Schema-based Representation Research Articles

3D topological relationships of landforms and their spatial schema-based representation

The science of geomorphology works on natural 3D landforms. Research includes the change of landforms as well as the processes causing these changes. Material transport processes lead to a composition of a geomorphic system that follows a certain spatial hierarchy. The analysis of 3D topological relations of landforms can help to investigate geomorphic systems in two ways. First, chronological order of geomorphic genesis can be derived and, second, indications of material source can be found. However, at least some 3D geometric information is needed if topology is supposed to be derived and examined. Landforms cannot simply be reconstructed by surface measurements. Data capture is a major problem when buried features are under investigation. Subsurface information is gathered by drillings or geophysical methods that reveal point or line information. Unfortunately, the ISO 19107 Spatial Schema does not offer a valid representation of 3D geometry from sparse data, either by aggregating a surface and one or few points or by aggregating a surface and a line. Here, we discuss the possibilities for the analysis of chronological order of landform genesis and material dependencies that arise from applying 3D topological relationships to geomorphic system analysis. We show five relationships that are able to be observed in nature. Further, we introduce a new class for the representation of 3D objects with under-specified geometry. A _UG_Solid mediates between the Spatial Schema’s geometric primitives with a dimension less than three on the one side and a GM_Solid on the other side. Constraints to aggregate such a _UG_Solid are defined. The introduction of a _UG_Solid facilitates the application of 3D topological concepts to geometric objects that are known to be volumetric but have to be modeled from sparse data.

Semantic web technologies for enterprise application integration

Large industrial interoperability projects use syntax-based Enterprise Application Integration standards, such as XML Schema, to accomplish interoperable data exchange among enterprise applications. In this paper, we describe an approach to assess the potential impact of Semantic Web technologies on these standards and on testability of integration results when using these standards. The experimental approach includes an automated translation of an XML Schema-based representation of business document content models into an OWL-based ontology. Based on this ontology, we use the Semantic Web representation and reasoning mechanisms to validate ontological constructs and constraints in support of data exchange. We demonstrate novel model-based integration capabilities that go beyond the existing syntax-based approaches. These new capabilities are relevant when managing multiple enterprise ontologies derived from a common ontology.

Analogical transfer is effective in a serial reaction time task in Parkinson's disease: Evidence for a dissociable form of sequence learning

Several studies of procedural learning in Parkinson's disease (PD) have demonstrated that these patients are impaired with respect to age-matched control subjects. In order to examine more closely the specific impairment, we considered three dimensions along which a procedural learning task could vary. These are: (1) implicit vs explicit learning, (2) instance vs rule learning, and (3) learning with internal vs external error correction. We consider two hypotheses that could explain the impairments observed in PD for different types of explicit motor learning: (H1) an impairment related to the acquisition of rules vs specific instances, and (H2) an impairment in learning when no explicit error feedback is provided. In order to examine the condition of rule learning with external error feedback, we developed a modified version of the serial reaction time (SRT) protocol that tests analogical transfer in sequence learning (ATSL). Reaction times are measured for responses to visual stimuli that appear in several different repeating sequences. While these isomorphic sequences are different, they share a common rule. Verbatim learning of a sequence would result in negative transfer from one sequence to a different one, while rule learning would result in positive transfer. Parkinson's patients and age-matched controls demonstrate significant acquisition and positive transfer of the rule between sequences. Our results demonstrate that PD patients are capable of learning and transferring rule or schema-based representations in an explicit learning format, and that this form of learning may be functionally distinct from learning mechanisms that rely on representations of the verbatim or statistical structure of sequences.

Knowledge-based design research at the key centre of design computing

This report outlines the current focus of the research at the Key Centre of Design Computing (KCDC) in the field of knowledge-based design and its future directions. The research is classified into seven main areas as follows: • Computer-Supported Synchronous Collaborative Design (CSSCD) • Design Creativity • Evolutionary Models of Design • Shape Emergence • Design Intent and Multiple Abstractions • Integration of Specific and General Knowledge • Concept Learning — Machine Learning While each such area will be discussed separately, there are many interrelations between them. For example, the research on CSSCD includes the research into shape emergence, design intent and multiple abstractions and their integration in the one environment; design creativity includes research into shape emergence and evolutionary models; CSSCD, evolutionary models, design creativity, case-based design and design intent/multiple abstractions all deal with object-oriented or schema-based representations of design objects and classes, using the design prototype schema developed at the then Design Computing Unit (DCU). The work on memory organization for heterogenous design knowledge includes aspects of concept learning and machine learning. Evolutionary models are also seen as an approach to machine learning. All the above areas of research are concerned with producing computable models of design processes based on integrating artificial intelligence approaches with CAD systems to provide better design support for designers using CAD systems. Other areas of research of the KCDC, not described in this paper are design theory and practice.

Automatic compilation of parallelism in visual object recognition

The paper presents the results of investigations into techniques for automatically generating concurrent implementations of visual object recognition systems from high-level visual domain descriptions. Visual knowledge can be represented using ‘schemata’-object-centred recognition models that intentionally represent classes of related objects. Schemata form hierarchies based on relationships such as composition and function. Schema-based representations contain implicit concurrencies that can be exploited by a recognition system called a ‘recognition network’. The paper presents the design of a simple schema representation language, and shows how it can be automatically transformed into concurrent code. This is done by building a compiler that takes schemata as input and produces a recognition network, in occam, as output. The design of the compiler involves several novel features. The existence of large transputer arrays makes it possible to execute the resulting code with high physical, as well as conceptual, parallelism.

Knowledge structuring and constraint satisfaction: the Mapsee approach

Schema-based representations for visual knowledge are integrated with constraint satisfaction techniques. This integration is discussed in a progression of three sketch map interpretation programs: Mapsee-1, Mapsee-2, and Mapsee-3. The programs are evaluated by the criteria of descriptive and procedural adequacy. The evaluation indicates that a schema-based representation used in combination with a hierarchical arc-consistency algorithm constitutes a modular, efficient, and effective approach to the structured representation of visual knowledge. The schemata used in this representation are embedded in composition and specialization hierarchies. Specialization hierarchies are further expanded into discrimination graphs.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>