Knowware: A Commodity Form of Knowledge

Expert system knowledge represents expertise obtained through formal education, training, and/or experience. Formal education provides deep knowledge of a particular domain; experience and training result in heuristic knowledge. A knowledge base defines the range of information and understanding with which the system is capable of dealing; therefore, its information must be structured and filed for ready access. The objective of this symposium is to address the challenges associated with establishment of valid expert system knowledge, specifically, knowledge to be used by expert system shells. As expert system knowledge is obtained, structured, and stored, it is formulated. In this symposium, knowledge formulation is addressed as a three-phase process: knowledge acquisition, the mechanics associated with structuring knowledge, and knowledge porting. Knowledge acquisition is the process of extracting expertise from a domain expert. Expertise may be collected through a series of interviews between the expert and a knowledge engineer or through sessions the expert holds with an automated knowledge acquisition tool. Thus, the ultimate outcome of knowledge acquisition is a collection of raw knowledge data. The following human factors issues become apparent: documenting mental models (where mental models are the expert's conceptualization of a problem), recording cognitive problem-solving strategies, and specifying an appropriate interface between the domain expert and the acquisition methodology. The knowledge structuring process involves the refinement of raw knowledge data, where knowledge is organized and assigned a semantic structure. One issue that must be considered is how to interpret knowledge data such that formal definitions, logical relationships, and facts can be established. Finally, formulation involves knowledge porting, that is, the movement of an expert system shell's knowledge base to various other shells. The outcome of this process is a portable knowledge base, where the challenges lie in maintaining consistent knowledge, understanding the constraints inherent to a shell (the shell's ability to incorporate all relevant knowledge), and designing an acceptable user-expert system interface. The fundamental component of any expert system is its knowledge base. The issues to be presented in this symposium are important because they address three processes that are critical to the development of a knowledge base. In addition to presenting computer science challenges, knowledge base formulation also presents human factors challenges, for example, understanding cognitive problem-solving processes, representing uncertain information, and defining human-expert system interface problems. This symposium will provide a forum for discussion of both types of challenges.

The development of a computer-aided design (CAD) system for neutralizing emissions from energy source enterprises and production enterprises requires the creation of an appropriate knowledge base. This article discusses the construction of such a knowledge base and its application in CAD system for neutralizing emissions from energy source enterprises and production enterprises. Using development tools such as Protégé (Java), PyCharm (Python) and Navicat (MySQL), a knowledge base and a client platform for the CAD system for neutralizing emissions from energy source enterprises and production enterprises were developed. A key technical framework for building the CAD system for neutralizing emissions from energy source enterprises and production enterprises knowledge base is developed, covering the main aspects of knowledge acquisition, representation and dynamic updating. The accuracy and efficiency of knowledge retrieval were significantly enhanced through ontological modeling, which enabled the analysis of associations within diverse datasets. During the development of the knowledge base, logical inference rules were created and transformed into a knowledge graph model. This approach, combined with the dynamic characteristics of the knowledge graph, facilitates continuous updating of the knowledge base. The practical value of applying the knowledge base in complex scenarios was validated through its implementation in the system for neutralizing emissions from energy source enterprises and production enterprises. The developed knowledge base is an integral component of the CAD system for neutralizing emissions from energy source enterprises and production enterprises, aimed at addressing emission-related challenges. The CAD system for neutralizing emissions from energy source enterprises and production enterprises knowledge base solves the key problems of knowledge fusion, data migration and reasoning validity, and provides theoretical support and technical path for building an intelligent and scalable CAD system for neutralizing emissions from energy source enterprises and production enterprises knowledge service system.

Evolution of Knowledge Bases (KBs) consists of incorporating new information in an existing KB. Previous studies assume that the new information should be fully trusted and thus completely incorporated in the old knowledge. We suggest a setting where the new knowledge can be partially trusted and develop model-based approaches (MBAs) to KB evolution that rely on this assumption. Under MBAs the result of evolution is a set of interpretations and thus two core problems for MBAs are closure, i.e., whether evolution result can be axiomatised with a KB, and approximation, i.e., whether it can be (maximally) approximated with a KB. We show that DL-Lite is not closed under a wide range of trust-sensitive MBAs. We introduce a notion of s-approximation that improves the previously proposed approximations and show how to compute it for various trust-sensitive MBAs.

The paper presents general features of Knowledge Base building for Nature-Technogenic Control System as innovation product. Interfaces between experts, users and Knowledge Base developers are shown. The Knowledge Base involves databases, monographs, papers and other information sources since 1996 up to date. The Knowledge Base is designed for enterprises working in Ecological Safety field.

