Object-oriented Knowledge Base Research Articles

A Tool for Creating Exams Automatically from an Object-Oriented Knowledge Base Question Bank

 Abstract—The way of creating exams can enhance education. Also, the exam should cover majority of course material and should include various levels of difficulties. This paper presents a tool designed for automatically creating exams by selecting questions from a bank of questions for several courses. This bank of question is designed as an object-oriented knowledge base. Its questions should emphasize on the structuring categories of all course domains. An intelligent agent will help in selecting questions. It will watch the examiners and learn from their experience, at editing questions or at creating exams. The presented tool could be applied in several courses and can be used at various education level and nature. It is designed to create online exam, as well as, offline one. Also, the tool will produce a key for the produced exam. So, this tool can extend the E-Learning and provide more success in distant education.

Comparison of entity with fuzzy data types in fuzzy object-oriented databases

Aiming at distinguishing entities, the concept of entity comparison plays an important role in object management in object-oriented databases as well as object-oriented knowledge bases. In order to model uncertain data and complex-valued attributes a

R-DEVICE

In this paper we present R-DEVICE, a deductive object-oriented knowledge base system for reasoning over RDF metadata. R-DEVICE imports RDF documents into the CLIPS production rule system by transforming RDF triples into COOL objects and uses a deductive rule language for reasoning about them. R-DEVICE is based on an OO RDF data model, different than the established triple-based model, which maps resources to objects and encapsulates properties inside resource objects, as traditional OO attributes. In this way, fewer joins are required to access the properties of a single resource resulting in better inferencing/querying performance, as it is experimentally shown in the paper. Furthermore, RDF can interoperate seamlessly with other Web data models and languages. The descriptive semantics of RDF may call for dynamic redefinitions of resource classes, which are handled by R-DEVICE effectively. Furthermore, R-DEVICE features a powerful deductive rule language for reasoning on top of RDF metadata. The rule language includes features such as normal and generalized path expressions, stratified negation, aggregate, grouping, and sorting functions. The rule language supports a second-order syntax, which is efficiently translated into sets of first-order logic rules using metadata, where variables can range over classes and properties, so that reasoning over the RDF schema can be made. Users can define views that are materialized and incrementally maintained by translating deductive rules into CLIPS production rules that preserve truth. Users can choose between an OPS5/CLIPS-like and a RuleML-like syntax. Finally, users can define and use functions through the CLIPS host language.

Object-oriented modelling of large-scale chemical engineering processes with ProMoT

The application of the process modelling tool ProMoT to large-scale chemical engineering processes is presented. Particular emphasis is on the flexibility of the tool in terms of creating flowsheet alternatives—a prerequisite for efficient plant design and control structure selection. The main concepts in this respect are the bottom-up development of an object-oriented knowledge base for the simplified implementation of submodels on the level of process units, the efficient aggregation on the flowsheet level and the flexibility to effectively perform top-down refinements for problem-specific applications. All concepts are illustrated on the basis of an industrial scale process.

類似性の観察に基づく知識ベースの内包的エラー修正法

In this paper, we propose a new framework of knowledge revision, Similarity-Driven Knowledge Revision. For an object-oriented knowledge base KB, our revision is triggered when a similarity between sort concepts detected from KB does not fit a user's intuition. We revise KB into a knowledge base from which such an undesirable similarity is not detected and in which the logical semantics of KB is still preserved. An observation of undesirable similarity is due to an over-general typing of variable in KB. In order to modify the typing, we introduce a notion of extended sorts that can be viewed as a sort concept not appearing explicitly in the original knowledge base. If a variable typing with some sort is considered over-general, the typing is modified by replacing it with more specific extended sort. Such an extended sort can be efficiently identified by forward reasoning with SOL-resolution from the original knowledge base. Some experimental results show that the use of SOL-resolution can drastically improve the computational efficiency.

