Knowledge Based System

We present a novel approach to learning taxonomic relations between terms by considering multiple and heterogeneous sources of evidence. In order to derive an optimal combination of these sources, we exploit a machine-learning ap- proach, representing all the sources of evidence as first-order features and training standard classifiers. We consider in particular different features derived from Word- Net, an approach

■ Project Halo is a multistaged effort, sponsored by Vulcan Inc, aimed at creating Digital Aristo-tle, an application that will encompass much of the world's scientific knowledge and be capable of applying sophisticated problem solving to answer novel questions. Vulcan ...

Good feature extraction and selection methods are crucial for adequate performance of remote sensing data classification methods. In general, a big set of features causes degradation in results of classification. When data dimensionality is very high, a search strategy should be used to select the subset of features that gives largest separability between classes. But good results of classification also

A computer system that consists of a set of several programs that share both the same geometric definition and a consistent set of definitions for construction elements, is under development. The system consists of a common program allowing different types of exportation to other simulation programs or to other modules. The main modules under development until now are: an interface to the Energy Plus program, which builds an "idf" file from a 3D geometric definition that was previously modeled in graphical mode; and a module, that will provide the necessary calculations to verify if a given shape fulfills the thermal regulation of buildings which is based upon the Energy Performance of Buildings Directive (EPBD) approved by European Parliament and Council. An interface with Radiance program is also being developed. Using this system, which has been designed firstly for architectural conception, the user can perform different simulations with different aims, but sharing the same architectural shape and construction types, in order to check if the building fulfills thermal Portuguese codes and to perform energy simulations with Energy Plus or Radiance. The use of this system has demonstrated that it can represent a great saving of time in simulations and helping to reduce the occurrence of design errors as well. Since it allows performing simulations in a very quick way it can contribute also to help architects on achieving better solutions while designing, in terms of the optimization of all the parameter evaluated. The use of energy plus to evaluate the comfort of the buildings while being design as shown that this methodology can help to find more sustainable solutions in terms of their architectural shape and construction elements.

This document presents the high-level design of a knowledge-based system to assist measurers in applying a functional measurement method consistently and systematically to often quite complex software applications which, moreover, may be from various application domains. The knowledge model underlying the proposed system is built on the key concepts of the software development process itself, as well as on the key concepts of a specific measurement method. The concepts describing the development process originate from the ontology of the SWEBOK [5] project and those related to the functional measurement method from the ontology of the COSMIC-FFP[6] method. The task originates from our modeling of the types of knowledge embedded in the measurement process.

To support an end to end Question and Answering system to help the clinical practitioners in a cardiovascular healthcare environment, an extended discourse representation structure CIDERS is introduced. This extension of the well-known DRT (Discourse Representation Theory) structures, go beyond single text representation extending them to embrace the general clinical history of a given patient. Introduced is a proposed and developed ontology framework, Ontology for General Clinical Practice, enhancing the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty, It's shown the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox).

Selection of optimal roofing materials is very important but it is a complex and onerous task as varieties of materials are available for housing roof construction. In order to select suitable materials, an extensive range of criteria would need to be considered. This paper presents the framework and the development of a knowledge-based decision support system for material selection implemented in roofing material selection domain, called 'Knowledge-based Decision Support system for roofing Material Selection and cost estimating' (KDSMS). It was developed to facilitate the selection of optimal materials for different roof sub elements. The system consists of a database and knowledge base that is equipped with an inference engine. The former is used to store different types of roofing materials with assigned attribute values. The later is used to hold qualitative and quantitative knowledge which were collected from domain experts and other technical literatures such as building regulations, price guide book and product catalogues. The proposed system employs the TOPSIS (Technique of ranking Preferences by Similarity to the Ideal Solution) multiple criteria decision making method to solve materials selection and optimisation problem. This study utilised the available roofing materials in the UK housing market in developing the system reported. The main contribution of the developed system is that it provides a tool for the architects, quantity surveyors or self house builder to select optimal materials from a wide array of possibilities for different roof sub elements and also to estimate the conceptual cost for the roof element in the early stage of building design.