: This effort focused on the development and integration of large knowledge bases and knowledge based tools. The project significantly advanced the state of the art in ontology and knowledge base development. Of particular scientific interest was a set of formal experiments that were conducted and described in technical reports and academic publications that quantified the value of knowledge base technology. The project also laid the groundwork for further efforts in ontology development and standardization. The Teknowledge integration approach was integration of components using a single large ontology based on Cyc. A significant result was the development of a version of Cyc for government distribution, which contained an Integrated Knowledge Base (IKB) and Integrated Development Environment (IDE). This software built on Cyc and included the ontology content developed during the HPKB project by our team. The HPKB project was structured to have several Challenge Problems that motivated and evaluate research progress. The Crisis Management Challenge Problem was focused on knowledge-based inference and the domain was reasoning about international crisis situations. The Battlespace Challenge Problem went through several iterations but wound up focusing on planning for battlefield workarounds and critiquing Army Courses of Action (COAs). The latter technology focus was on an integration of knowledge based sketching, restricted English to logic translation, problem solving, and knowledge-based inference.

Augmented Reality is a technology that offers enormous potential in the industry. Due to a lack of expertise, however, companies are facing various challenges in exploiting this potential. This includes the demand-oriented configuration of the AR system. Depending on the planned use case and company-specific influencing factors and general conditions, suitable AR devices are to be identified and functionalities need to be purposefully selected. The necessary knowledge is implicitly available in numerous sources and difficult to access for companies. In the scope of this work, therefore, a machine-readable knowledge base is developed, in which the knowledge relevant for the AR system configuration is consolidated and formalized. Four aspects of knowledge are considered: The AR system structure including technical and functional components; Potential influences through ambient factors; Experience and application knowledge; The specification of specific AR devices. The knowledge base is realized based on the Web Ontology Language (OWL) and thus enables a digital and partially automated processing of the knowledge using software tools and algorithms. In combination with Artificial Intelligence solutions, the developed knowledge base can be transferred to other systems in the future and provide a powerful tool for system configuration.

Several papers have addressed the theory foundation of DSR. While researchers usually emphasize that the existence of such a knowledge base (KB) is essential for high quality design science research (DSR), opinions depart what kind of knowledge comprises such a knowledge base and which qualitative requirements apply regarding the knowledge leveraged. Some researchers demand that DSR is based on descriptive formal theories, while other scholars extend the width of the knowledge base also to unverified empirical evidence, conceptual knowledge and prescriptive knowledge. In order to provide some guidance for practical DSR, we apply literature review methodology on recent DSR articles to determine the common practice regarding the use and development of knowledge bases in previous projects. Based on this investigation, we discuss current issues, derive implications for future research and suggest measures to strengthen the role of the knowledge base in DSR.

The World Wide Web presents new challenges for the development of legal inferencing systems. This project starts by implementing via the web the inferencing component of the previous ‘DataLex’ research, through the wysh interface. This provides one solution to the problem of maintaining state in web interactions, while only requiring standard HTML and CGI facilities. Wysh knowledge bases can interact with other wysh knowledge bases located anywhere on the web, creating opportunities for new forms of ‘cooperative’ knowledge base development. Automated and customized means of linking knowledge bases and inferencing dialogues to a large dynamic ‘real world’ legal information system (the Australasian Legal Information Institute) are provided, giving one means of overcoming the ‘knowledge base lag’. The project’s development tools are available to any legal knowledge base developer via the web.This was the first paper to explain Austlii's approach to implementing rule-based reasoning systems in the context of free access to law legal information institutes.

As organizations have become increasingly effective at reducing costs and improving efficiency in their operations, their attention has shifted to revenue growth. Tapping the collective creativity and know‐how of employees is emerging as one of the best ways to anticipate customer needs and grow new markets. Typically, when companies set out to create knowledge bases, the first issue that they tackle is technology. In many cases, resources and patience are exhausted by the time they have resolved technology issues. This paper reviews Arthur Andersen’s experiences in developing knowledge bases ‐ focusing on content; knowledge sharing; and culture change.

