Knowledge Representation Approach Research Articles

Representation of functional micro-knowledge cell (FMKC) for conceptual design

The conceptual design process of a complex product concerns multidisciplinary design knowledge. This paper presents an approach of consistent knowledge representation for conceptual design. Firstly, the concept of functional micro-knowledge cell (FMKC) is presented, and the knowledge representation approach of FMKC is proposed. In the function layer, the functional ontology is used to provide a rich vocabulary. By constructing the mapping relationships between the function and structure layer, a systematic knowledge representation scheme is obtained. Secondly, the functional knowledge decomposition theory is proposed to unify the resolution of knowledge representation. Finally, the FMKC is applied to a hydraulic cylinder, which demonstrates the possibility in representing the multidisciplinary knowledge for conceptual design with a unified systematic representation scheme. In addition, the FMKC can be used in knowledge fusion and design reuse for our further study.

Graph-Based Analysis of Nuclear Smuggling Data

Much of the data that is collected and analyzed today is structural, consisting not only of entities but also of relationships between the entities. As a result, analysis applications rely on automated structural data mining approaches to find patterns and concepts of interest. This ability to analyze structural data has become a particular challenge in many security-related domains. In these domains, focusing on the relationships between entities in the data is critical to detect important underlying patterns. In this study we apply structural data mining techniques to automate analysis of nuclear smuggling data. In particular, we choose to model the data as a graph and use graph-based relational learning to identify patterns and concepts of interest in the data. In this article, we identify the analysis questions that are of importance to security analysts and describe the knowledge representation and data mining approach that we adopt for this challenge. We analyze the results using the Russian nuclear smuggling event database.

A state-based knowledge representation approach for information logical inconsistency detection in warning systems

Detecting logical inconsistency in collected information is a vital function when deploying a knowledge-based warning system to monitor a specific application domain for the reason that logical inconsistency is often hidden from seemingly consistent information and may lead to unexpected results. Existing logical inconsistency detection methods usually focus on information stored in a knowledge base by using a well-defined general purpose knowledge representation approach, and therefore cannot fulfill the demands of a domain-specific situation. This paper first proposes a state-based knowledge representation approach, in which domain-specific knowledge is expressed by combinations of the relevant objects’ states. Based on this approach, a method for information logical inconsistency detection (ILID) is developed which can flexibly handle the demands of various domain-specific situations through reducing part of restrictions in existing methods. Finally, two real-case based examples are presented to illustrate the ILID method and its advantages.

On integrating event definition and event detection

We develop, in this paper, a representation of time and events that supports a range of reasoning tasks such as monitoring and detection of event patterns which may facilitate the explanation of root cause(s) of faults. We shall compare two approaches to event definition: the active database approach in which events are defined in terms of the conditions for their detection at an instant, and the knowledge representation approach in which events are defined in terms of the conditions for their occurrence over an interval. We shall show the shortcomings of the former definition and employ a three-valued temporal first order nonmonotonic logic, extended with events, in order to integrate both definitions.

Ontologically-based approach for Knowledge Representation for Stamping Die Applications

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

FCANN: A new approach for extraction and representation of knowledge from ANN trained via Formal Concept Analysis

Nowadays, Artificial Neural Networks (ANN) are being widely used in the representation of different systems and physics processes. Once trained, the networks are capable of dealing with operational conditions not seen during the training process, keeping tolerable errors in their responses. However, humans cannot assimilate the knowledge kept by those networks, since such implicit knowledge is difficult to be extracted. In this work, Formal Concept Analysis (FCA) is being used in order to extract and represent knowledge from previously trained ANN. The new FCANN approach permits to obtain a complete canonical base, non-redundant and with minimum implications, which qualitatively describes the process being studied. The approach proposed has a sequence of steps such as the generation of a synthetic dataset. The variation of data number per parameter and the discretization interval number are adjustment factors to obtain more representative rules without the necessity of retraining the network. The FCANN method is not a classifier itself as other methods for rule extraction; this approach can be used to describe and understand the relationship among the process parameters through implication rules. Comparisons of FCANN with C4.5 and TREPAN algorithms are made to show its features and efficacy. Applications of the FCANN method for real world problems are presented as case studies.