A decision support system for geotechnical applications

Abstract A knowledge-based system shell called ‘Confound’ has been developed for the production of decision support systems. It was initially used for developing a system for conceptual foundation design but has subsequently been used for retaining wall, expansive soil and ground investigation applications. It uses a hierarchical object-oriented knowledge base where each object represents a decision in the decision making process. A simple rule-based approach is used to provide advice on the suitability of each potential choice, based on project specific information that is provided by the user. Confound has the ability to work with incomplete information and uses a system of confidence levels so that the user is aware of the confidence with which each piece of advice is given. Conflicting information in areas where knowledge is poorly defined can also be handled. Confound is designed to leave the user in full control. It simply provides comments on suitability (with an indication of confidence) and leaves the final decision to the user.

SciFinance: A Program Synthesis Tool for Financial Modeling

The SciFinance software synthesis system, licensed to major investment banks, automates programming for financial risk-management activities -- from algorithms research to production pricing to risk control. SciFinance's high-level, extensible specification language, aspen, lets quantitative analysts generate code from concise model descriptions written in application-specific and mathematical terminology; typically, a page or less produces thousands of lines of c. aspen's abstractions help analysts focus on their primary tasks -- model description, validation, and analysis -- rather than on programming details. Compared with manual programming, automation produces codes that are more sophisticated, accurate, and consistent. Analysts develop models within a day that previously took weeks or were not even attempted. SciFinance extends a system that generates scientific computing codes in a variety of target languages. The implementation integrates an object-oriented knowledge base, refinement and optimization rules, computer algebra, and a planning system. The shared knowledge base is used by the specification checker, synthesis system, and information portal.

Computer tool supported process design methodology

This work focuses on the development of a computer tool for conceptual chemical process design. We demonstrate how to create a multipurpose chemical process design tool, after development of a process design methodology. A computer environment for methodology implementation is presented, MetaEdit+ ®, which provides a platform both for methodology implementation and methodology usage. This work results in a prototype tool for supporting conceptual chemical process engineering in the form of object-oriented knowledge base.

OSAM*.KBMS/P: a parallel, active, object-oriented knowledge base server

An active object-oriented knowledge base server can provide many desirable features for supporting a wide spectrum of advanced and complex database applications. Knowledge rules, which are used to define a variety of database tasks to be performed automatically on the occurrence of some events, often need much more sophisticated rule-specification and control mechanisms than the traditional priority-based mechanism to specify the control structural relationships and parallel execution properties among rules. The underlying object-oriented (OO) knowledge representation model must provide a means to model the structural relationships among data entities and the control structures among rules in a uniform fashion. The transaction execution model must provide a means to incorporate the execution of structured rules in a transaction framework. Also, a parallel implementation of an active knowledge base server is essential to achieve the needed efficiency in processing nested transactions and rules. In this paper, we present the architecture, implementation, and performance of a parallel active OO knowledge base server, which has the following features. First, the server is developed based on an extended OO knowledge representation model that models rules as objects and their control structural relationships as association types. This is analogous to the modeling of entities as objects and their structural relationships as association types. Thus, entities and rules, and their structures, can be uniformly modeled. Second, the server uses a graph-based transaction model that can naturally incorporate the control semantics of structured rules and guarantee the serializable execution of rules as subtransactions. Thus, the rule-execution model is uniformly integrated with that of transactions. Third, it uses an asynchronous parallel execution model to process the graph-based transactions and structured rules. This server, named OSAM*.KBMS/P, has been implemented on a shared-nothing multiprocessor system (nCUBE2) to verify and evaluate the proposed knowledge-representation model, graph-based transaction model, and asynchronous parallel-execution model.

Application of the modeling and simulation environment ProMoT/Diva to the modeling of distillation processes

In this contribution, the application of the computer-aided modeling and simulation environment ProMoT/Diva to the modeling of (reactive) distillation processes is presented. The main focus is on the description of the underlying modeling concept, together with some implications of this concept for the modeling of distillation processes. One important point of the modeling concept and the subsequent design of the knowledge base of ProMoT is the decomposition of individual process units into physically meaningful, finer grained modeling entities on different hierarchical levels. The hierarchical levels considered thus far are the process unit level, the phase level, and the storage level. This contribution focuses mainly on the definition of the modeling entities on the phase level, which are important for the modeling of (reactive) distillation processes. Furthermore, the design of the object-oriented knowledge base of ProMoT that contains these modeling entities is illustrated.

