Knowledge discovery in molecular databases

An approach to knowledge discovery in complex molecular databases is described. The machine learning paradigm used is structured concept formation, in which object's described in terms of components and their interrelationships are clustered and organized in a knowledge base. Symbolic images are used to represent classes of structured objects. A discovered molecular knowledge base is successfully used in the interpretation of a high resolution electron density map     

挖掘所关注规则的多策略方法研究

通过数据挖掘,从大型数据库中发现了大量规则,如何选取所关注的规则,是知识发现的重要研究内容。该文研究了利用领域知识对规则的主观关注程度进行度量的方法,给出了一个能够度量规则的简洁性和新奇性的客观关注程度的计算函数,提出了选取用户关注的规则的多策略方法。     

A bi-level concise modular structured knowledge representation scheme

For many years, major drawbacks that have been present in knowledge-based systems are their lack of structure-based knowledge access, high consumption of storage space, long response time, and incapability of tracing items related to each other, unlike a data processing system manipulating a data constructed according to the doubly linked list data structure. This article presents a bi-level knowledge representation scheme that addresses these problems along with several others. This scheme is based on a new knowledge access method in which the expert system communicates with the knowledge base through sub-knowledge sources rather than knowledge sources (facts and rules), i.e., through distinguished objects and relations. This method is used to design the scheme through building the knowledge base using a set of individual objects and relations. This individuality is shown to make it possible to construct highly efficient indices for these objects and relations.

It is shown that although the physical structure knowledge achieves high performance through implementing a concise and structured version of the knowledge in its low level, it is organized to provide efficient service to all the tasks carried out by the accompanying knowledge based system. Furthermore, although the low-level knowledge is highly abstracted, it is easily browsed in its full text mode, just like many existing knowledge bases. One of the important issues that this scheme address is the optiional incorporation of certainty degrees that is used by an appropriate reasoning strategy. It is also shown that fuzziness manipulation can be carried out or halted without having to rewrite the knowledge stored physically.     

Performance-friendly rule extraction in large water data-sets with AOC posets and relational concept analysis

In this paper, we consider data analysis methods for knowledge extraction from large water data-sets. More specifically, we try to connect physico-chemical parameters and the characteristics of taxons living in sample sites. Among these data analysis methods, we consider formal concept analysis (FCA), which is a recognized tool for classification and rule discovery on object–attribute data. Relational concept analysis (RCA) relies on FCA and deals with sets of object–attribute data provided with relations. RCA produces more informative results but at the expense of an increase in complexity. Besides, in numerous applications of FCA, the partially ordered set of concepts introducing attributes or objects (AOC poset, for Attribute–Object–Concept poset) is used rather than the concept lattice in order to reduce combinatorial problems. AOC posets are much smaller and easier to compute than concept lattices and still contain the information needed to rebuild the initial data. This paper introduces a variant of the RCA process based on AOC posets rather than concept lattices. This approach is compared with RCA based on iceberg lattices. Experiments are performed with various scaling operators, and a specific operator is introduced to deal with noisy data. We show that using AOC poset on water data-sets provides a reasonable concept number and allows us to extract meaningful implication rules (association rules whose confidence is 1), whose semantics depends on the chosen scaling operator.     

Structured analysis of the ISI Atomic Pair Actions dataset using workflows

Understanding pair-wise activities is an essential step towards studying complex group and crowd behaviors in video. However, such research is often hampered by a lack of datasets that concentrate specifically on Atomic Pair Actions; [Here, we distinguish between the atomic motion of individual objects and the atomic motion of pairs of objects. The term action in Atomic Pair Action means an atomic interaction movement of two objects in video; a pair activity, then, is composed of multiple actions by a pair or multiple pairs of interacting objects ( and ). Please see Section 1 for details.] in addition, the general dearth in computer vision of a standardized, structured approach for reproducing and analyzing the efficacy of different models limits the ability to compare different approaches. In this paper, we introduce the ISI Atomic Pair Actions dataset, a set of 90 videos that concentrate on the Atomic Pair Actions of objects in video, namely converging, diverging, and moving in parallel. We further incorporate a structured, end-to-end analysis methodology, based on workflows, to easily and automatically allow for standardized testing of state-of-the-art models, as well as inter-operability of varied codebases and incorporation of novel models. We demonstrate the efficacy of our structured framework by testing several models on the new dataset. In addition, we make the full dataset (the videos, along with their associated tracks and ground truth, and the exported workflows) publicly available to the research community for free use and extension at <http://research.sethi.org/ricky/datasets/>.     