On Scaffolding Adaptive Teaching Prompts Within Virtual Labs

Despite a growing development of virtual laboratories which use the advantages of multimedia and Internet for distance education, learning by means of such tutorial tools would be more effective if they were specifically tailored to each student needs. The virtual teaching process would be well adapted if an artificial tutor can identify the correct acquired knowledge, recognise the erroneous learner’s knowledge and suggest a suitable sequence of pedagogical activities to improve the level of the student. This paper proposes a knowledge representation model which judiciously serves the remediation process to students’ errors during e-learning activities. The model is inspired by recent researches on computational representation of the knowledge and by cognitive psychology theories that offer a refined modelling of the human learning processes. Experimental results, obtained via practical tests, show that the knowledge representation and remediation approach facilitates the planning of tailored sequences of feedbacks that considerably help the learner.

On Pose Estimation for Human-Robot Symbiosis

This paper presents a vision based pose estimation system using knowledge based approach for human-robot symbiosis. The system is based on visual information of the face by connected component analysis of the skin color segmentation of images in HSV color model and is commenced with the face recognition and pose classification scheme using subspace PCA based pattern-matching strategies. With the knowledge of the known user's profile, face poses are then classified by multilayer perceptron. Based on the frame-based knowledge representation approach, face poses are being interpreted using the Software Platform for Agent and Knowledge (SPAK) management. On face pose recognition, robot is then instructed to perform some specific tasks by issuing pose commands. Experimental results demonstrate that the subspace method is better than that of the standard PCA method for face pose classification. The system has been demonstrated with the implementation of the algorithm to interact with an entertainment robot named, AIBO for human-robot symbiotic relationship.

AURELLIO

Encouraging results of the last few years in the field of knowledge representation within virtual learning environments confirm that artificial intelligence research in this topic finds it very beneficial to integrate the knowledge that psychological research has accumulated on understanding the cognitive mechanism of human learning and all the positive results obtained in computational modeling theories. This article introduces a novel cognitive and computational knowledge representation approach inspired by cognitive theories that explain the human cognitive activity in terms of memory subsystems and their processes, and whose aim is to suggest formal computational models of knowledge that offer efficient and expressive representation structures for virtual learning. Practical studies both contribute to validate the novel approach and permit to draw general conclusions.

Modelling of manufacturing activities by process planning knowledge representation

Process planning is one of the key activities for product design and manufacturing. Impact of process plans on all phases of product design and manufacture requires high level of interaction of different activities and tight integration of them into coherent system. In this paper we describe a model for manufacturing activities that allows integration. The framework for integration is briefly described and the manufacturing process model that considers three dimensions of planning is explained. Than an object-oriented knowledge representation approach is presented with module for parts modeling and module for generation of process plan. Description of machining process entities and their relationships with features, machines and tools are provided. The benefits of the proposed representation, which include connection with geometric model, reduced search space and alternative plan generation, are discussed. These new contributions provide for a new generation of CAPP systems that can be adapted for various manufacturing systems and can be integrated with other CIM modules. (Received in October 2005, accepted in February 2006. This paper was with the authors 2 months for 2 revisions.)

Semantic Interoperability

To meet the challenge for high quality and efficient care, highly specialized and distributed healthcare establishments have to communicate and co-operate in a semantically interoperable way. Information and communication technology must be open, flexible, scalable, knowledge-based and service-oriented as well as secure and safe. For enabling semantic interoperability, a unified process for defining and implementing the architecture, i.e. structure and functions of the cooperating systems' components, as well as the approach for knowledge representation, i.e. the used information and its interpretation, algorithms, etc. have to be defined in a harmonized way. Deploying the Generic Component Model, systems and their components, underlying concepts and applied constraints must be formally modeled, strictly separating platform-independent from platform-specific models. As HL7 Version 3 claims to represent the most successful standard for semantic interoperability, HL7 has been analyzed regarding the requirements for model-driven, service-oriented design of semantic interoperable information systems, thereby moving from a communication to an architecture paradigm. The approach is compared with advanced architectural approaches for information systems such as OMG's CORBA 3 or EHR systems such as GEHR/openEHR and CEN EN 13606 Electronic Health Record Communication. HL7 Version 3 is maturing towards an architectural approach for semantic interoperability. Despite current differences, there is a close collaboration between the teams involved guaranteeing a convergence between competing approaches.