QPS — A TOOL FOR QUANTITATIVE PROBLEM SOLVING IN SCIENCE AND ENGINEERING

An AI tool called the Quantitative Problem Solver (QPS) has been developed for building knowledge based systems which can solve quantitative problems in science and engineering. QPS can store and manipulate quantitative knowledge comprising numerical data and scientific laws represented by formulas. The human interface is based on the symbols commonly used by scientists and engineers. All knowledge is represented as objects and classes in an object-oriented knowledge base. QPS employs the familiar Problem Decomposition strategy for selecting the correct sequence of equations needed for solving problems and it has been tested by the building of knowledge based systems to solve several simple problems in Engineering and Physics.

Design and development of a medical decision Support System

SUMMARY This paper reports on POEMS (Post-Operative Expert Medical System), which incorporates a diagnostic decision support system and a learning apprentice. The former embodies an object—oriented knowledge base and an incremental diagnostic engine which is based on a temporal modeller and a constraint based diagnoser. It employs an active user—interaction methodology for acquiring patient data, and a collaborative question- answering facility. The decision support system provides assistance to the doctors in order to reach diagnostic decisions, and helps to discriminate between multiple diagnoses. The learning apprentice actively acquires new diagnostic knowledge from patient cases and the medical expert's criticism of its diagnostic actions. It is based on an integrated machine learning methodology, Extended Exemplar—Based Learning (EExBL), which draws on exemplar—based learning, explanation—based learning, and similarity—based learning.

An object-oriented knowledge base for multi-domain expert systems

With the current technology of “deep ” expert systems, domain knowledge can be modeled, using an object-oriented representation, separately from the strategic knowledge required to make inferences on the domain. Where such domains involve the construction of some hierarchy or network, a generic inference strategy can be devised to search the domain knowledge for plausible solutions. Thus, our main concern is in building an appropriate object-class structure that accurately models the problem domain. Rules are merely written to customize the expert system according to the uniqueness of each problem domain. Thus, a common framework for user interaction can be devised for domains of the same “type.” By accessing the various domains stored in various knowledge bases, an individual is able to get the benefit of “expert advice” about several areas, using a somewhat similar interface, varying only according to context and terminology. Hence, the objective of this paper is to show how deep knowledge can be used to effectively model the descriptive information of a problem domain without the need to develop and maintain a large rule network. Consequently, we suggest a generic inference strategy that reasons on the deep knowledge via the application of heuristic functions.

An integrated neural network/expert system approach for fault diagnosis

The main thrust of this research is the development of an artificially intelligent system to be used as an operators' aid in the diagnosis of faults in large-scale chemical process plants. The operator advisory system involves the integration of two fundamentally different artificial intelligence techniques: expert systems and neural networks. A diagnostic strategy based on the hierarchical use of neural networks is used as a first-level filter to diagnose faults commonly encountered in chemical process plants. Once the faults are localized within the process by the neural networks, the deep knowledge expert system analyzes the results, and either confirms the diagnosis or else offers an alternative solution. The model-based expert system contains information of the plant's structure and function within its object-oriented knowledge base. The diagnostic strategy can handle multiple faults, novel or previously unencountered faults, and noisy process sensory measurements. The operator advisory system is demonstrated using a multi-column distillation plant as a case study.