In this paper, we discuss how networked robot architectures can facilitate the realization, deployment, management and adaptation of distributed robotic applications. Our aim is to modularize applications by factoring out environment-, task-, domain-, and robot-specific knowledge components and representing them explicitly in a formal knowledge base that is shared between the robots. Robot control decisions can then be formulated in terms of inference tasks that are evaluated based on this knowledge during task execution. The explicit and modular knowledge representation allows operators with different areas of expertise to adapt the respective parts of the knowledge independently. We realized this concept by integrating knowledge representation methods of the RoboEarth project with the distributed task execution capabilities of the Ubiquitous Network Robot Platform.

This paper presents the automatic extension of TERSEO to other languages, a knowledge-based system for the recognition and normalization of temporal expressions, originally developed for Spanish. TERSEO was extended to English and Italian through the automatic translation of the temporal expressions, and it was presented in previous works (see Saquete et al. (2004a)), but a new methodology has been designed with the purpose of obtaining better results in this issue. This new methodology is based on the use of parallel corpora for extending the TERSEO temporal model to other languages. In this case, two different methods have been tested: (1) automatic translation of TERSEO patterns to other languages and (2) automatic corpora annotation in the target side of parallel corpora. The main idea is focused on annotating the Spanish side of a parallel corpora, projecting the analysis to the second language, and then obtaining new TERSEO patterns (1) and new annotated corpus (2). The set of new patterns will be used to improve the current TERSEO language independent modules. Whereas the new annotated corpus will be used to train a ML system. This system will annotate new temporal expressions in the new language.

In this paper, a method of event ordering based on temporal information resolution is presented. This method consists of two main steps: on the one hand, the recognition and resolution of the temporal expressions that can be transformed on a date, and therefore these dates establish an order between the events that contain them. On the other hand, the detection of temporal signals, for example after, that can not be transformed on a concrete date but relate two events in a chronological way. This event ordering method can be applied to Natural Language Processing systems, for example: Summarization, Question Answering, etc. It is important to emphasize that the event ordering method is based on a multilingual temporal information resolution system. Moreover, this multilinguality has been automatically obtained from a monolingual system (Spanish). The evaluation of the multilingual system is also shown in this paper, achieving a precision of 88% for Spanish and 77% for English.

A distinctive property of relational database systems is the ability to perform data updates and queries in atomic blocks called transactions, with the well-known atomicity, consistency, isolation and durability (ACID) properties. To date, the ability of systems performing reasoning to maintain the ACID properties, even over data held within a relational database, has been largely ignored. This article studies an approach to reasoning over data from web ontology language (OWL) 2 RL ontologies held in a relational database, where the ACID properties of transactions are maintained. Taking an incremental approach to maintaining materialised views of the result of reasoning, the approach is demonstrated to support a query and reasoning performance comparable to or better than other OWL reasoning systems, yet adding the important benefit of supporting transactions.

Cloud computing enables the sharing of resources such as storage, network, applications and software through internet. Cloud users can lease multiple resources according to their requirements, and pay only for the services they use. However, despite all cloud benefits there are many security concerns related to hardware, virtualization, network, data and service providers that act as a significant barrier in the adoption of cloud in the IT industry. In this paper, we survey the top security concerns related to cloud computing. For each of these security threats we describe, i) how it can be used to exploit cloud components and its effect on cloud entities such as providers and users, and ii) the security solutions that must be taken to prevent these threats. These solutions include the security techniques from existing literature as well as the best security practices that must be followed by cloud administrators.

As more applications are depending on semantic web data from diverse sources, semantic web data evaluation is becoming more critical. While language validators and general reasoners can help, these typically focus on syntax and logical consistency. Many applications need additional support for finding possible issues (as well as provable mistakes). We are investigating methods and environments that provide computational support for identifying possible problems with instance data. We report on a line of work focusing on evaluation of provable and possible problems with OWL instance data and provide some discussion motivated by our first application setting validating large amounts of diverse explanation data.