Improving National and Homeland Security through Context Knowledge Representation & Reasoning Technologies

In the aftermath of the 9/11 tragedy it has became clear that the lack of effective information exchange among government agencies hindered the capability of identifying potential threats and preventing terrorism actions. It has been noted by the National Research Council that Although there are many private and public databases that contain information potentially relevant to counterterrorism programs, they lack the necessary context definitions (i.e., metadata) and access tools to enable interoperation with other databases and the extraction of meaningful and timely information1. This report clearly recognized the important problem that the semantic data integration research community has been studying. In this chapter, we describe the Laboratory for Information Globalization and Harmonization Technologies (LIGHT) developed at MIT. LIGHT arises from previous research, most notably the COntext INterchange (COIN) context mediation technology and the Global System for Sustainable Development (GSSD). Context Mediation technology addresses the above problem and deals directly with the integration of heterogeneous contexts (i.e. data meaning) in a flexible, scalable and extensible environment. This approach makes it easier and more transparent for receivers (e.g., applications, sensors, users) to exploit distributed sources (e.g., databases, web, information repositories, sensors). In this paper we define context as the assumptions of the source and receiver that affect correct interpretation of the meaning of the information. Receivers are able to specify their desired context so that there will be no uncertainty in the interpretation of the information coming from heterogeneous sources. The COIN context knowledge representation approach and associated reasoning tools significantly reduce the overhead involved in the integration of multiple sources and simplifies maintenance in an environment of changing source and receiver context. This technology is essential in the counter-terrorism environment in a number of areas including: (1) allowing for receivers (i.e., applications, analysts) to have multiple views of the same data (e.g., different semantic assumptions - two analysts may have a different meaning for Soviet Union depending on the application), (2) allowing for the collection of information into a single data warehouse, and (3) use in a dynamic federated environment where applications may have changing contexts and sources are added and removed from the grid. This approach is essential to the agile integration of information to support counter terrorism.

An expert system on site selection of sanitary landfill

It is desirable to encapsulate heuristic and empirical knowledge including hydrological and bio-geochemical considerations into the selection process of a potential landfill site. This paper delineates a prototype expert system, which has been developed using a commercially available microcomputer-based expert system shell VISUAL RULE STUDIO, for the selection of a landfill site. VISUAL RULE STUDIO acts as an ActiveX Designer under the Microsoft Visual Basic programming environment, with the hybrid knowledge representation approach under object-oriented design environment. The blackboard architecture, which is able to group all the knowledge together into an integrated system effectively, is adopted. The evaluation is based on the hazardous waste site ranking system recommended by the US Environmental Protection Agency, adapted to Hong Kong conditions. Increase in efficiency, improvement, consistency of results and automated record keeping are among the advantages of such a system.

Prototype expert system for site selection of a sanitary landfill

It is desirable to incorporate heuristic and empirical knowledge including hydrological and bio-geochemical considerations into the selection process of a potential landfill site. In this article, a prototype expert system for the selection of a landfill site, with hybrid knowledge representation approach under object-oriented design environment in a blackboard architecture, is described. It incorporates an artificial neural network for training of partial hazardous scores and a fuzzy rule base for the representation of heuristic knowledge. The evaluation is based on the hazardous waste site ranking system recommended by the US Environmental Protection Agency, adapted to Hong Kong conditions by incorporating the stipulation of some local regulations. It is shown to be a useful aid in assisting novice engineers in the selection process of a potential landfill site during preliminary investigation.

