A self-organizing knowledge representation scheme for extensibleheterogeneous information environment

The self-organizing knowledge representation aspects in heterogeneous information environments involving object-oriented databases, relational databases, and rulebases are investigated. The authors consider a facet of self-organizability which sustains the structural semantic integrity of an integrated schemea regardless of the dynamic nature of local schemata. To achieve this objective, they propose an overall scheme for schema translation and schema integration with an object-oriented data model as common data model, and it is shown that integrated schemata can be maintained effortlessly by propagating updates in local schemata to integrated schemata unambiguously     

An integrated knowledge representation scheme for expert systems

Abstract The purpose of knowledge representation for an expert system is to specify functions to be performed by the system. In this paper, a knowledge representation scheme which mutually combines procedures, functions, production rules and Horn clauses is outlined. Its knowledge representation model is an imaginary organisation for performing functions of a target system, where a number of members try to solve given problems systematically. Knowledge is distributed to each of the members with considerable modularity. Functional specification of expert systems would be performed with less difficulty.     

Data/knowledge representation of modular robot and its working environment

This paper reports a work towards automating the design of a modular robot in the manufacturing environment. As the first as well as the most important step, a data model or data representation of all the design related information needs to be developed. This paper discusses the data representation of information needed for the computer to automatically generate a suitable modular robot configuration. A modular robot is supposed to work in a manufacturing assembly environment. Therefore, the collection of the information needed starts from such an environment. The information is further derived based on a proposed architecture of the computer-aided system for generating a modular robot configuration, which is based on two theories, i.e., cased-based reasoning and function-behaviour-structure (FBS). As a consequence, an integrated data representation of both modular robot and its environment is proposed and discussed in this paper. The integrated data representation also implies that function, behaviour and structure are related to a modular robot (as well as modular joint) are represented in the computer. EXPRESS data modelling language is used to complete the modelling work, and is found necessary for complex systems. A case study has been provided to show the capability of the developed data representations.     

一种精简的关联规则表示模型

针对关联规则之间存在的冗余性问题,已提出多种精简关联规则模型,但这些模型仍不同程度存在紧致度欠佳、信息丢失或恢复算法复杂的问题.提出了一种含更丰富关联信息的基本关联规则,并以基本关联规则为基础构建无损的精简关联规则集合,它是原始关联规则集合的子集,并能据此完全恢复原始关联规则集合.给出了基本关联规则模型的定义,证明了该精简模型的几个重要性质,并设计了用于挖掘该类规则的挖掘算法.实验表明,基本关联规则模型比现有的关联规则精简模型更加紧致.     

A hybrid scheme for action representation

Strong deficiencies are present in symbolic models for action representation and planning, regarding mainly the difficulty of coping with real, complex environments. These deficiencies can be attributed to several problems, such as the inadequacy in coping with incompletely structured situations, the difficulty of interacting with visual and motorial aspects, the difficulty in representing low-level knowledge, the need to specify the problem at a high level of detail, and so on. Besides the purely symbolic approaches, several nonsymbolic models have been developed, such as the recent class of subsym-bolic techniques. A promising paradigm for the modeling of reasoning, which combines features of both symbolic and analogical approaches, is based on the construction of analogical models of the reference for the internal representations, as introduced by Johnson-Laird. In this work, we propose a similar approach to the problem of knowledge representation and reasoning about actions and plans. We propose a hybrid approach, symbolic and analogical, in which the inferences are partially devolved to measurements on analogical models generated starting from the symbolic representation. the interaction between the symbolic and the analogical level is due to the fact that procedures are connected to some symbols, allowing generating, updating, and verifying the mental model. the hybrid model utilizes, for the symbolic component, a representation system based on the distinction between terminological and assertional knowledge. the terminological component adopts a SI-Net formalism, extended by temporal primitives. the assertional component is a subset of first-order logics. the analogical representation is a set of concurrent procedures modeling parts of the world, action processes, simulations, and metaphors based on force fields concepts. A particular case study, regarding the problem of the assembly of a complex object from parts, is taken as an experimental paradigm. © 1993 John Wiley Sons, Inc.     

Structure-significant representation of structured datasets

Numerical simulation of physical phenomena is an accepted way of scientific inquiry. However, the field is still evolving, with a profusion of new solution and grid generation techniques being continuously proposed. Concurrent and retrospective visualization are being used to validate the results. There is a need for representation schemes which allow access of structures in an increasing order of smoothness. We describe our methods on datasets obtained from curvilinear grids. Our target application required visualization of a computational simulation performed on a very remote supercomputer. Since no grid adaptation was performed, it was not deemed necessary to simplify or compress the grid. Inherent to the identification of significant structures is determining the location of the scale coherent structures and assigning saliency values to them. Scale coherent structures are obtained as a result of combining the coefficients of a wavelet transform across scales. The result of this operation is a correlation mask that delineates regions containing significant structures. A spatial subdivision is used to delineate regions of interest. The mask values in these subdivided regions are used as a measure of information content. Later, another wavelet transform is conducted within each subdivided region and the coefficients are sorted based on a perceptual function with bandpass characteristics. This allows for ranking of structures based on the order of significance, giving rise to an adaptive and embedded representation scheme. We demonstrate our methods on two datasets from computational field simulations. We show how our methods allow the ranked access of significant structures. We also compare our adaptive representation scheme with a fixed blocksize scheme     

