Knowledge Modeling

Creating knowledge requires the existence of a person or group of people who come up with new ideas, new concepts, innovative product or process, etc.. Knowledge creation can be achieved through research, innovation projects, experiments, observations etc. Firestone suggests that knowledge production begins with the request of knowledge, followed by individual or group learning, information acquisition, application for evaluation of knowledge and ultimately, build organizational knowledge. According to Nonaka [4], organizational knowledge creation and conversion is based on two dimensions. The first dimension shows that only individuals create knowledge. The second dimension relates to the interaction between explicit and tacit knowledge. These two dimensions form the basis for defining the four processes of creation / conversion of knowledge -socialization, externalization, combination and internalization.

Peer-to-Peer systems are a new paradigm for information sharing and some systems have successfully been deployed. It has been argued that current Peer-to-Peer systems suffer from the lack of semantics. The SWAP project (Semantic Web and Peer-to-Peer) aims at overcoming this problem by combining the Peer-to-Peer paradigm with Semantic Web technologies. In the course of our investigations it turned out that the nature of Peer-to-Peer systems requires some compromises with respect to the use of semantic knowledge models. In particular, the notion of ontology does not really apply as we often do not find a shared understanding of the domain. In this paper, we propose a data model for encoding semantic information that combines features of ontology (concept hierarchies, relational structures) with a flexible description and rating model that allows us to handle heterogeneous and even contradictory views on the domain of interest. We discuss the role of this model in the SWAP environment and describe the model as well as its creation and access.

Peer-to-Peer systems are a new paradigm for information sharing and some systems have successfully been deployed. It has been argued that current Peer-to-Peer systems suffer from the lack of semantics. The SWAP project (Semantic Web and Peer-to-Peer) aims at overcoming this problem by combining the Peer-to-Peer paradigm with Semantic Web technologies. In the course of our investigations it turned out that the nature of Peer-to-Peer systems requires some compromises with respect to the use of semantic knowledge models. In particular, the notion of ontology does not really apply as we often do not find a shared understanding of the domain. In this paper, we propose a data model for encoding semantic information that combines features of ontology (concept hierarchies, relational structures) with a flexible description and rating model that allows us to handle heterogeneous and even contradictory views on the domain of interest. We discuss the role of this model in the SWAP environment and describe the model as well as its creation and access.

Performing exercises in a simulation-based environment is a convenient and cost-effective way of learning spatial tasks. However, training systems that offer such environments lack models for the assessment of learner's spatial representations and skills, which would allow the automatic generation of customized training scenarios and assistance. Our proposal aims at filling this gap by extending a model for representing learner's cognitive processes in tutoring systems, based on findings from research on spatial cognition. This article describes how the model is applied to represent knowledge handled in complex and demanding tasks, namely, the manipulation of the robotic arm Canadarm2, and, more specifically, how a training system for Canadarm2 manipulation benefits from this model, both by its ability to assess spatial representations and skills and to generate customized assistance and exercises.

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.

The availability of high-resolution satellite images allowed new applications, especially in urban areas. In Brazil, where up-to-date information is frequently missing in data banks at Municipal, State and Federal level, the information obtained from these images could eventually fill this gap. In this paper, a case study, performed in the city of São José dos Campos, São Paulo State, Brazil is presented. A QUICKBIRD scene was classified using an object-oriented approach. After presenting the fundamentals of this new approach, a land cover map of a section from this city is presented and discussed. This is a contribution for urban planning. 1.

In this work, we propose an improved approach of time series data discretization using the Relative Frequency and K-nearest Neighbor functions called the RFknn method. The main idea of the method is to improve the process of determining the sufficient number of intervals for discretization of time series data. The proposed approach improved the time series data representation by integrating it with the Piecewise Aggregate Approximation (PAA) and the Symbolic Aggregate Approximation (SAX) representation. The intervals are represented as a symbol and can ensure efficient mining process where better knowledge model can be obtained without major loss of knowledge. The basic idea is not to minimize or maximize the number of intervals of the temporal patterns over their class labels. The performance of RFknn is evaluated using 22 temporal datasets and compared to the original time series discretization SAX method with similar representation. We show that RFknn can improve representation preciseness without losing symbolic nature of the original SAX representation. The experimental results showed that RFknn gives better term of representation with lower and comparable error rates.