As personal assistant software matures and assumes more autonomous control of its users' activities, it becomes more critical that this software can explain its task processing. It must be able to tell the user why it is doing what it is doing, and instill trust in the user that its task knowledge reflects standard practice and is being appropriately applied. We will describe the ICEE (Integrated Cognitive Explanation Environment) explanation system and its approach to explaining task reasoning. Key features include (1) an architecture ...

In this paper, we begin by introducing reactive multicontext systems (rMCSs), a framework for reactive reasoning in the presence of heterogeneous knowledge sources. In particular, we show how to integrate data streams into multi-context systems (MCSs) and how to model the dynamics of the systems, based on two types of bridge rules. We then discuss various methods for handling inconsistencies, a problem that occurs with reasoning based on multiple knowledge sources that need to be integrated, with a special focus on non-existence of equilibria.

Modelling, reasoning about and integrating knowledge based on multiple time granularities in knowledge-based systems is important, especially when talking about events that take place in the real world. Formal approaches based on temporal logics have been successfully applied in many application domains of knowledge-based systems where the notion of dynamic change (that is, evolution of a system through time) is central. This paper presents a methodology based on temporal logic to deal with knowledge based on multiple time granularities in knowledge-based systems. The methodology includes an approach to the representation of timing systems, a method used for representing facts and rules in a knowledge-based system that involve multiple time granularities, and several deductive reasoning techniques.

Representation, reasoning about and integrating knowledge based on multiple time granularities in knowledge-based systems is important, especially when talking about events that take place in the real world. Formal approaches based on temporal logics have been successfully applied in many application domains of knowledgebased systems where the evolution of a system and its environment through time is central. This paper presents a methodology based on temporal logic to deal with knowledge based on multiple time granularities in knowledge-based systems. The temporal logic we consider is especially suitable for modelling events with different rates and/or scales of progress. The methodology includes an approach to the representation of timing systems, a method used for representing facts and rules in a knowledge-based system that involve multiple time granularities using temporal logic, and several deductive reasoning techniques.

The objective of expert systems is the use of Artificial Intelligence tools so as to solve problems within specific prefixed applications. In the last two decades a great experimental effort together with some theoretical knowledge have been employed to investigate the completeness and consistency of knowledge-based systems and to clarify the structure of these systems. Nevertheless, there is often a gap in the formalism which allows the structuring of the expert system programming towards the expert system design. In the last years, a new field called Ontological Engineering, defined by the IEEE as "the field that establishes a set of concepts, axioms, and relationships that describe a domain of scientific or technological interest" is trying to fill this gap. The work presented here may be placed in this context. In particular, the paper deals with the development of an expert system valid to optimize the adaptation transients arising in adaptive control using a logic formalism previously described, providing good simulation results. Its structure is composed by a supervisor based on an expert network organization and designed to improve the transient performances in the adaptive control of a planar robot. Apart form the basic adaptation scheme consisting of an estimation algorithm plus an adaptive controller, two additional coordinated expert systems are used to update an adaptation gain and the sampling period with a master expert system coordinating both above expert systems.

The overwhelming flood of data in intensive care medicine precludes consistent judgement of medical interventions by humans. Therefore, computerized decision support is needed to assist the health care professional in making reproducible, high-quality decisions at the bedside. Traditional expert systems rely on a tedious, labor-intensive and time-consuming approach in their development which falls short of exploiting existing numerical and qualitative data in large medical databases. Therefore, we applied a new concept of combining time series analysis and a knowledge base system with learning and revision capabilities (MOBAL) for rapid development of decision support algorithms for hemodynamic management of the critically ill. This approach could be successfully implemented in an existing intensive care database handling time-oriented data to validate and refine the intervention rules. The generation of hypotheses for identified contradictions lead to conclusive medical explanations that helped to further refine the knowledge base. This approach will provide for a more efficient and timely development of decision support algorithms.