Algebraic representation of structured R-programs

Conclusion The algebraic approach to representation of various graph structures and operations on them has been exploited by a number of authors [5, 6]. It should be noted, however, that the previous authors essentially relied on the notion of abstract computer memory, and the main focus was on the analysis of the informal meaning of the algorithms. The distinctive feature of our study is the construction of an algebra that depends only on the topological structure of the R-graphs, i.e., structure invariant under various arc loadings. The existence of this algebra leads to a useful representation of R-graphs, and the operations introduced in this paper may be used as technological operations for various programming transformations — optimization, verification, and debugging of graphic programs. Moreover, by associating a type with each R-graph, we can efficiently store R-graphs in computer memory in the form of linear lists, which simplifies the development of system programs of R-graph manipulation. Note, however, that effective use of the algebraic approach in R-technology requires a more detailed study of the R-algebra, especially in the direction of identical transformations.Translated from Kibernetika, No. 5, pp. 14–21, September–October, 1988.     

A multiresolution representation scheme for multimedia databases

We present a compression scheme that is useful for interactive video applications such as browsing a multimedia database. The focus of our approach the development of a compression scheme (and a corresponding retrieval scheme) that is optimal for any data rate. To browse a multimedia database, such a compression scheme is essential. We use a multiresolution setting, but eliminate the need for wavelets. This results in much better compression. We show experimental results and explain in detail how to extend our approach to multidimensional data.     

A knowledge representation for constraint satisfaction problems

We present a general representation for problems that can be reduced to constraint satisfaction problems (CSP) and a model for reasoning about their solution. The novel part of the model is a constraint-driven reasoner that manages a set of constraints specified in terms of arbitrarily complex Boolean expressions and represented in the form of a dependency network. This dependency network incorporates control information (derived from the syntax of the constraints) that is used for constraint propagation, contains dependency information that can be used for explanation and for dependency-directed backtracking, and is incremental in the sense that if the problem specification is modified, a new solution can be derived by modifying the existing solution. The constraint-driven reasoner is coupled to a problem solver which contains information about the problem variables and preference orderings     

A decidable first-order logic for knowledge representation

Decidable first-order logics with reasonable model-theoretic semantics have several benefits for knowledge representation. These logics have the expressive power of standard first order logic along with an inference algorithm that will always terminate, both important considerations for knowledge representation. Knowledge representation systems that include a faithful implementation of one of these logics can also use its model-theoretic semantics to provide meanings for the data they store. One such logic, a variant of a simple type of first-order relevance logic, is developed and its properties described. This logic, although extremely weak, does capture a non-trivial and well-motivated set of inferences that can be entrusted to a knowledge representation system.This is a revised and much extended version of a paper of the same name that appears in the Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, California, 1985.     

A new pattern representation scheme using data compression

We propose the PRDC (Pattern Representation based on Data Compression) scheme for media data analysis. PRDC is composed of two parts: an encoder that translates input data into text and a set of text compressors to generate a compression-ratio vector (CV). The CV is used as a feature of the input data. By preparing a set of media-specific encoders, PRDC becomes widely applicable. Analysis tasks - both categorization (class formation) and recognition (classification) - can be realized using CVs. After a mathematical discussion on the realizability of PRDC, the wide applicability of this scheme is demonstrated through the automatic categorization and/or recognition of music, voices, genomes, handwritten sketches and color images     

A new shape decomposition scheme for graph-based representation

Nowadays, the part-based representation of a given shape plays a significant role in shape-related applications, such as those involving content-based retrieval, object recognition, and so on. In this paper, to represent both 2-D and 3-D shapes as a relational structure, i.e. a graph, a new shape decomposition scheme, which recursively performs constrained morphological decomposition (CMD), is proposed. The CMD method adopts the use of the opening operation with the ball-shaped structuring element, and weighted convexity to select the optimal decomposition. For the sake of providing a compact representation, the merging criterion is applied using the weighted convexity difference. Therefore, the proposed scheme uses the split-and-merge approach. Finally, we present experimental results for various, modified 2-D shapes, as well as 3-D shapes represented by triangular meshes. Based on the experimental results, it is believed that the decomposition of a given shape coincides with that based on human insight for both 2-D and 3-D shapes, and also provides robustness to scaling, rotation, noise, shape deformation, and occlusion.     