It is our great pleasure, as guest editors, to present an issue with invited articles by authors of high-impact articles from the journal's past. By the end of 2015, the International Journal of Artificial Intelligence in Education completed its 25th volume and its 25th year of operation. This milestone called for a celebration of a scholarly kind. The current issue is part of that celebration. Although several landmark publications in the field appeared long before the journal's first year of publication (1989), the journal's early years coincided with the coalescing of the research community in Artificial Intelligence and Education (AIED). The same period saw the start of two relevant conferences, the International Conference on AI in Education (first held in 1987) and Intelligent Tutoring Systems (first held in 1988), although there had been two AI and Education conferences in the UK prior to

The vision of the Social Semantic Desktop defines a user's personal information environment as a source and end-point of the Semantic Web: Knowledge workers comprehensively express their information and data with respect to their own conceptualizations. Semantic Web languages and protocols are used to formalize these conceptualizations and for coordinating local and global information access. From the way this vision is being pursued in the NEPOMUK project, we identified several requirements and research questions with respect to knowledge representation. In addition to the general question of the expressivity needed in such a scenario, two main challenges come into focus: i) How can we cope with the heterogeneity of knowledge models and ontologies, esp. multiple knowledge modules with potentially different interpretations? ii) How can we support the tailoring of ontologies towards different needs in various exploiting applications? In this paper, we present NRL, an approach to these two question that is based on named graphs for the modularization aspect and a view concept for the tailoring of ontologies. This view concept turned out to be of additional value, as it also provides a mechanism to impose different semantics on the same syntactical structure. We think that the elements of our approach are not only adequate for the semantic desktop scenario, but are also of importance as building blocks for the general Semantic Web. The very core idea of the Social Semantic Desktop is to enable data interoperability on the personal desktop based on Semantic Web standards and technologies, e. g., ontologies and semantic metadata. The vision [6] aims at integrated personal information management as well as at information distribution and collaboration, envisioning two expansion states: i) the Personal Semantic Desktop for personal information management and later ii) the Social Semantic Desktop for distributed information management and social community aspects.

Metadata for the World Wide Web is important, but metadata for Peer-to-Peer (P2P) networks is absolutely crucial. In this paper we discuss the open source project Edutella which builds upon metadata standards defined for the WWW and aims to provide an RDF-based metadata infrastructure for P2P applications, building on the recently announced JXTA Framework. We describe the goals and main services this infrastructure will provide and the architecture to connect Edutella Peers based on exchange of RDF metadata. As the query service is one of the core services of Edutella, upon which other services are built, we specify in detail the Edutella Common Data Model (ECDM) as basis for the Edutella query exchange language and format implementing distributed queries over the Edutella network. Finally, we shortly discuss registration and mediation services, and introduce the prototype and application scenario for our current Edutella aware peers.

Perceptions of mentors' practices related to primary science teaching from nine Australian universities (n=331 final year preservice teachers) were gathered through a literature-based instrument. Five factors that characterise effective mentoring practices in primary science teaching were supported by confirmatory factory analysis. These factors, namely, personal attributes, system requirements, pedagogical knowledge, modelling, and feedback had Cronbach alpha coefficients of internal consistency reliability of . 93, .76, .94, .95, and .92 respectively. Final model fit indices were χ 2 =1335, df=513, CMIDF=2.60, IFI=.922, CFI=.921, RMR=.066, RMSEA=.070 (p<.001). Specific mentoring interventions for improving primary science teaching practices may be implemented by measuring preservice teachers' perceptions of their mentoring with a valid and reliable instrument.

The technology for building large knowledge bases (KBs) is yet to witness a breakthrough so that a KB can be constructed by the assembly of prefabricated knowledge components. Knowledge components include both pieces of domain knowledge (for example, theories of economics or fault diagnosis) and KB tools (for example, editors and theorem provers). Most of the current KB development tools can only manipulate knowledge residing in the knowledge representation system (KRS) for which the tools were originally developed. Open Knowledge Base Connectivity (OKBC) is an application programming interface for accessing KRSs, and was developed to enable the construction of reusable KB tools. OKBC improves upon its predecessor, the Generic Frame Protocol (GFP), in several significant ways. OKBC can be used with a much larger range of systems because its knowledge model supports an assertional view of a KRS. OKBC provides an explicit treatment of entities that are not frames, and it has a much better way of controlling inference and specifying default values. OKBC can be used on practically any platform because it supports network transparency and has implementations for multiple programming languages. In this paper, we discuss technical design issues faced in the development of OKBC, highlight how OKBC improves upon GFP, and report on practical experiences in using it.