Carrying out a statistical analysis of empirical data the researcher is typically concerned with the problem of choosing an appropriate statistical technique from a large number of competing methods. Most of the common statistical software only allows to analyse the data by applying certain methods that are implemented in this software without giving any support to the researcher with respect to the adequacy of a method for a particular data set. This paper outlines the main features of the computer system CORA which p r o vides a statistical analysis of strati ed contingency tables and additionally supports the researcher at the di erent steps of this analysis. The support given by the system consists of two di erent aspects. On the one hand the help system of CORA contains general information on the implemented statistical methods which can be obtained on request by the user. On the other hand an advice tool recommends an adequate statistical method. This advice depends on the actual empirical case{control data that the user wants to analyse. To build up the advice tool a set of rules being discovered by m a c hine learning is integrated into the system CORA. Simulation studies that investigate the nite{sample behaviour of estimators serve a s k n o wledge sources for this discovery process. The presented way of constructing such systems can be seen as a general approach which is applicable in many elds of research: with the means of machine learning you can discover knowledge in simulation studies and then integrate this knowledge into a system which supports the user by recommendations (guidelines) how to proceed.

Trust and commitment have been identified as crucial concepts in electronic commerce applications. In this paper we are interested in the relation between these social concepts. We introduce a dynamic logic in which violations of stronger commitments result in a higher loss of trustworthiness than violations of weaker ones. We illustrate how the logic can be used to analyze some aspects of a well known example of trust within reason.

This paper presents a framework for a distributed knowledge based system by integrating case based reasoning (CBR) and Fuzzy Logic. Fuzzy Logic gives CBR the power to deal with impreciseness and uncertainty. The framework for handling distributed case bases enables our system to construct solution based on collective experience distributed by discipline, time, and geography. In the proposed system the cases can be expressed in terms of attributes that can be crisp as well as fuzzy and appropriately similarity scores are computed. The cases can have attributes from a vocabulary, which can be defined with the constraint of global commitments so that the attributes can be shared and interpreted in a distributed setting. We have implemented a knowledge sharing protocol with common ontology as the repository of exchange vocabulary for knowledge sources with different Universe of Discourses (UOD). We have developed a shell for tailored application development in different domains. We have used RDBMS as the back end repository for cases, DAML + OIL for Ontology design, SAX and DOM for ontology access and RMI for remote procedure call. We have illustrated effectiveness of our approach by developing a travel planning and a help desk application.

Task-and artefact-centric business process models (BPMs) are mostly used in isolation. This entails, e.g., problems with formal and automated verification of BPMs through model checking. We address this gap through semantic task specification, which is transferred from more widely known semantic service specification. In summary, we present a new and systematic approach for connecting a task-centric BPM (in BPMN) with a model of an artefactcentric object life cycle through semantic task specification. As a consequence, we achieve a seamless approach for formal and automated verification of BPMs using model checking. Semantic service specification has been based upon the Web Ontology Language (OWL), which is a knowledge representation language used to build and administer on-4817

This paper presents the hardware architecture and the software development of a real-time knowledge-based distributed control system for the supervision of a wastewater treatment pilot plant with biological removal of organic matter and nitrogen. A continuous monitoring of plant and controls data is used by an expert system developed in G2, a development environment based on object-oriented paradigm. A set of rules and procedures to help fault detection, plant maintenance, and nitrification -denitrification cycle operation was implemented and validated at pilot scale. The hardware architecture includes different supervision levels, including two autonomous process computers (plant control and analysers control).

Since the 2000s, social networks have grown spectacularly until they are now regarded as indispensable and introduced as a daily practice of millions of users. Enterprises have become aware of the need and the importance of these collaborative tools, and the concept of Enterprise Social Network (ESN) has now emerged. As such, OpenPaaS is an innovative ESN that aims at facilitating inter-organizational collaborations. In this sense, this paper describes the new OpenPaaS platform to address current issues of ESNs: (i) the management and exchange of large amount of data during inter-organizational collaborations; (ii) the ability of the system to provide synchronous communications between collaborative partners; and finally, (iii) the establishment of transverse collaborations. This last point is a key feature of OpenPaaS, which goal is to automatically generate new inter-organizational coalitions by deducing collaborative processes in response to an opportunity of collaborations brought by any enterprise of the ESN.

