Deductive Knowledge Research Articles

Issues in an inference platform for generating deductive knowledge: a case study in cultural heritage digital libraries using the CIDOC CRM

Most information retrieval research focuses collecting documents that match the same set of concepts. This study considers a more advanced problem, namely how to discover knowledge not contained in a single source from combined historical facts. By using a well-designed core ontology in the cultural domain (CIDOC CRM, ISO21127), this study discusses the requirement for a robust inference platform for real-life knowledge discovery and integration over distributed sources. The methodology and design are justified in detail through functional requirements for an inference service with the capability of inferring new knowledge from combinations of facts distributed over different sources. A number of critical issues for developing such a robust inference platform are identified, namely (1) systematic accumulation of common concepts and inference rules; (2) extending the ontology with metaclasses; (3) accumulation of factual and categorical knowledge; (4) incorporation of fuzzy inference into the inference engine, and (5) improvement of performance and scalability in the inference engine.

AN ARTIFICIAL INTELLIGENCE APPROACH TO COURSE TIMETABLING

Course Timetabling is a complex problem that cannot be dealt with by using only a few general principles. The various actors (the administrator, the chairman, the instructor and the student) have their own objectives, and these objectives usually conflict. The complexity of the relationships among time slots, classes, classrooms, and instructors makes it difficult to achieve a feasible solution. In this article, we propose an artificial intelligence approach that integrates expert systems and constraint programming to implement a course timetabling system. Expert systems are utilized to incorporate knowledge into the timetabling system and to provide a reasoning capability for knowledge deduction. Separating out the knowledge base, the facts, and the inference engine in expert systems provides greater flexibility in supporting changes. The constraint hierarchy and the constraint network are utilized to capture hard and soft constraints and to reason about constraints by using constraint satisfaction and relaxation techniques. In addition, object-oriented software engineering is applied to improve the development and maintenance of the course timetabling system. A course timetabling system in the Department of Computer Science and Information Engineering at the National Changhua University of Education (NCUE) is used as an illustrative example of the proposed approach.

A review of studies on decomposer microbiota in Uganda

AbstractSoil microflora and fauna participate in many ecosystem functions including organic matter decomposition and nutrient cycling. These processes significantly control natural and agro‐ecosystems productivity particularly in Uganda. The decomposer microbiota is poorly studied despite voluminous data on the processes involved. Currently there is scanty information on the diversity, distribution, abundance, population dynamics, biochemical capabilities of these organisms and how the latter relate to the above ground biodiversity and the environment as a whole. Definite rather than deductive knowledge is required for conservation and sustainable management of these resources. Immediate research programmes are recommended to address gaps in scientific and socio‐economic aspects of micro‐decomposers and related fields. Equally crucial is the review of existing policies, how they relate to belowground biodiversity and identify gaps and avenues of influencing formulation of pertinent instruments. In addition, capacity building, both physical and human to handle the challenge(s) effectively is obligatory.

PlanetOnto: from news publishing to integrated knowledge management support

Given a scenario in which members of an academic community collaboratively construct and share an archive of news items, several knowledge management challenges arise. The authors' integrated suite of tools, called PlanetOnto, supports a speedy but high quality publishing process, allows ontology-driven document formalization and augments standard browsing and search facilities with deductive knowledge retrieval.

Logical Structure of Contract Law System - For Constructing a Knowledge Base of the United Nations Convention on Contracts for the International Sale of Goods -

In order to construct a deductive legal knowledge base, it is necessary first to clarify the structure of the law as a deductive system from which a legal judgement can be justified as a conclusion of logical deduction together with relevant facts. As the legal state of affairs changes according to the time progress of an event, a clarified logical model of law is necessary to enable us to deduce changes among legal relationships over time from the beginning to the end of a case. This study presents such a model based on Logical Jurisprudence, in which the relationship between legal sentences and the legal meta sentences regulating the validity of legal sentences plays a definitive role. The model is applied to the United Nations Convention on Contracts for the International Sale of Goods (CISG) to develop a deductive knowledge base. The deductive structure of the contract law is clarified so that appropriate answers are deduced to questions about legal state of affairs at any time point as a results of the application of CISG provisions to a concrete case.