In this paper the underlying knowledge model and architecture of I-PETER (Intelligent Personalised English Tutoring EnviRonment) are presented. This system has been designed for the on-line distance learning of English where too many students restrict the teacher's possibilities to provide individualised guidance. I-PETER is made up of four domain models that represent linguistic and didactic knowledge: the conceptual framework related to linguistic levels and knowledge stages, and the educational content and study strategies. The student model represents the knowledge that the student has learnt, the study strategies, and his/her profile. A student's command of English is evaluated by interpreting his/her performance on specific linguistic units in terms of three related criteria, rather than by a general linguistic competence ranking. Evaluation consists of a diagnostic task model which assesses student performance, taking the form of a Bayesian network, and a selection mechanism that proposes appropriate materials and study strategies.

This paper is based on the survey of various clinicalautomated systems based on the tasks of temporalabstraction and temporal-maintenance. Further, we illustrate the integration of above two tasks using temporal mediators and will show how temporal mediators help in managing and maintaining temporalclinical databases.

Content management is the process of handling information within an organization or community. Therefore, content management systems have to provide generic functionality for generation, extraction, storage, and exchange of digital assets. Because of the heterogeneity and complexity of content, a sufficient semantical and user-oriented annotation
of content is crucial. Although semantical annotation by metadata and ontologies together with reasoning support has been extensively studied for a long time, commercially available content management systems provide only basic support for semantic modelling. Conceptual aspects of content users and support of user specific intensions are neglected. In this paper collection we will analyze the mismatch between the requirements of content management and semantical description and propose a data model for content which treats semantic information not only as describing metadata but incorporates the data itself, the intension behind the data, the usage of data and the origin of data on the same level.

This paper proposes a knowledge representation method that supports greater reuse of manufacturing knowledge in design. The method draws on recent research into objectoriented product and manufacturing models, and problem solving agents. A research platform is proposed, and the results of a test case (based on a simplified jet engine combustion chamber) are described. The paper concludes with three basic principles of reuse, i.e. product/process separation, procedural/declarative knowledge separation, and guidelines for the optimum location of rules and constraints within product/manufacturing models.

Knowledge constantly grows in scientific discourse and is revised over time by domain experts. The body of knowledge will get structured and refined as the Communities of Practice concerned with the field of knowledge develop a deeper understanding of issues. The knowledge model, as a result evolves to a new state to accommodate the new knowledge. Keeping trail of these changes in semantically rich and formally sound mechanism, has pragmatic advantages for providing the undo and redo facility and recover to a previous state of the knowledge body (i.e. ontology). In this research, we have developed and tested comprehensive methodological framework for Change Tracer. The ontology changes are captured and then stored in Change History Log (CHL) in conformance to Change History Ontology (CHO). The CHL is later used for reverting ontology to a previous consistent state and visualization of change effects on ontology. The system is compared with ChangesTab of Protégé, a comprehensive evaluation of the accuracy of rollback and roll-forward algorithm has been conducted over Documentation ontology. The system is also tested over a standard dataset of OMV and high accuracy results are observed for both roll-back and roll-forward algorithms.

We report a strategy of collaborative knowledge modeling between experts and novices implemented in a Canadian organization since 2002 to support the transfer of expertise and knowledge management in this organization. Participants use an object-typed knowledge modeling editor called MOT to elaborate knowledge models in pairs. A knowledge model is similar to a concept map, except that it is based on a typology of knowledge objects and on a typology of links, and its structure is not necessarily hierarchical. This technique is used to represent concepts, principles, procedures and facts related to a specific aspect of the work done by employees in the organization. The paper presents the rationale behind this project, describes how it is implemented and identifies some research issues.

Please, cite this publication as: Lundgren-Cayrol, K., Paquette, G., Marino, O., Léonard, M., & de la Teja, I. (2006). Learning Design Repositories – Structure Ontology and Processes. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria: TENCompetence. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

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 ...

