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A knowledge model for knowledge base system in the Web environment is proposed, which includes problem description layer and knowledge layer. A knowledge base is made up of a set of knowledge models. Knowledge models are used to support knowledge representation and reasoning. Visual knowledge modeling tool and visual knowledge service tool based on Web environment realize the construction of knowledge base system in Web environment. The knowledge base system supports the construction of agricultural expert system. By comparison with CommonKADS, it is proved that this method can improve the efficiency of knowledge acquisition, management and maintenance in Web environment. This method also has the reference significance to the construction of the knowledge base system in the Web environment in other domains.

The intelligent control of cleaning of rice–wheat combined harvester is a complex problem, which includes the initial setting of cleaning control, judgment of cleaning loss state, cause analysis and selection of corresponding control strategies and many other sub-problems. The knowledge contained in these sub-problems, including knowledge representation methods and reasoning strategies, is different. Therefore, this paper decomposes the complex problem of cleaning control into a sub-problem of hierarchical structure, and constructs a knowledge model of cleaning control based on binary tree structure. In this way, the cleaning control problem can be decomposed into a small set of sub-problems by the judgment of the nodes of the binary tree, until the sub-problems are small enough to be solved directly so as to get the solution of the original problem. It is proved by examples that this method is of great significance to improve the efficiency of knowledge acquisition, management and maintenance of the expert system of rice–wheat combine harvester, and to enhance the knowledge service ability of the expert system of rice–wheat combine harvester. This method can also be used for reference in other fields.

The use of external libraries in today’s software projects allows developers to take advantage of features provided by such application programming interfaces (APIs) without having to reinvent the wheel. However, APIs have also introduced new challenges to the software engineering community (e.g. API incompatibilities, software vulnerabilities, and license violations) that extend beyond traditional project boundaries and often involve different software artifacts. One potential solution to these challenges is to provide a technology-independent representation of software dependency semantics and its integration with knowledge from other software artifacts.

In our research, we take advantage of the semantic web (SW) and its technology stack to establish a unified knowledge representation of build and dependency repositories. Given this knowledge base, we can now extend and integrate other (heterogeneous) resources to allow for a flexible and comprehensive global impact analysis approach. To illustrate the applicability of our SW-enabled modeling approach, we discuss two different applications. These applications illustrate how our modeling approach can not only integrate and reuse knowledge from dependency management systems and other software artifacts, but also take advantage of inference services provided by the SW to support novel software analytics services across artifact and project boundaries.

This paper introduces a statistical framework for extracting medical domain knowledge from heterogeneous corpora. The acquired information is incorporated into a natural language understanding agent and applied to DIKTIS, an existing web-based educational dialogue system for the chemotherapy of nosocomial and community acquired pneumonia, aiming at providing a more intelligent natural language interaction. Unlike the majority of existing dialogue understanding engines, the presented system automatically encodes semantic representation of a user's query using Bayesian networks. The structure of the networks is determined from annotated dialogue corpora using the Bayesian scoring method, thus eliminating the tedious and costly process of manually coding domain knowledge. The conditional probability distributions are estimated during a training phase using data obtained from the same set of dialogue acts. In order to cope with words absent from our restricted dialogue corpus, a separate offline module was incorporated, which estimates their semantic role from both medical and general raw text corpora, correlating them with known lexical-semantically similar words or predefined topics. Lexical similarity is identified on the basis of both contextual similarity and co-occurrence in conjunctive expressions. The evaluation of the platform was performed against the existing language natural understanding module of DIKTIS, the architecture of which is based on manually embedded domain knowledge.

Modeling user knowledge and creating user profiles not only for special web-based social media but also for complex and mixed personalized learning environments are important research challenges. The key component for adaptation is the user’s knowledge model. This paper introduces fuzzy metric (FM)-based novel and efficient similarity measurement method and adaptive artificial neural network (AANN) and artificial bee colony (ABC)-based knowledge classification approaches for personalized learning environments. For this purpose, FM-based method has been developed to measure distances more efficiently among the users and their knowledge model using the web logs/session data. In addition, a novel knowledge classifier based on ABC and AANN having combined with the generic object model has been developed for user modeling strategies and user modeling server of adaptive educational electric course (AEEC). Finally, the approaches have been tested to compare the classification performance of the user modeling methods developed for user modeling task. The experimental results have shown that proposed methods have improved similarity measurements considerably and decreased the misclassifications in user modeling processes. Thus, powerful user modeling approaches have been presented to the literature. It is expected that the approaches introduced in this article can be a reference to others researches and to develop more adaptive and personalized web applications in future.

As populations age and large numbers of skilled workers progress toward retirement, the importance of preserving and sharing expert knowledge is becoming an increasing concern for organisations worldwide. This article contains descriptions of two case studies involving initiatives to elicit, preserve, and share expert knowledge in the nuclear power industry using a knowledge modeling toolkit named CmapTools and knowledge elicitation techniques that were originated at the Institute for Human and Machine Cognition, Pensacola, FL., USA. Along with an account of preparations for the work including the selection of the experts, the course of the sessions, results and impacts of the studies, missed opportunities, and lessons learned are described. Some similarities and some interesting differences between the case studies are discussed.