Recent information technologies make it possible to include sophisticated three-dimensional display formats in emergency management information systems (EMIS), decision-support systems that facilitate decision making in crisis situations. However, if decision makers are to improve their decisional performance, they must correctly identify appropriate situations for using these formats. We conduct two experiments and find that, as prior research has suggested, decision makers do not choose the most appropriate display format, but their performance improves when given prospective decisional guidance. We discuss implications of these findings for EMIS design, for the training of emergency management professionals, and for future research on display formats and decisional guidance.

This paper presents TRIPLE, a layered and modularrule language for the semantic web [1]. TRIPLE is basedon Horn logic and borrows many basic features fromF-Logic [9] but is especially designed for querying andtransforming RDF models [17].TRIPLE can be viewed as a successor of SiLRI (SimpleLogic-based RDF Interpreter [5]). One of the mostimportant dierences to F-Logic and SiLRI is thatTRIPLE does

One of the bene®ts of formally represented knowledge lies in its potential to be shared. Ontologies have been proposed as the ultimate solution to problems in knowledge sharing. However even when an agreed correspondence between ontologies is reached that is not the end of the problems in knowledge sharing. In this paper we explore a number of realistic knowledge-sharing situations and their related problems for which ontologies fall short in providing a solution. For each situation we propose and analyse alternative solutions.

In this paper, we present a self-integrating knowledge-based expert system for brain tumor diagnosis. The system we propose comprises knowledge building, knowledge inference and knowledge refinement. During knowlege building, an automatic knowledge-integration process, based on Darwin's theory of natural selection, integrates knowledge derived from knowledge-acquisition tools and machinelearning methods to construct an initial knowledge base, thus eliminating a major bottleneck in developing a brain tumor diagnostic system. During the knowledge inference process, an inference engine exploits rules in the knowledge base to help diagnosticians determine brain tumor etiologies according to computer tomography pictures. And, a simple knowledge refinement method is proposed to modify the existing knowledge base during inference, which dramatically improves the accuracy of the derived rules. The performance of the brain tumor diagnostic system has been evaluated on actual brain tumor cases.

This paper describes the design and implementation of a vision-guided autonomous vehicle that represented BYU in the 2005 Intelligent Ground Vehicle Competition (IGVC), in which autonomous vehicles navigate a course marked with white lines while avoiding obstacles consisting of orange construction barrels, white buckets and potholes. Our project began in the context of a senior capstone course in which multi-disciplinary teams of five students were responsible for the design, construction, and programming of their own robots. Each team received a computer motherboard, a camera, and a small budget for the purchase of additional hardware, including a chassis and motors. The resource constraints resulted in a simple vision-based design that processes the sequence of images from the single camera to determine motor controls. Color segmentation separates white and orange from each image, and then the segmented image is examined using a 10x10 grid system, effectively creating a low resolution picture for each of the two colors. Depending on its position, each filled grid square influences the selection of an appropriate turn magnitude. Motor commands determined from the white and orange images are then combined to yield the final motion command for video frame. We describe the complete algorithm and the robot hardware and we present results that show the overall effectiveness of our control approach.

Results of queries by personal names often contain documents related to several people because of the namesake problem. In order to differentiate documents related to different people, an effective method is needed to measure document similarities and to find documents related to the same person. Some previous researchers have used the vector space model or have tried to extract common named entities for measuring similarities. We propose a new method that uses Web directories as a knowledge base to find shared contexts in document pairs and uses the measurement of shared contexts to determine similarities between document pairs. Experimental results show that our proposed method outperforms the vector space model method and the named entity recognition method.