This chapter discusses how to build IMS learning designs focusing on three aspects, instructional engineering, modeling tools and graphical design techniques. First, we propose that instructional designers use a systemic and systematic instructional engineering method to build Units of Learning conforming to the IMS-LD specification. MISA, a mature instructional engineering method will serve as the basis to our design approach. Second, we present a graphical modeling tool, MOT+, and a representation technique that was created to support instructional engineering. In MOT+, concepts, procedures and principles are used to describe all IMS-LD components as well as their relationships. We believe this graphical language to be closer to instructional designers, in that it represents a more pedagogical viewpoint than software engineering graphical languages like UML, while still enabling an automatic translation from graphical models into a machine-readable IMS-LD XML. Third, we will provide an example of the design processes involved in building learning designs, from the preliminary analysis to the definition of a unit of learning method, the central part of the IMS Learning Design.

Domain ontologies that can solve various tasks using its concepts and determine fault reason for students’ answers may serve as a good basis for creating for a testing system with extensive explanatory abilities. But capabilities of modern ontological reasoners may not be enough for this kind of task. In this study, we developed and tested an ontology able to build execution trace for the given algorithm containing sequences and alternatives and find fault reasons for incorrect traces. The study also showed problems with the used reasoner that hinder the developed ontology from becoming fully effective.

Summary Objectives To review the issues that have arisen with the advent of translational research in terms of integration of data and knowledge, and survey current efforts to address these issues. MethodsUsing examples form the biomedical literature, we identified new trends in biomedical research and their impact on bioinformatics. We analyzed the requirements for effective knowledge repositories and studied issues in the integration of biomedical knowledge. Results New diagnostic and therapeutic approaches based on gene expression patterns have brought about new issues in the statistical analysis of data, and new workflows are needed are needed to support translational research. Interoperable data repositories based on standard annotations, infrastructures and services are needed to support the pooling and meta-analysis of data, as well as their comparison to earlier experiments. High-quality, integrated ontologies and knowledge bases serve as a source of prior knowledge used in ...

Ahstract: Well Founded Semantics is shown adequate to capture hypothetical reasoning if we interpret the Well Founded model of a program P as a (possihly incomplete) view of the world. Thus the Well Founded model may he accepted as a partially definite view of the world, and the other extended stahle models as alternative extended consistent views of the world. The original contrihutions of this paper are: showing that Well Founded Semantics is useful for representing hypothetical reasoning prohlems; to present a framework for ...

When ignorance due to the lack of knowledge, modeled as epistemic uncertainty using Dempster-Shafer structures on closed intervals, is present in the model parameters, a new uncertainty propagation method is necessary to propagate both aleatory and epistemic uncertainty. The new framework proposed here, combines both epistemic and aleatory uncertainty into a second-order uncertainty representation which is propagated through a dynamic system driven by white noise. First, a finite parametrization is chosen to model the aleatory uncertainty by choosing a representative approximation to the probability density function conditioned on epistemic variables. The epistemic uncertainty is then propagated through the moment evolution equations of the conditional probability density function. This way we are able to model the ignorance when the knowledge about the system is incomplete. The output of the system is a Dempster-Shafer structure on sets of cumulative distributions which can be combined using different rules of combination and eventually transformed into a singleton cumulative distribution function using Smets' pignistic transformation when decision making is needed.

FrameNet Project is being developed by ICSI at Berkeley, with the goal of documenting the English language lexicon based on Frame Semantics. For Brazilian Portuguese, the FrameNet-Br Project, hosted at UFJF, follows the same theoretical and methodological perspective. This work presents a service-based infrastructure that combines Semantic Web technologies with FrameNet-like databases, by considering the hypothesis that the application of technologies such as ontologies, linked data, and web services can contribute to build and reuse lexical resources based on Frame Semantics. The contributions are related to enriched semantics, data reliability and natural language processing.

Transferring expertise from highly qualified experts to nonexpert veterinarians and animal breeders is one of the Central Laboratory for Agricultural Expert Systems (CLAES) main objectives. CLAES used the knowledge based system technology to achieve this objective. Five animal knowledge based systems have been developed for diagnosis animal diseases and/or management farm. This paper presents the different methodologies used to develop these systems in Egypt. The used methodologies are: the hierarchical classification generic task, the case based reasoning and the commonKADS.