Nowadays, software development and maintenance are highly distributed processes that involve a multitude of supporting tools and resources. Knowledge relevant for a particular software maintenance task is typically dispersed over a wide range of artifacts in different representational formats and at different abstraction levels, resulting in isolated 'information silos'. An increasing number of task-specific software tools aim to support developers, but this often results in additional challenges, as not every project member can be familiar with every tool and its applicability for a given problem. Furthermore, historical knowledge about successfully performed modifications is lost, since only the result is recorded in versioning systems, but not how a developer arrived at the solution. In this research, we introduce conceptual models for the software domain that go beyond existing program and tool models, by including maintenance processes and their constituents. The models are supported by a pro-active, ambient, knowledge-based environment that integrates users, tasks, tools, and resources, as well as processes and history-specific information. Given this ambient environment, we demonstrate how maintainers can be supported with contextual guidance during typical maintenance tasks through the use of ontology queries and reasoning services.

Computers are central in processing scientific data. This data is typically expressed as numbers and strings. Appropriate annotation of "bare" data is required to allow people or machines to interpret it and to relate the data to real-world phenomena. In scientific practice however, annotations are often incomplete and ambiguous — let alone machine interpretable. This holds for reports and papers, but also for spreadsheets and databases. Moreover, in practice it is often unclear how the data has been created. This hampers interpretation, reproduction and reuse of results and thus leads to suboptimal science. In this paper we focus on annotation of scientific computations. For this purpose we propose the ontology OQR (Ontology of Quantitative Research). It includes a way to represent generic scientific methods and their implementation in software packages, invocation of these methods and handling of tabular datasets. This ontology promotes annotation by humans, but also allows automatic, semantic processing of numerical data. It allows scientists to understand the selected settings of computational methods and to automatically reproduce data generated by others. A prototype application demonstrates this can be done, illustrated by a case in food research. We evaluate this case with a number of researchers in the considered domain.

Knowledge acquisition is becoming an integral part of the manufacturing industries, which rely on domain experts in various phases of product life cycle including design, analysis, manufacturing, operation and maintenance. It has the potential to enable knowledge reuse, however, poorly managed knowledge can cause information loss and inefficiency. If technical documentation is managed well in the manufacturing industries, intended piece of knowledge can easily be located, used and reused for purpose and as a result, the corresponding industry can be benefited. Some examples of technical documentation are design specification, operating manual and maintenance manual. Model-based Documentation (MBD) is a documentation approach that uses model to provide structure to the data of the documents. MBD can be thought of as a way to better organize knowledge thereby knowledge identification and retrieval become easier, faster and efficient. In this paper, we propose MBD and its extension as a potential solution to overcome the issues involved in the typical technical documentation approaches.

The essence of knowledge-level modeling (henceforth knowledge modeling) is to represent a system at a level which abstracts from implementation considerations and focuses instead on its competence: what does the system know? how does the system use its knowledge? The system in question is not necessarily a software artifact: it can be an organization, a human being, an artificial agent. For instance, modern methodologies for knowledge-based system development, such as CommonKADS, prescribe the development of abstract problem solving and domain models, prior to their implementation in a particular tool. In a knowledge management scenario, a knowledge modeling approach can be used to develop a model of the competence of an organization, which can then support a variety of decision-making scenarios. The recent social and technological changes (i.e., the rise of the knowledge-creating company, the rapid growth of the Internet) have also emphasized the need for effective methods and formalisms for acquiring, representing, sharing and maintaining knowledge, independently of its use in performance systems. In this paper, I will provide an overview of research in knowledge modeling. Specifically, I will first characterize the main tenets of the knowledge modeling paradigm, as formulated by a number of researchers in knowledge-based systems, and I will then present the state-of-the-art in knowledge modeling research, with particular emphasis on the application of knowledge modeling technology to knowledge engineering, knowledge management and knowledge sharing and reuse.

Knowledge acquisition is becoming an integral part of the manufacturing industries, which rely on domain experts in various phases of product life cycle including design, analysis, manufacturing, operation and maintenance. It has the potential to enable knowledge reuse, however, poorly managed knowledge can cause information loss and inefficiency. If technical documentation is managed well in the manufacturing industries, intended piece of knowledge can easily be located, used and reused for purpose and as a result, the corresponding industry can be benefited. Some examples of technical documentation are design specification, operating manual and maintenance manual. Model-based Documentation (MBD) is a documentation approach that uses model to provide structure to the data of the documents. MBD can be thought of as a way to better organize knowledge thereby knowledge identification and retrieval become easier, faster and efficient. In this paper, we propose MBD and its extension as a potential solution to overcome the issues involved in the typical technical documentation approaches.

The criterion compliance evaluation method of CAD model for electromechanical cable product has been researched. The purpose of this paper is to ensure the design quality and reduce the time and cost of the design changes before the manufacturing stage. The six elements notation of rule definition, the architecture of rule system and hierarchy of evaluation objects have been put forward to build a comprehensive evaluation method. This method is able to measure and estimate CAD model data using subjective or objective evaluation means, and the evaluation priority level and weight coefficient can be determined by AHP. An application system has been developed to achieve the above evaluation process, using knowledge modeling language to define checking rules in CAD model. The problem of complex model evaluation can be resolved by the flexible strategy of man-machine combination. This method and its application have the advantages of high efficiency, simple operation and reliable evaluation results, which has agreeable engineering practicability.