基于混合推理的知识库的构建及其应用研究

该文提出了基于OWL本体与Prolog规则的平面几何知识库的构建方法,从而可形式化地表示平面几何中丰富的语义信息.一方面,用类型、定义域、值域、分类、属性、实例等本体描述来表达结构化的知识,为领域内概念与概念之间关系的描述提供形式化的语义;另一方面,用Prolog规则来解决本体不能有效表达的诸如属性之间的关系和操作等问题,从而支持复杂关系间的推理.在此基础上,用Protégé和Prolog构建了一个基于本体和规则的平面几何知识库.实验证明:此知识库可实现知识和语义层次上的信息查询,还可进行复杂问题求解,其丰富的语义描述和混合推理能力弥补了传统知识库的不足.     

Fuzzy concepts in an object oriented expert system shell

Fuzzy logic is one of the methods to model the vagueness and imprecision of human knowledge. Some rule-based expert system shells have been successfully developed and have demonstrated the power of fuzzy logic in dealing with inexact reasoning and rule inferences. However, using rules for knowledge representation is not structured enough. In addition, knowledge cannot be easily represented in an abstracted (hierarchical) from. In this article the introduction of fuzzy concepts into object oriented knowledge representation (OOKR), which is a structured knowledge representation scheme, is presented. A framework for handling all the possible fuzzy concepts in OOKR at both the dynamic and static levels is proposed. In order to handle the inheritance mechanism and to model the relations among classes, instances, and attributes, some new fuzzy concepts and operations are introduced. These concepts and operations are developed from the semantic meaning rather than by an ad hoc approach. A prototype of the expert system shell. System FX-I, has been successfully developed based on the above framework, showing the feasibility of handling inexact knowledge in a structural way.     

Automatic vectorization of segmented road networks by geometrical and topological analysis of high resolution binary images

In this paper, a new method in order to achieve the geometrical and topological definition of extracted road networks is presented. Starting from a raster binary image where a road network is depicted, this algorithm seeks the automatic raster – vector conversion based on skeleton extraction and graph theory and using GIS database if it is available. The last goal of this method is to provide a numerical structured file which includes the geometric definition for all roads as well as the topologic relations between them. The applied technique comprises six steps. In the first step, the quality of the binary image is improved through a noise cleaning process. In the second step, parallel edges of road network are smoothed by means a generalization process. In the third step, skeleton is extracted applying a known and efficient method which some years ago was published. The fourth step consists in constructing the graph and generating the different cartographic objects which compose the road network. In this phase, GIS information can be used in order to improve the result. In the fifth step, objects are numerically adjusted by means of polynomial adjustment in the opened objects case, and using a reiterative polygonal adjustment in the sharp objects case. In the last step, mathematical morphology is applied to validate topologically the geometrical adjustment. For it, the junction nodes are analyzed for changing automatically their coordinates in order to achieve a topologically correct road network vectorization. Finally, objects are structured according to cartographic criteria and a numerical file with the vectorized road network is provided. Experimental results show the validity of this approach.     