The main objective of this paper is to propose a new concept of an intelligent tutoring system based on the use of an ontology for knowledge representation. An ontology provides a specific body of knowledge in a formal, structured and precise form such that knowledge can be ...

INPE – Instituto Nacional de Pesquisas Espaciais, Caixa Postal 515, 12.245-970 São José dos Campos, SP, Brazil (*) hermann@ltid.inpe.br, (**) carolina@ltid.inpe.br ... KEY WORDS: Object-oriented classification, Quickbird satellite, Ikonos satellite, Remote Sensing, e-...

The consensus of the documentation literature is that users rarely use help, usually preferring to muddle through. To increase use of help, tutorials for novice users could be changed from guided presentations toward using the system’s actual help system. To determine whether this approach would increase users’ use of help when they encountered problems with an application, we developed an alternative, help-based tutorial introduction to Microsoft Publisher. We compared the behaviors of users introduced to Publisher with the help-based tutorial with the behaviors of users who learned from a traditional tutorial. A balanced study of 22 novice users of Publisher suggests that using a help-based tutorial leads to significantly greater use of help systems when users encounter problems. However, the data also suggest that the increased use of help may not lead to more effective task performance.

Analysis for Knowledge MOdeling n recent years there has been increasing interest in describing complicated information processing systems in terms of the knowledge they have, rather than by the details of their implementation. This requires a means of modeling the knowledge in a system. Several different approaches to knowledge modeling have been developed by researchers working in Artificial Intelligence (AI). Most of these approaches share the view that knowledge must be modeled with respect to a goal or task. In this article, we outline our modeling approach in terms of the notion of a task-structure, which recursively links a task to alternative methods and to their subtasks. Our emphasis is on the notion of modeling domain knowledge using tasks and methods as mediating concepts. We begin by tracing the development of a number of different knowledge-modeling approaches. These approaches share many features, but their differences make it difficult to compare systems that have been modeled using different approaches. We present these approaches and describe their similarities and differences. We then give a detailed description, based on the task structure, of our knowledge-modeling approach and illustrate it with task structures for diagnosis and design. Finally, we show how the task structure can be used to compare and unify the other approaches. ® ® ® ® ® ® ® ® • ® ® ® • ® ® Knowledge-Based Systems: What are they?

The worldwide increase in demand for qualified workers in science, technology, engineering, and mathematics (STEM) fields has resulted in a greater focus on preparing students to enroll in postsecondary STEM programs. The processes that lead students to become interested in and equip them for STEM careers begin years earlier. Previous research indicates that family background, financial resources, and prior family academic achievement can be used to predict whether a student will enroll in a STEM major. In this paper, we consider another class of factors that may be predictive while being more actionable. In this paper, we use predictive analytics, based on previously-validated automated detectors of student learning and engagement, to predict which students will choose a STEM major. With data from 363 college students who used ASSISTments during their regular middle school math classes, we develop a model that can successfully distinguish 66% of the time if a student will choose a STEM major or a non-STEM major when they enter college. In doing so, we offer steps towards providing educators with more actionable information on the STEM trajectories of individual students.