The amount of information on the World Wide Web (WWW) is increasing at an explosive rate, and the role of computer systems in processing such a huge amount of data has become crucial. In this paper, we focus on the name disambiguation problem when searching for people, because information about people is an important part of the web and improvements to personal information may benefit many web citizens. The name ambiguity problem occurs frequently when searching for people, because a name may be shared by several people. In this research, we use external knowledge while solving this problem, so that we can analyze information in web documents more easily. We collect web directories and use the latent Dirichlet allocation method to extract latent topics from web directories. The extracted topics are used to modify the search result documents so that important contexts that help to discriminate people can be recognized more easily. We carried out experiments with real web documents and verified the advantages of our approach over other disambiguation approaches that use the vector space model and named entity recognition methods.

An enterprise federated architecture intends to mirror the structure of the organisation, aiming to provide better support for both new and legacy applications within a distributed environment and facilitating data exchange between applications to support information integration. Under this architectural form, the organisation's information systems are separated out into autonomous co-operating application clusters, each connected to a message-oriented federal highway acting as the vehicle for inter-domain communication. The federated approach intends to avoid unnecessary coupling (in the distributed computing sense) by grouping highly interdependent modules and applications into domains, whilst minimising the strength of inter-domain connections. This article presents how to design a distributed federated architectural form using three architectural patterns, and shows how these three patterns are to be connected to comply with the specification of the the federated form. Let us consider this example: SafetyNet Insurance started as a small Australian company selling house and car insurance, but it expanded very quickly by merging with another company specialised in life insurance. The merger was to take advantage of the synergies between the different insurance products by offering their customers discounts and other advantages if they were loyal to the company by conducting all their business with SafetyNet. Based on the success of their sales force, the organisation expanded with branches in other capital cities in Australia. It was considered crucial that salespeople maintained at least the same level of satisfaction with their jobs, so new incentive schemes were introduced to reward the high selling branches, salespersons, etc. The inital attempts to provide the necessary information for the new way of doing business were paper or file based. Lists of customers, sales, policy information, and other data were exchanged on paper or via files by Head Office and the different branches and groups within their branches, such as General insurance, Life Insurance, Human Resources, Finance, Actuarial Services, and Management Information Systems. Very soon a stream of problems started to emerge:

We describe a proposal of a knowledge model for the development of a framework for hypnogram con-struction from intelligent analysis of pulmonology and electrophysiological signals. Throughout the twen-tieth century, after the development of electroencephalography (EEG) by Hans Berger, there have been multiple studies on human sleep and its structure. Polysomnography (PSG), a sleep study from several biophysiological variables, gives us the hypnogram, a graphic representation of the stages of sleep as a function of time. This graph, when analyzed in conjunction with other physiological parameters, such as the heart rate or the amount of oxygen in arterial blood, has become a valuable diagnostic tool for differ-ent clinical problems that can occur during sleep and that often cause poor quality sleep. Currently, the gold standard for the detection of sleep events and for the correct classification of sleep stages are the rules published by the American Academy of Sleep Medicine (AASM), version 2.2. Based on the standards available to date, different studies on methods of automatic analysis of sleep and its stages have been de-veloped but because of the different development and validation procedures used in existing methods, a rigorous and useful comparative analysis of results and their ability to correctly classify sleep stages is not possible. In this sense, we propose an approach that ensures that sleep stage classification task is not af-fected by the method for extracting PSG features and events. This approach is based on the development of a knowledge-intensive base system (KBS) for classifying sleep stages and building the corresponding hypnogram. For this development we used the CommonKADS methodology, that has become a de facto standard for the development of KBSs. As a result, we present a new knowledge model that can be used for the subsequent development of an intelligent system for hypnogram construction that allows us to isolate the process of signal processing to identify sleep stages so that the hypnograms obtained become comparable, independently of the signal analysis techniques.