A new fragmentation scheme for recursive query processing

Data fragmentation is a well-known technique used in distributed database design to support efficient query processing. In deductive databases and knowledge bases, one distinct operation is the computation of transitive closures. In this paper, we employ a fragmentation approach to deductive databases. Fragmentation techniques are investigated in distributed database environments for parallel recursive query processing. Based on the novel fragmentation scheme, a new parallel transitive closure algorithm is proposed. Compared with previous algorithms, our algorithm can explore a much higher level of parallelism with very little communication overhead increase.

Revising deductive knowledge and stereotypical knowledge in a student model

A user/student model must be revised when new information about the user/student is obtained. But a sophisticated user/student model is a complex structure that contains different types of knowledge. Different techniques may be needed for revising different types of knowledge. This paper presents a student model maintenance system (SMMS) which deals with revision of two important types of knowledge in student models: deductive knowledge and stereotypical knowledge. In the SMMS, deductive knowledge is represented by justified beliefs. Its revision is accomplished by a combination of techniques involving reason maintenance and formal diagnosis. Stereotypical knowledge is represented in the Default Package Network (DPN). The DPN is a knowledge partitioning hierarchy in which each node contains concepts in a sub-domain. Revision of stereotypical knowledge is realized by propagating new information through the DPN to change default packages (stereotypes) of the nodes in the DPN. A revision of deductive knowledge may trigger a revision of stereotypical knowledge, which results in a desirable student model in which the two types of knowledge exist harmoniously.

Disorder, Dynamical Chaos and Structures

Science, like life itself, often poses questions whose answers are so sophisticated and indefinite that one must either rely on the opinion established by the majority or refer to some article of “faith” rather than deductive knowledge. “Where does randomness come from?” “How does order arise from disorder?” These are perhaps the oldest questions of this kind. Anyone who has thought about the principles governing nature has surely faced such questions. Everyday life, as well as conventional learning in physics, has almost persuaded us that complex, irregular and intricate behavior is possible only in very complex systems and that the study of some of these very complex systems may be beyond the scope of physical theory. The vast multitude of molecules in a bottle filled with gas or a crowd of infuriated fans reacting to the news that a football match has been canceled suddenly, are both examples of complex systems. In such systems we usually fail to relate cause and effect uambigously—that is, we cannot predict the behavior of the system in detail and therefore conclude that it is random. Of course, there is always the hope that it might be possible to eliminate such seeming randomness and unpredictability if one had more precise knowledge about the system. Would having a detailed knowledge of the interaction among the elements of a complex system and comprehensive and precise information about their initial states allow one to make precise predictions about the system's behavior at large times, so that the system will not appear to be random? The answer is yes only if one believes in Laplacian determinism, which was one of the earliest dogmas of science.

Searching for the Starting Points of Scientific Inquiry: Durkheim's Rules of Sociological Method and Schopenhauer's Philosophy*

Durkheim's methodological classic is frequently read from Kantian, positivistic, or other Enlightenment contexts despite the fact that Durkheim criticizes these doctrines. Durkheim also tends to be read as a deductive analyst. Using Schopenhauer's philosophy as an alternative starting point in reading the Rules, it is demonstrated that Schopenhauer and Durkheim agree that perceptual, inductive knowledge of “things” is superior to conceptual, deductive knowledge; that causal explanations are merely phenomenal; and that the one, well‐designed experiment is sufficient for the establishment of scientific laws. Durkheim's distinction between the normal and the pathological is also addressed in this context. The implications of Durkheim's focus on induction are discussed with regard to its similarity to the works of Claude Bernard, Florian Znaniecki, and Max Weber, as well as current epistemological crises in sociology.

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.

Problem Solving Training — A Factor in Debugging Computer Programs

This research demonstrates problem solving training using tasks that require deductive reasoning and intellectual knowledge can influence the ability of programmers to debug source code. Professional programmers were assigned to either the control or experimental groups. The experimental group participated in 10 training sessions. At the completion of these sessions, all subjects were required to locate the bugs in test source code. Performance measure was time to locate the bugs. The performance of the experimental group was signifivantlly better than the control group. The results are discussed from the behavior point of view.

An investigation into the goals of research in automatic theorem proving as related to mathematical reasoning

We argue that theorem provers based on domain dependent knowledge must be able to increase their domain dependent deductive knowledge if they are to serve as a component of a mathematical reasoning system. The reason for this is that if such systems are not extensible then they would not be able to assimilate and use new deductive knowledge produced by a mathematical reasoning system.