Past work concerning the design and implementation of voice-operated personal assistants that are easy to configure and apply without directly worrying about the sophistication of leveraging Large Language Models or Neural Networks has been conducted at Makerere University via the use of intelligent parsing of user voice queries against simple plain-text Question-Answer Knowledge Bases (QAKBs). This work was part of the VOSA project, and was successfully presented at SE2024 in Copenhagen. However, one glitch in this Artificial Assistant model design is that the accuracy and relevance of the answers provided by the robot are so tightly coupled to the literal phrasing and expression of answers (and alternative answers) in the underlying manually prepared QA entries that define/compose a knowledge model against which the bot can correctly respond to questions about a particular domain. In reality though, such an approach can become too limiting; the authors of a QAKB need to know/anticipate all/most possible ways a user might pose a question to the robot in order to correctly match an entry in the KB; handling of answers that need to contain dynamic information (such as date and time, location, prices, weather and such) isn't easily catered for by the flat, literal and static JSON-formatted QAKB structures used in VOSA-like assistants much as they retain the advantage of being simpler for humans to reason about or tweak than most current alternatives using human-opaque structures such as use of matrices of probabilities in Hidden Markov [Knowledge] Models (HMKMs) or use of thousands if not millions or billions of weights and activation functions in Neural Network Models (NNMs). With the recent emergence of a feature-complete general-purpose Text Processing language, TEA, which, by its lightweight nature could allow for easy integration into existing or future intelligent systems, an idea for how VOSA-like Artificial Assistants could be further enhanced in terms of their cognitive processing power and efficiency is by leveraging smarter QAKB models that also allow for some runtime/just-in-time reprogramming/generation of answers to suit user-context and perhaps even add multi-turn QA Processing that is currently missing in VOSA but which is somewhat necessary given the state-of-the-art in assistant chatbot technologies across the industry. So, how might this better QAKB technology be realized? One idea is to add a TEA program processor inside of the VOSA client engine/architecture, and then, to allow for designers or authors of VOSA-targeting QAKBs to extend their QA-entries with specially crafted, domain relevant mini-programs or perhaps QAKB-wide QA-preprocessing Macro Programs either of which are written using the succinct, terse but powerful text processing language of TEA, the Transforming Executable Alphabet. This then would allow for VOSAC to sometimes take a user's query and the active QAKB, and then dynamically process it relative to preprocessing instructions embedded in the QAKB, and thus compile new or better answers to user queries on-the-fly and in a manner perhaps more straightforward and more transparent than leveraging pre-trained models or merely falling back to over-the-web queries to a search engine as the VOSA paper had originally proposed. This research shall also usher in a whole new approach to designing and programming Artificial Intelligence personal assistants that's different from approaches leveraging HMKMs, NNMs or just basic QAKBs and/or ontologies. Consider this a research proposal abstract.

This chapter describes a model and an underlying theoretical framework for hybrid planning. Modern planning domain-description formalisms are based on purely sentential languages. Sentential representations produce problem encodings that often require the system to carry out an unnecessary amount of trivial deductions, preventing it from concentrating all the computational effort on the actual search for a plan and leading to a loss in performances. This chapter illustrates how techniques from the area of knowledge representation and reasoning can be adopted to develop more efficient domain-description languages. In particular, experimental evidence suggests that the adoption of analogical descriptions can lead to significant improvements in planning performance. Although often more efficient, however, analogical representations are generally less expressive than sentential ones. The hybrid approach proposed here provides a framework in which sentential and analogical descriptions can be integrated and used interchangeably, thereby overcoming the limitations and exploiting the advantages of both paradigms.

The effectiveness of tools that provide support for software development is highly dependent on the match between the tools and their task. Knowledge-acquisition (KA) tools constitute a class of development tools targeted at knowledge-based systems. Generally, KA tools that are custom-tailored for particular application domains are more effective than are general KA tools that cover a large class of domains. The high cost of custom-tailoring KA tools manually has encouraged researchers to develop recta-tools for KA tools. Current research issues in meta-tooZs for knowledge acquisition are the specification styles, or me,a-views, for target KA tools used, and the relationships between the specification entered in the metatool and other specifications for the target program under development. We examine different types of meta-views and meta-tools. Our current project is to provide meta-too!s that produce KA tools from multiple specification sources--for instance, from a task analysis of the target application. = :. _

This paper presents spatio-temporal multi-modality ontology for indexing and retrieving satellite images in the high level to improve the quality of the system retrieval and to perform semantic in the retrieval process. Our approach is based on three modules: (1) regions and features extraction, (2) ontological indexing and (3) semantic image retrieval. The first module allows extracting regions from the satellite image using the fuzzy c-means (FCM) segmentation algorithm. The second module allows indexing the satellite image by a spatio-temporal ontology which represents sensors, spatial objects, visual features (spectral signature, texture, shape, etc.), spatial relation between objects, temporal variation and semantic relation between terms. The last module allows retrieving satellite images by comparing the ontological model of the requested satellite image with the ontological model base. We adopted several algorithms for measuring the lexical and the structural similarity degree between ontological models. Our approach attempts to fully describe the semantic content of the satellite image and to perform the quality of the retrieval system.