Several implementation issues of the primitive-based image codec are discussed in this paper, namely, the structure of the database, coding the primitives when no match is found in the database, buffer control, and accumulation of error. A hierarchical database, constructed during the coder's operation, is used to avoid the difficulties of constructing an off-line database. Singular-value decomposition of the primitives are used in a way similar to that of transform coding to construct a compact database and to code the primitives when no match from the database is found. Accumulation of error is prevented by transmitting with each frame the pixels from 3 lines that are evenly spaced in the image raster and moved up each frame time. The achievable compression ratio is also analyzed in the paper.

For several years, Lisp has been the most popular programming language for artificial intelligence. PRO-LOG, however, is rapidly becoming the second most popular Al language; for several applications, PRO-LOG is even preferred to Lisp. In this paper, the suitability of PROLOG as an expert system building tool is demonstrated: a small expert system shell is discussed, and compared to the OELFl-2 system, after the example of which the PROLOG system has been developed.

The Centre for Mathematics and Computer Science is a research institute of the Stichting Mathematisch Centrum, which was founded on February 11, 1946, as a nonprofit institution aiming at the promotion of mathematics, computer science, and their applications. It is sponsored by the Dutch Govern- ment through the Netherlands Organization for the Advancement of Research (N.W.O.).

The Ontolingua ontology development environment provides a suite of ontology authoring tools and a library of modular, reusable ontologies. The environment is available as a World Wide Web service and has a substantial user community. The tools in Ontolingua are oriented toward the authoring of ontologies by assembling and extending ontologies obtained from a library. In this paper, we describe Ontolingua's formalism for combining the axioms, definitions, and words (non-logical symbols) of multiple ontologies. We also describe Ontolingua's facilities that enable renaming of words from multiple component ontologies and that provide unambiguous mapping between words and text strings during input and output. These features support cyclic inclusion graphs and enable users to extend ontologies in multiple ways such as adding simplifying assumptions and extending the domains of polymorphic operators. ' By word, we mean non-logical symbols -the names of relations, functions, and object constants.

Abstract— Finding interesting patterns in time-oriented data can be useful in various domains. In time-oriented clinical data it helps in determining a diagnosis, prescribing therapy and browsing electronic patient records for management or research purpose. The paper ...

I would like to thank the following people and institutions who have helped me undertake this research: My supervisor Dr. wafaa faisal .M, for her enthusiasm for the study for her, support, encouragement and patience; consistent support and guidance during the works of this study. The bahri educational hospital, federal ministry of health, ahmed gasim hospital, their collaborative effort during data collection. Sudan university of science and technology / graduate studies for They gave me a chance to study with them in this solid educational edifice. Dear friends and family and to my parents, and university who set me off on the road to this MSc a long time ago.

In this work, we outline an approach to incrementally building knowledge-based systems based on tightly controlling the order of evaluation of the knowledge components of the system. The order of evaluation is based on two relations, sequence and correction, that correspond to two kinds of changes that an expert may wish to make to a knowledge base. A consistency notion based on past performance is also introduced. Knowledge base refinement is structured so that newly added knowledge has one of these relations with existing knowledge components in the system and the consistency condition is preserved. We further propose that the knowledge components added might be any knowledge-based systems or programs rather than rules. This proposal is a generalisation of the Ripple-Down Rule incremental approach to building knowledge-based systems.

The emergence of information technology has resulted in an ever-increasing demand to use computers for the efficient management and dissemination of information. Keeping in view the strong need of farmers to collect important and updated information for interactive, flexible and quick decision-making, a model of Decision Support System for Farm Management is developed. The paper discusses the use of Internet technology for the farmers to take decisions. A model is developed for the farmers to access online interactive and flexible information for their farm management. The workflow of the model is presented highlighting the information transfer between different modules.