Guia de Apoio à Decisão em Enfermagem Obstétrica: aplicação da técnica da Lógica Fuzzy

A Lógica Fuzzy tem sido utilizada como uma forma de representação de conhecimento e uma técnica para a modelagem de Sistemas de Apoio à Decisões Clínicas. Ao considerar a pouca utilização desta técnica para modelar decisões clínicas de enfermagem, este ensaio objetiva apresentar noções gerais sobre esta técnica e por meio dela desenvolver uma formulação teórica, em forma de guia prático, para o apoio à decisão nos casos de amniotomia em gestantes pimíparas em trabalho de parto normal.

Automated inspection process planning: Algorithmic inspection feature recognition, and inspection case representation for CBR

General metrological inspection planning is among the least explored computer-aided process planning (CAPP) domains. This paper explores certain basic issues involved in inspection planning using case-based reasoning in an environment of a Generic CAPP Support System. Firstly, algorithmic methods for characterizing and extracting inspection features are proposed and discussed. A sequential knowledge based filtering method is developed to reduce the number of inspection features typically encountered in metrological inspection planning. Finally, a formalized approach for case representation of relevant inspection domain knowledge using a newly developed parametric-list technological feature graph (PLTFG) is presented.

Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems

In this paper, we first present and compare existing categorization schemes for neuro-symbolic approaches. We then stress the point that not all hybrid neuro-symbolic approaches can be accommodated by existing categories. Such a case is rule-based ne

Formalizations of Commonsense Psychology

■ The central challenge in commonsense knowledge representation research is to develop content theories that achieve a high degree of both competency and coverage. We describe a new methodology for constructing formal theories in commonsense knowledge domains that complements traditional knowledge representation approaches by first addressing issues of coverage. We show how a close examination of a very general task (strategic planning) leads to a catalog of the concepts and facts that must be encoded for general commonsense reasoning. These concepts are sorted into a manageable number of coherent domains, one of which is the representational area of commonsense human memory. We then elaborate on these concepts using textual corpus-analysis techniques, where the conceptual distinctions made in natural language are used to improve the definitions of the concepts that should be expressible in our formal theories. These representational areas are then analyzed using more traditional knowledge representation techniques, as demonstrated in this article by our treatment of commonsense human memory.

An invariant scheme for exact match retrieval of symbolic images: Triangular spatial relationship based approach

In this paper, a novel method of representing symbolic images in a symbolic image database (SID) invariant to image transformations, useful for exact match retrieval is presented. The proposed model is based on Triangular Spatial Relationship (TSR) [Guru, D.S., Nagabhushan, P., 2001. Triangular spatial relationship: A new approach for spatial knowledge representation, Pattern Recognition Lett. 22, 999–1006]. The proposed model preserves TSR among the components in a symbolic image by the use of quadruples. A distinct and unique key called TSR key is computed for each distinct quadruple. The mean and standard deviation of the set of TSR keys computed for a symbolic image are stored along with the total number of TSR keys as the representatives of the symbolic image. An exact match retrieval scheme based on the modified binary search technique [Guru, D.S., Raghavendra, H.J., Suraj, M.G., 2000. An adaptive binary search based sorting by insertion: An efficient and simple algorithm, Statist. Appl., 2, 85–96] is also presented in this paper. The presented retrieval scheme requires O(log n) search time in the worst case, where n is the total number of symbolic images in the SID. An extensive experimentation on a large database of 13,680 symbolic images is conducted to corroborate the superiority of the model.

A Logic for SVG Documents Query and Retrieval

We propose a knowledge representation approach to the semantic retrieval by content of graphics described in Scalable Vector Graphics (SVG), the novel XML based W3C approved standard language for describing two-dimensional graphics. The approach is based on a description logic devised for the semantic indexing and retrieval of complex objects. We provide a syntax to describe basic shapes, complex objects as compositions of basic ones, and transformations. An extensional semantics, which is compositional, is introduced for defining retrieval, classification, and subsumption services. Algorithms exploiting reasoning services, which are sound with respect to the semantics, are also described. Using our logical approach as a formal specification, we implemented a prototype system. A set of experiments, carried out on a testbed of SVG documents to assess the retrieval capabilities of the system, is presented.