Introduction Several parts of the World Heritage City of Timbuktu have been destroyed during the Malian civil war [whc.unesco.org, 13/06/2013]. In Italy's Emilia Romagna and Lombardy regions, a series of earthquakes and their aftershocks caused serious damage to the cultural heritage in May 2012 [UNESCO, 2012]. These two examples illustrate that both human and natural actions can in a short time destroy objects to which a historical, scientifi c or aesthetic value has been attributed for a long time. Therefore, accurate documentation of cultural heritage is of inestimable value [Santana, 2003]. Such efforts at the protection and conservation of cultural heritage are generally accepted as important for future generations [e.g. UNESCO, 1972; Jokelitho, 2008]. However, there is no shared understanding of what is meant by 'cultural heritage' [Jokelitho, 2008; González-Pérez & Parcero-Oubiña, 2012], since several defi nitions exist in literature and in national and international legislation. For example, Jokelitho [2008] observed an emerging diversity of 'things' conceptualized as cultural heritage. In addition, González-Pérez and Parcero-Oubiña [2012] described a set of principles that reoccur in the defi nitions. For a clear understanding, this paper combines the defi nitions of the UNESCO 1 World Heritage Convention [1972] and the ICOMOS 2 [www. icomos.org, 8/03/2013]. In these defi nitions, monuments, groups of buildings, cultural landscapes and archaeological sites are considered as cultural heritage if they are assigned a historical, artistic or scientifi c value. For all of these classes of cultural heritage, a precise documentation results not only in a more well-founded assessment of its conservation state, but also in a better understanding of its historical background [Santana, 2003]. Consequently, sharing heritage documentation can create an important knowledge transfer between policy makers, researchers (e.g. archaeologists) and the general public (e.g. tourists) [Shaw et al., 2009; McKeague et al., 2012]. However, the future reuse of heritage documentation, such as archaeological data and its derived interpretations, is often prevented because the data sets (i) contain sensitive information, (ii) are conceived as copyrighted or (iii) are simply forgotten [McKeague et al., 2012]. The fi rst two reasons are linked to the general 'Open Data' 3 discussion. The last issue (iii) can be tackled by applying a common data model, which facilitates the sharing of information [Shaw et al., 2009; McKeague et al., 2012]. Besides enhancing the data interoperability and exchange by encouraging the use of data models, the domain of cultural heritage can also reap the fruits from the increasing popularity of Geographical Information Systems (GIS). These systems may hold many advantages for the integration and analysis of cultural heritage information. This paper focusses on the current use of GIS and data models in cultural heritage. The remainder of this paper is organized as follows. Section 2 de

Intelligent Tutoring Systems are based on the knowledge-module that is holding the system's knowledge in a well-structured format. Considering the current state of the art knowledge-module representations, we lack a model that can represent evolving information. Representing evolving information is needed for those tutoring systems that are working with dynamically changing domains, e.g.: software science. In this paper a new combined model is presented that is based on the Ontology model and the fundamentals of Knowledge Space Theory. Our new model introduces the term of abstract time to be able to formulate an evolving knowledge graph. Our model also introduces the term of evoking-hooks that makes it possible to realize connections between external domain elements and our model's nodes.

The aim of this paper is to improve one of the limitations of the fuzzy models of business failure. In this sense an application to a group of small and medium –sized enterprises (SMEs) from the construction sector is introduced in a given time horizon to test the diagnosis model capacity to predict diseases. This simulation involves the adaptation of the methodological hypothesis, and the definition of the main variables that interact in the estimation. This research presents the estimation of the matrix of economic–financial knowledge and verifies its capacity of diagnosis. Also, a goodness measure, through approximate solutions, is introduced to test the model ́s functionality with companies outside initial sample to demonstrate its ability to predict. Therefore, through this simulation some of the fuzzy model ́s restrictions are overcome, the theoretical postulates are adapted; the difficulties associated with the estimation are solved and new criteria and guidelines that enrich...

A study was conducted to determine the effectiveness of using advance organizers in instruction at the graduate level. Participants were 218 graduate students in research methodology courses. Fifty-four students were enrolled in sections in which advance and post organizers were used; 164 were enrolled in sections in which this type of instruction did not take place. Conceptual knowledge, involving students' command of research concepts, methodologies, and applications, was measured individually in both sets of classes through midterm and final examinations. Findings show that students in the advance organizer sections of the course obtained higher levels of overall achievement than did their counterparts. The effect size pertaining to this difference was 0.54, which is considered moderate. The implications of these findings are discussed, as are recommendations for incorporating advance and post organizers in research methodology courses. (Contains 1 figure and 44 references.) (SLD) Reproductions supplied by EDRS are the best that can be made from the original document.