EuroCASE CD—A knowledge-based debugger for hardware/software configurations

EuroCASE CD is a “configuration debugger” i.e. a CASE-tool to be used to diagnose and repair malfunctioning software composed of different products at module level. Software configurations consisting of various products, using resources and showing error symptoms, are mapped both into a relational database and an object-oriented knowledge base. In a two-step analysis procedure, first the necessary conditions for the correct operation of a set of software modules are checked in the database. The second step is done by case-based reasoning. Based on similarity measures and already-known configurations, products and symptoms, the configuration debugger tries to deduce the reason for the error. Then the system delivers proposals on how to “repair” a configuration (by replacing modules or changing parameters). The system is based on Oracle and Nexpert Object; an operational prototype is running both on PCs and VMS-workstations.

A knowledge-based approach for high-level programming of concurrent systems

It is generally accepted that object-based systems provide an elegant paradigm for general-purpose concurrent computations. In this paper, we propose to use the frameworks of object-oriented knowledge base and object-oriented concurrent production system to facilitate the task of programming object based systems. Specifically, we describe a program transformation system which accepts a set of sequential processes written in a conventional sequential language and a set of coordination constraints. The output of the transformation system is a set of coordinating (distributed) processes where each of which is a production system and parallel in nature, communicating asynchronously with others, and all-together satisfying the coordinating constraints identified by the programmer.

Transformation approach for consistency in object-oriented knowledge bases

A knowledge base is consistent if all its data, information, or knowledge (regardless they are stored in database or implied by inference engine) satisfy the predefined constraints which represent a real world rules or restrictions. Maintaining knowledge consistency in an object-oriented knowledge base is difficult since (i) two data items in the knowledge base may have the relationship that is not explicitly represented by the inheritance; and (ii) logic programming trends to manipulate "flat" data. In contrast, it is easier to develp a mechanism for maintaining the consistency in a value-oriented system (e.g., relational database or logic programming) [Ullm87].

Query optimization in object-oriented knowledge bases

We define an object-oriented knowledge base to be a deductive knowledge base that is based on an object model. One of the major advantages provided by object-oriented knowledge bases is that the first order language used for object description can be used as a query language as well. Since object-dependent procedures are included in this framework, the problem of query processing is nontrivial. On one hand some attributes involved in a query need to be derived from a procedure and constrained by some other relations, and therefore combinatorial explosion may occur; on the other hand a query may be specified in an improper order such that the search space becomes intolerable large. In this paper we first describe two approaches to optimize the query evaluation process: the semantic optimization approach and the conjuct reordering approach. Next, we examine the problem of logical query processing when an object-oriented knowledge base is built on a relational database such as Ingres.

Object-based knowledge bases in automatic manufacturing environments

A manufacturing system is a very complex network of physical activities, decision making and information flows. Hence the complexity of the design and the integration of such a system increases with increases in the degree of automation. In this paper the authors propose to use the framework of an object-oriented knowledge base as the basis to, organise the knowledge in an automatic manufacturing environment. Such systems can provide significant advantages in terms of knowledge management, integrity control, object abstraction, design abstraction, and very high level programming. The power of this framework is illustrated in detail in the context of process planning, which is one of the major components of an automatic manufacturing environment.

VLSI design with object-oriented knowledge bases

One major technology that is needed is the development of a system that can integrate design tools and design databases, and is able to design an entire engineering system based on formal specifications. In this paper, a knowledge engineering framework for designing VLSI computer architectures is introduced. This framework introduces three core concepts: knowledge abstraction, object-oriented design, and very high-level design programming. The input to the system is a descriptive specification of the behaviour of a computing system. The description is then matched against the existing design knowledge in the knowledge base, where the knowledge is abstracted and organized as classes. If a match can be found, the abstract knowledge is instantiated and can be reused; otherwise a heuristic synthesis process will be performed according to the design knowledge stored in the system. In the worst case, the designer goes one level down and decomposes the system into a set of smaller systems, where a structural description among different modules is specified, but only the behavioural specification for each module is given. The process is then repeated until a complete structural description is developed. At the bottom level, the system is designed to support and automate the following tasks: integration of layout design tools; propagation of dynamic changes; propagation and abstraction of performance measurements; incremental design rule checking; and version control.