Knowledge bases constitute the core of those Artificial Intelligence programs which have come to be known as Expert Systems. An examination of the most dominant knowledge representation schemes used in these systems reveals that a knowledge base can, and possibly should, be described at several levels using different schemes, including those traditionally used in operational databases. This chapter provides evidence that solutions to the organization and access problem for very large knowledge bases require the employment of appropriate database management methods, at least for the lowest level of description -the facts or data. We identify the database access requirements of knowledge-based or expert systems and then present four general architectural strategies for the design of expert systems that interact with databases, together with specific recommendations for their suitability in particular situations. An implementation of the most advanced and ambitious of these strategies is then discussed in some detail. Evidence for the successful application of Artificial Intelligence (AI) research is nowhere stronger than in the area of Knowledge-Based or Expert Systems (ES) cCLIF831. In addition to being among the first A1 systems which are finding their place in the commercial world, Expert Systems seem to lessen the controversies between differing A1 research approaches, and even contribute to wider overlaps of A1 with sciences like Philosophy or Cognitive Science. Although a formal theory of Expert Systems is yet to be developed, there are some key common features that can be identified. As a computer system attempting to act like a human expert in a limited application domain, an ES shares similar goals with other, more traditional, cmputer application systems. What differentiates an ES from these traditional systems are its overall architecture, and, usually, its method of development. Expert Systems are typically developed in an incremental way. A small group of about three people, filling the three roles of application domain expert, programmer, and knowledge engineer, successively refines the ES to approximate the behavior of the expert.

Car failure detection is a complicated process and requires high level of expertise. Any attempt of developing an expert system dealing with car failure detection has to overcome various difficulties. This paper describes a proposed knowledge-based system for car failure detection. The paper explains the need for an expert system and the some issues on developing knowledge-based systems, the car failure detection process and the difficulties involved in developing the system. The system structure and its components and their functions are described. The system has about 150 rules for different types of failures and causes. It can detect over 100 types of failures. The system has been tested and gave promising results.

This article examines the social and spatial organization of artisanal gemstone mining in Namibia, shedding light on a largely under-researched area. It delves into the power dynamics and experiences of precarity faced by miners, highlighting the associative strategies they employ daily. These strategies are shown to be responses to specific situations, shaped by various factors, with the miners' agency playing a significant yet not dominant role. Drawing on fieldwork conducted at two major artisanal mining sites in Namibia, Xoboxobos and Tubusis, the article utilizes semi-structured interviews and participant observation to propose a typology of associative modes, identifying the influential factors and organizational choices of the miners. By providing a comprehensive overview, this article paves the way for future research on the artisanal mining sector in Namibia, opening new dimensions and perspectives.

An important problem in the adaptive control of large flexible structures is to appropriately select the adaptive controller parameters so that good performance is obtained. There is a lack of systematic methods in selecting these parameters mainly because of the mathematical complexity in studying the transient response of nonlinear systems. In this paper a method. based on machine learning, to solve this problem is introduced and discussed. These results are then used to propose an intelligent adaptive control system where the parameters in the adaptive controller are to be tuned on-line without human supervision.

This chapter states and explains that a Learning Design is the result of a knowledge engineering process where knowledge and competencies, learning design and delivery models are constructed in an integrated framework. We present a general graphic al ...

The knowledge engineering world provides a rich source of software components for transforming formally expressed knowledge on a large scale, such as induction systems, knowledge base refiners and ontology merging tools. Although most of these systems have been designed as stand-alone components, there is interest in making them accessible on the Web, with the ultimate goal in mind that a knowledge engineer should be able, with a small amount of intellectual effort, to locate and assemble sequences of these components to perform complex transformations on large repositories of knowledge. The sorts of transformations used in knowledge engineering are not always trustworthy: some may not preserve the semantics of the knowledge transformed; some may not be able to perform a given transformation reliably under all circumstances. Therefore, it is crucial to have ways of inspecting the key properties we expect to be preserved by each transformational component and of describing how these properties change as new transformations are applied. We present initial experiments on a large-scale knowledge engineering problem and show how an abstract characterisation of knowledge-transformation steps, accompanied by a customisable editor, can allow a high degree of automation in this task. With such an editor we can analyse and represent sequences of general transformation steps and check if properties such as subsumption, completeness and soundness are preserved during different stages of the transformation, by analysing the structure of these sequences.