Methods to help efficiently digest and use information have become an important research area in resolving information overload. While automatic text summarization research demonstrates significant efforts in this regard, the processes are complex and results have poorer quality than those created manually by experts. The generated summary tends to be rigid as the systems are designed for specific domains and applications. Our Automatic Table of Content Developer system, based on the Probability of Co-occurrence Analysis (POCA) technique, creates outputs that are content dependent, and yet the system can adapt to all document domains. We thus propose the resultant table-of-contents (TOC) display as an alternative to help users digest a large document collection using less time and effort.

In this article we analyse the notion of knowledge role. First of all, we present how the relationship between problem solving methods and domain models is tackled in different approaches. We concentrate on how they cope with this issue in the knowledge engineering process. Secondly, we introduce several properties which can be used to analyse, characterise and define the notion of role. We evaluate and compare the works exposed previously following these dimensions. This analysis suggests some developments to better exploit the relationship between reasoning and domain knowledge. We present them in a last section.

Resolving the semantic heterogeneity in the semantic web requires finding correspondences between ontologies describing resources. In particular, with the explosive growth of data sets in the Linked Open Data, linking multiple vocabularies and ontologies simultaneously, known as holistic matching problem, becomes necessary. Currently, most state-of-the-art matching approaches are limited to pairwise matching. In this paper, we propose a holistic ontology matching approach that is modeled through a linear program extending the maximumweighted graph matching problem with linear constraints (cardinality, structural, and coherence constraints). Our approach guarantees the optimal solution with mostly coherent alignments. To evaluate our proposal, we discuss the results of experiments performed on the Conference track of the OAEI 2015, under both holistic and pairwise matching settings.

In conceptual design of engineering devices, a designer decomposes a required function into sub-functions, so-called functional decomposition, using a kind of functional knowledge representing achievement relations among functions. However, such knowledge about functionality of engineering devices is usually left implicit because each designer possesses it. Even if such knowledge is found in documents, it is often scattered around technical domains and lacks consistency. Aiming at capturing such functional knowledge explicitly and sharing it in design teams, we discuss its systematic description based on functional ontologies which provide common concepts for its consistent and generic description. We propose a new concept named 'way of achievement' as a key concept for capturing such functional knowledge. Categorization of typical representations of the knowledge and its organization as is-a hierarchies are also discussed. The generic concepts representing functionality of a device in the functional knowledge are provided by the functional concept ontology, which makes the functional knowledge consistent and applicable to other domains. We also discuss development of a design supporting system using the systematized knowledge, called a functional way server. It helps human designers redesign an existing engineering device by providing a wide range of alternative ways of achievement of the required function in a manner suitable for the viewpoint of each designer and then facilitates innovative design.

We present a stochastic greybox model of a BioDenitro WWTP that can be used for short time horizon Model Predictive Control. The model is based on a simplified ASM1 model and takes model uncertainty into account. It estimates unmeasured state variables in the system, e.g. the inlet concentration or the sensor measurements in case of temporary sensor faults. This improves control performance without adding additional or redundant sensors. We fitted the parameters of the model to actual plant data and demonstrate the state estimation capabilities with this data set. The model now runs online at a WWTP in Denmark.

The resource accessibility problem in a cooperative learning environment can be stated as how both teachers and students can gain access to resources (information and hardware devices) not only from remote locations, but also from the classroom. In this paper we present a task model (TMo) that describes the services for a Resource Management System (RMS) to solve such problem. The model illustrates how to: 1) manage course information, and 2) operate hardware devices. As a modelling methodology we have resorted to CommonKADS.

Artigo de reflexão que enfoca as origens e as principais caracterís-ticas dos modelos clínico e epide-miológico, seus nexos com os mo-delos assistenciais vigentes no sis-]tema de saúde brasileiro, e a cons-trução do conhecimento da enfer-magem nesse contexto. Nessa perspectiva, as autoras propõem um repensar acerca do saber/fazer da enfermagem, apontando possibilidades de expansão do seu campo de atuação, bem como dos limites e desafios a serem venci-dos pelos profissionais da área.