Building modular legal knowledge systems

A practical legal knowledge system must be versatile and capable of supporting many fundamentally different aspects of the lawyer's work. In this paper a general framework for building knowledge systems from distinct but communicating modules is proposed. There are currently three different kinds of modules: computation, data storage, and environmental interaction. These modules include inference engines, database managers, and hypertext. Several strategies for coupling these modules are discussed. This framework is used to construct expert systems for two distinct domains: a legal expert system for labour law and a real-time expert system for process control in a pulp plant. The legal application shows that this system can be used to construct an “intelligent library”, guiding the user to relevant documents, rather than merely retrieving documents on request.     

A connectionist inference model for pattern-directed knowledge representation

In this paper we propose a connectionist model for variable binding. The model is topology dependent on the graph it builds based on the predicates available. The irregular connections between perceptron-like assemblies facilitate forward and backward chaining. The model treats the symbolic data as a sequence and represents the training set as a partially connected network using basic set and graph theory to form the internal representation. Inference is achieved by opportunistic reasoning via the bidirectional connections. Consequently, such activity stabilizes to a multigraph. This multigraph is composed of isomorphic subgraphs which all represent solutions to the query made. Such a model has a number of advantages over other methods in that irrelevant connections are avoided by superimposing positionally dependent sub-structures that are identical, variable binding can be encoded and multiple solutions can be extracted simultaneously. The model also has the ability to adapt its existing architecture when presented with new clauses and therefore add new relationships/rules to the model explicitly; this is done by some partial retraining of the network due to the superimposition properties.     

A computational knowledge representation model for cognitive computers

The accumulating data are easy to store but the ability of understanding and using it does not keep track with its growth. So researches focus on the nature of knowledge processing in the mind. This paper proposes a semantic model (CKRMCC) based on cognitive aspects that enables cognitive computer to process the knowledge as the human mind and find a suitable representation of that knowledge. In cognitive computer, knowledge processing passes through three major stages: knowledge acquisition and encoding, knowledge representation, and knowledge inference and validation. The core of CKRMCC is knowledge representation, which in turn proceeds through four phases: prototype formation phase, discrimination phase, generalization phase, and algorithm development phase. Each of those phases is mathematically formulated using the notions of real-time process algebra. The performance efficiency of CKRMCC is evaluated using some datasets from the well-known UCI repository of machine learning datasets. The acquired datasets are divided into training and testing data that are encoded using concept matrix. Consequently, in the knowledge representation stage, a set of symbolic rule is derived to establish a suitable representation for the training datasets. This representation will be available in a usable form when it is needed in the future. The inference stage uses the rule set to obtain the classes of the encoded testing datasets. Finally, knowledge validation phase is validating and verifying the results of applying the rule set on testing datasets. The performances are compared with classification and regression tree and support vector machine and prove that CKRMCC has an efficient performance in representing the knowledge using symbolic rules.     

A new concise representation of frequent itemsets using generators and a positive border

A complete set of frequent itemsets can get undesirably large due to redundancy when the minimum support threshold is low or when the database is dense. Several concise representations have been previously proposed to eliminate the redundancy. Generator based representations rely on a negative border to make the representation lossless. However, the number of itemsets on a negative border sometimes even exceeds the total number of frequent itemsets. In this paper, we propose to use a positive border together with frequent generators to form a lossless representation. A positive border is usually orders of magnitude smaller than its corresponding negative border. A set of frequent generators plus its positive border is always no larger than the corresponding complete set of frequent itemsets, thus it is a true concise representation. The generalized form of this representation is also proposed. We develop an efficient algorithm, called GrGrowth, to mine generators and positive borders as well as their generalizations. The GrGrowth algorithm uses the depth-first-search strategy to explore the search space, which is much more efficient than the breadth-first-search strategy adopted by most of the existing generator mining algorithms. Our experiment results show that the GrGrowth algorithm is significantly faster than level-wise algorithms for mining generator based representations, and is comparable to the state-of-the-art algorithms for mining frequent closed itemsets.

A new belief rule base knowledge representation scheme and inference methodology using the evidential reasoning rule for evidence combination

In this paper, we extend the original belief rule-base inference methodology using the evidential reasoning approach by i) introducing generalised belief rules as knowledge representation scheme, and ii) using the evidential reasoning rule for evidence combination in the rule-base inference methodology instead of the evidential reasoning approach. The result is a new rule-base inference methodology which is able to handle a combination of various types of uncertainty.Generalised belief rules are an extension of traditional rules where each consequent of a generalised belief rule is a belief distribution defined on the power set of propositions, or possible outcomes, that are assumed to be collectively exhaustive and mutually exclusive. This novel extension allows any combination of certain, uncertain, interval, partial or incomplete judgements to be represented as rule-based knowledge. It is shown that traditional IF-THEN rules, probabilistic IF-THEN rules, and interval rules are all special cases of the new generalised belief rules.The rule-base inference methodology has been updated to enable inference within generalised belief rule bases. The evidential reasoning rule for evidence combination is used for the aggregation of belief distributions of rule consequents.