基于Web语料的概念获取系统的研究与实现

互联网网页中存在大量的专业知识。如何从这些资源中获取知识已经成为10多年来的一个重要的研究课题。概念和概念间的关系是知识的基本组成部分，因此如何获取并验证概念，成为从文本到知识的过程中的重要步骤。本文提出并实现了一种自动从Web语料中获取概念的方法，该方法利用了规则、统计、上下文信息等多种方法和信息。实验结果表明，该方法达到了较好的效果。     

A rule-based support system for dissonance discovery and control applied to car driving

This paper is based on the concept of dissonance, that is, gaps or conflicts existing in a specific knowledge base or among different knowledge bases. It presents a rule-based system that assists human operators in dissonance discovery and control by taking into account two kinds of dissonance, i.e., affordance to study conflicts of use, and inconsistencies to study conflicts of intention and action, through the analysis of cognitive behavior implemented in knowledge bases. This system elaborates the knowledge base composed of rules, and analyzes the knowledge content to discover new knowledge by creating additional rules, or to identify inconsistencies when conflicts between rules occur. The affordance discovery control process uses a deductive and an inductive reasoning algorithm of which the aim is to establish new rules using existing ones. The inconsistency discovery control process applies an abductive reasoning algorithm in order to determine contradictory rules when existing rules may result in opposite intentions being accomplished. Two groups of inconsistencies are addressed: interferences involving several decision makers, and contradictions involving the same decision maker. A knowledge acquisition control process facilitates the creation of the initial rules that contain parameters such as intentions relating to the goals to be achieved, actions to be performed to achieve these intentions, objects used to carry out these actions and the decision makers who execute these actions using the corresponding objects. A feasibility study taking into account five rule bases relating to the manual use of an Automated Speed Control System (ASCS), the automated control of the car speed by the ASCS, the manual control of aquaplaning, the manual control of the car speed, and the manual control of car fuel consumption is proposed to validate the rule-based support system.     

概念指导的关联规则的挖掘

关联规则是数据依赖关系泊有效描述方法,是知识发现研究的重要内容,传统的关联规则挖掘算法缺少挖掘的针对性,挖掘速度慢,挖掘效果难于理解,挖掘析数量巨大,需要进行大量的筛选以便抽取出有用规则,文中提出了将概念融入挖掘过程中,提高挖掘的效率和挖掘的针对性的方法,给出了概念指导的关联规则挖掘算法ＣＧＡＲＭ和大数据库中概念的交互式生成方法。算法ＣＧＡＲＭ是对基于分类的挖掘算法的拓展。实验结果表明,算法ＣＧＡ     

Ontologies for agents

An ontology is a computational model of some portion of the world. It is often captured in some form of a semantic network-a graph whose nodes are concepts or individual objects and whose arcs represent relationships or associations among the concepts. This network is augmented by properties and attributes, constraints, functions, and rules that govern the behavior of the concepts. Formally, an ontology is an agreement about a shared conceptualization, which includes frameworks for modeling domain knowledge and agreements about the representation of particular domain theories. Definitions associate the names of entities in a universe of discourse (for example, classes, relations, functions, or other objects) with human readable text describing what the names mean, and formal axioms that constrain the interpretation and well formed use of these names. For information systems, or for the Internet, ontologies can be used to organize keywords and database concepts by capturing the semantic relationships among the keywords or among the tables and fields in a database. The semantic relationships give users an abstract view of an information space for their domain of interest     

基于概念格的异构数据知识发现方法

基于概念格的知识发现方法已被广泛关注,同时也吸引了众多学者的研究兴趣,特别是决策形式背景的知识发现,近年来取得了一些重要的研究成果。然而,现有的知识发现方法在面临大数据环境时,缺乏可行性与有效性。考虑到异构性是大数据的主要数据特征之一,针对异构数据,研究了基于概念格的知识发现方法。具体地,提出了异构形式背景及其概念格,通过异构形式背景定义了异构决策形式背景,进一步在异构决策形式背景上讨论了规则提取问题,并给出了挖掘非冗余决策规则的有效算法。     