Formal methods are a usual means to avoid errors or bugs in the development, adjustment and maintenance of both software and knowledge bases. This chapter provides a formal method to refine a knowledge base based on insides about its correctness derived from its use in practice. The objective of this refinement technique is to overcome particular invalidities revealed by the application of a case-oriented validation technology, i.e. it is some kind of “learning by examples”. Approaches from AI or Data Mining to solve such problems are often not useful for a system refinement that aims at is an appropriate modeling of the domain knowledge in way humans would express that, too. Moreover, they often lead to a knowledge base which is difficult to interpret, because it is too far from a natural way to express domain knowledge. The refinement process presented here is characterized by (1) using human expertise that also is a product of the validation technique and (2) keeping as much as possible of the original humanmade knowledge base. At least the second principle is pretty much adopted from Software Engineering. This chapter provides a brief introduction to AI rule base refinement approaches so far as well as an introduction to a validation and refinement framework for rulebased systems. It also states some basic principles for system refinement, which are adopted from Software Engineering. The next section introduces a refinement approach based on these principles. Moreover, it considers this approach from the perspective of the principles. Finally, some more general conclusions for the development, employment, and refinement of complex systems are drawn. The developed technology covers five steps: (1) test case generation, (2) test case experimentation, (3) evaluation, (4) validity assessment, and (5) system refinement. These steps can be performed iteratively, where the process can be conducted again after the improvements have been made.

The use and impact of a community-developed knowledge base on the decisionmaking process of an actual school closing is discussed. This was not an academic exercise but rather grew out of the needs and frustrations of a highly polarized community. The study is one of second-order cybernetics, utilizing citizens of the community as participant-observers or co-researchers in the process. In so doing the knowledge base development and its community use are related to the issues of holistic communication, citizens' use of technology, and conceptual model building.

The role of prior knowledge in elaboration strategies was examined in 3 experiments. University students were presented with material that accessed either a highly developed or a less developed knowledge base and instructed to answer why questions (elaborative interrogation), create interactive images (imagery), create interactive images using keywords (keyword), or read the information aloud (repetition). Across all 3 experiments, elaborative interrogation was most effective when learners were able to draw on a rich knowledge base. However, when the knowledge base was low, imagery-based strategies were more potent than elaborative interrogation. These results suggest that creating mental images encourages the processing of both relations and distinctions to a greater extent than elaborative interrogation when the learners' background knowledge is limited. Many studies have demonstrated that new information can be made more memorable if learners create meaningful elaborations (e.g., Stein, Morris, & Bransford, 1978). Presumably, when creating meaningful associations, learners search their available knowledge and use that information to connect novel to-be-learned materials. Elaborative interrogation is one higher order questioning strategy that is thought to enhance the learning of facts by encouraging students to connect novel information to their own richly developed knowledge base. Instead of simply presenting students with facts to learn, students are asked to answer a why question for each fact (e.g., Why would that fact be true?). A series of studies has demonstrated the potency of this strategy for both child and adult populations across a variety of fact-learning tasks (see Pressley et al., 1992, for a review). In this article we investigate how elaborative interrogation operates to achieve such strong learning effects.

The development of the IShopping Agent's knowledge base, the intelligent shopping agent which is named in our research project and proposed the IShopping Agent's knowledge base explicitly takes into account the fact that customers do not adopt the same decision style and that the marketing strategy mix applied by e-commerce companies to promote and sale their product has an influence on the search activity in the sense that their marketing strategy influences the way Web sites are presented. Therefore, using this knowledge base, the Ishopping Agent identifies user's information needs and the most relevant information for customers.

We language educators are constant learners of teaching, not only of up-to-date pedagogy and technology but also the social context of target and native culture within which teaching occurs. Beyond my formal education, integral to my knowledge base are the range of student populations I have served and the variety of language teaching contexts I have encountered, such as heritage learners at UC Berkeley, the global popularity of Korean pop culture since the late 90s, and the pandemic situation since 2020. These are the local contextual factors that have affected the development of my knowledge base. To gain this knowledge base, I developed ways to integrate technology that accommodated my local contextual factors.

In the current scenario of organizations Process Management aims to perform process in an organized, prioritizing actions through techniques and methods that are linked to the improvement of the organization in the market segment it operates. As a determinant for achieving goals successfully, organizational memory is all important in the management of processes, allowing all areas meet in a systemic way, crediting their information to the various organizational sectors and thus using knowledge to direct an action with a focus planning to achieve strategic organizational goal. The organizational memory can be registered through the development of a knowledge base. The work in question aims to provide a better understanding of the importance of the knowledge base in an organization, to perform appropriate actions, planning, simulating and reaching a decision through meaningful data. Soon after the development of a bibliographic research, a bibliometric study on the proposed topic was accomplished with the main events of the Brazilian scientific areas of production engineering, analyzing how the topic has been addressed by authors in the areas of Process Management and Base Knowledge. The main results so far observed the need for the implementation of knowledge-based models in systems that seek to improve the execution of processes and thus reduce failures and decision-making processes more appropriate.