables better utilization of both the server and the client's computational capability. Java can also provide sessions state information (for client side session control and resource management) in an otherwise stateless Web world. Besides naturally decentralizing application execution, Java makes it possible to decentralize application deployment. In large enterprises, organization wide applications such as personnel timekeeping or document routing can benefit from Java implementations. One immediate impact is a noticeable reduction of the server load during peak hours. Finally, Java enabled browsers can provide greater functionality than HTML     

Decision making with a fuzzy ontology

Knowledge mobilisation is a transition from the prevailing knowledge management technology that has been widely used in industry for the last 20?years to a new methodology and some innovative methods for knowledge representation, formation and development and for knowledge retrieval and distribution. Knowledge mobilisation aims at coming to terms with some of the problems of knowledge management and at the same time to introduce new theory, new methods and new technology. More precisely, this paper presents an outline of a fuzzy ontology as an enhanced version of classical ontology and demonstrates some advantages for practical decision making. We show that a number of soft computing techniques, e.g. aggregation functions and interval valued fuzzy numbers, will support effective and practical decision making on the basis of the fuzzy ontology. We demonstrate the knowledge mobilisation methods with the construction of a support system for finding the best available wine for a number of wine drinking occasions using a fuzzy wine ontology and fuzzy reasoning methods; the support system has been implemented for a Nokia N900 smart phone.     

Rough Mereological Calculi of Granules: A Rough Set Approach To Computation

Rough Mereology is a paradigm allowing for a synthesis of main ideas of two potent paradigms for reasoning under uncertainty: Fuzzy Set Theory and Rough Set Theory. Approximate reasoning is based in this paradigm on the predicate of being a part to a degree . We present applications of Rough Mereology to the important theoretical idea put forth by Lotfi Zadeh (1996, 1997), i.e., Granularity of Knowledge: We define granules of knowledge by means of the operator of mereological class and we extend the idea of a granule over complex objects like decision rules as well as decision algorithms. We apply these notions and methods in the distributed environment discussing complex problems of knowledge and granule fusion. We express the mechanism of complex granule formation by means of a formal grammar called Synthesis Grammar defined over granules of knowledge, granules of classifying rules, or over granules of classifying algorithms. We finally propose hybrid rough-neural schemes bridging rough and neural computations.     

A formally specified ontology management API as a registry for ubiquitous computing systems

Recently, several standards have emerged for ontology markup languages that can be used to formalize all kinds of knowledge. However, there are no widely accepted standards yet that define APIs to manage ontological data. Processing ontological information still suffers from the heterogeneity imposed by the plethora of available ontology management systems. Moreover, ubiquitous computing environments usually comprise software components written in a variety of different programming languages, which makes it particularly difficult to establish a common ontology management API with programming language agnostic semantics. We implemented an ontological Knowledge Base Server, which can expose the functionality of arbitrary off-the-shelf ontology management systems via a formally specified and well defined API. A case study was carried out in order to demonstrate the feasibility of our approach to use a formally specified ontology management API to implement a registry for ubiquitous computing systems.     

通用知识网格下以用户为中心的数据挖掘本体研究

分布异构的海量数据挖掘是数据挖掘领域急待解决的课题，通用知识网格（UKB）架构模型用于在网格环境下创建大规模的分布式知识发现和知识集成系统。本体服务器是整个架构的核心模块，负责本体的管理和查询。数据挖掘本体服务是本体服务器提供的主要服务。本文主要介绍通用知识网格下以用户为中心的数据挖掘本体的设计和OWL实现。数据挖掘本体可满足各种不同领域、不同层次用户的知识发现服务，使系统具有开放性、可扩展性和高用户可用性。还介绍了一个反洗钱领域数据挖掘解决方案实例。     

Experience-Oriented Intelligence for Internet of Things

ABSTRACT

The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade.The main reason behind this interest is the capabilities of the IoT for seamlessly integrating classical networks and networked objects, and hence allowing people to create an intelligent environment based on this powerful integration. However, how to extract useful information from data produced by IoT and facilitate standard knowledge sharing among different IoT systems are still open issues to be addressed. In this paper, we propose a novel approach, the Experience-Oriented Smart Things (EOST), that utilizes deep learning and knowledge representation concept called Decisional DNA to help IoT systems acquire, represent, and store knowledge, as well as share it amid various domains where it can be required to support decisions. Decisional DNA motivation stems from the role of deoxyribonucleic acid (DNA) in storing and sharing information and knowledge. We demonstrate our approach in a set of experiments, in which the IoT systems use knowledge gained from past experience to make decisions and predictions. The presented initial results show that the EOST is a very promising approach for knowledge capture, representation, sharing, and reusing in IoT systems.     

Automatic extraction of OWL ontologies from UML class diagrams: a semantics-preserving approach

Full implementation of the Semantic Web requires widespread availability of OWL ontologies. Manual ontology development using current OWL editors remains a tedious and cumbersome task that requires significant understanding of the new ontology language and can easily result in a knowledge acquisition bottleneck. On the other hand, abundant domain knowledge has been specified by existing database schemata such as UML class diagrams. Thus developing an automatic tool for extracting OWL ontologies from existing UML class diagrams is helpful to Web ontology development. In this paper we propose an automatic, semantics-preserving approach for extracting OWL ontologies from existing UML class diagrams. This approach establishes a precise conceptual correspondence between UML and OWL through a semantics-preserving schema translation algorithm. The experiments with our implemented prototype tool, UML2OWL, show that the proposed approach is effective and a fully automatic ontology extraction is achievable. The proposed approach and tool will facilitate the development of Web ontologies and the realization of semantic interoperations between existing Web database applications and the Semantic Web.     

Using OWL ontologies for adaptive patient information modelling and preoperative clinical decision support

We here present our research and experience regarding the design and implementation of a knowledge-based preoperative assessment decision support system. We discuss generic design considerations as well as the practical system implementation. We developed the system using semantic web technology, including modular ontologies developed in the OWL web ontology language, the OWL Java application programming interface and an automated logic reasoner. We discuss how the system enables to tailor patient information collection according to personalized medical context. The use of ontologies at the core of the system’s architecture permits to efficiently manage a vast repository of preoperative assessment domain knowledge, including classification of surgical procedures, classification of morbidities and guidelines for routine preoperative tests. Logical inference on the domain knowledge according to individual patient’s medical context enables personalized patients’ reports consisting of a risk assessment and clinical recommendations such as relevant preoperative tests.     

Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA): Past,Present and Future

This article reviews research work on set of experience knowledge structure (SOEKS)-decisional DNA (DDNA) done in the past, ongoing, and planned for the future. Firstly, the concept of the knowledge representation technique of SOEKS-DDNA is discussed, and then an attempt is made to organize the past research related with it in chronological order. This work focuses on the review on SOEKS-DDNA, its application in different domains, the various implementation platforms, as well as its benefits and its limitations. The second part of this article provides an idea of the SOEKS-DDNA-related research endeavors currently carried out by us and the last part is a sneak peek into our planned future work.