Flexible control in expert systems for construction tasks

In most expert systems for constructional tasks, the knowledge base consists of a set of facts or object definitions and a set of rules. These rules contain knowledge about correct or ideal solutions as well as knowledge on how to control the construction process. In this paper, we present an approach that avoids this type of rules and thus the disadvantages caused by them.We propose a static knowledge base consisting of a set of object definitions interconnected by is-a and part-of links. This conceptual hierarchy declaratively defines a taxonomy of domain objects and the aggregation of components to composite objects. Thus, the conceptual hierarchy describes the set of all admissible solutions to a constructional problem. Interdependencies between objects are represented by constraints. A solution is a syntactically complete and correct instantiation of the conceptual hierarchy.No control knowledge is included in the conceptual hierarchy. Instead, the control mechanism will use the conceptual hierarchy as a guideline. Thus it is possible to determine in which respects a current partial solution is incomplete simply by syntactical comparison with the conceptual hierarchy. The control architecture proposed here has the following characteristics: separation of control and object knowledge, declarative representation of control knowledge, and explicit control decisions in the problem solving process. Thus, a flexible control mechanism can be realized that supports interactive construction, integration of case-based approaches and simulation methods.This control method is part of an expert system kernel for planning and configuration tasks in technical domains. This kernel has been developed at the University of Hamburg and is currently applied to several domains.     

Ontological emergence scheme in self-organized and emerging systems

This paper presents the concept of “Ontological Emergence”, a process that seeks to adapt an ontology to the changes and new components in a self-organized and emergent system, through the application of a set of rules that allows the emergence of a new conceptualization (emerging concepts). The Ontological Emergence provides the structuration of the information and knowledge that could be generated in the system, creating conceptual models that can adequately represent the new behavior that is emerging. It arises from the need to represent ontologically a conceptualization of a reality that is dynamic, which cannot be pre-defined or pre-determined, in order to generate emerging knowledge models that follows the scalability and the evolution of it. In that sense, in this paper is proposed an “Ontological Emergence Scheme” based on a set of processes of registration, monitoring, analysis and adaptation of the various conceptual models that interact in the system, as well as on some processing rules in regard to requirements and information of the context, in order to allow the ontological emergence. In this proposal scheme, the Meta-ontologies guide the ontological emergence process through the definition of general categories, to facilitate the integration of concepts from different ontologies or data sources. Finally, the paper presents some case studies, showing its utility in self-organized and emergent systems.     

面向矛盾问题的描述逻辑SHOQ扩展

为了利用描述逻辑的推理规则分析并解决简单矛盾问题,引入了可拓集合作为描述逻辑SHOQ的集合论基础,提出了一种新的描述逻辑D-SHOQES,定义了可拓概念和可拓关系的截集形式作为原子概念和原子关系,引入了动作理论以获取概念和关系的质变域和量变域,并给出了D-SHOQES中概念、关系、动作等的语义解释以及Tableau-算法推理规则,研究了矛盾问题的一般求解方法,从而为矛盾问题的求解提供了策略。     

智能家居情境感知服务的运行时建模与执行方法

随着智能家居基础设施的不断发展,智能家居逐渐进入以智能服务为特征的新时期.大量复杂、异构的智能设备相互协同,构成海量、智能、集成的智能家居应用.其中,情境感知服务根据服务对象所处情境的变化为其提供准确的服务,是智能家居应用的典型代表.目前,情境感知服务往往面向场景进行构建,其设备多样性和服务随需性给应用开发带来极大的挑战.开发者需要熟悉设备管理接口、进行接口调用和交互,同时,理解服务功能和质量需求,进行管理逻辑的编写.为了快速定制和开发情境感知服务,将知识图谱引入开发过程,提出一种智能家居情境感知服务的运行时建模与执行方法：首先,提出智能家居情境感知服务知识图谱概念模型,定义其情境中各种概念和关系;其次,提出智能家居情境感知服务知识图谱实例模型的构造与维护机制,通过运行时概念、关系实例表示情境知识;最后,提出基于知识推理的智能家居情境感知服务执行方法,通过知识推理自动执行设备功能.面向实际场景,构建智能家居原型系统.实验结果显示,该方法能够实现情境感知服务运行时建模与执行,其代码减少量超过90%.