Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

An Ontology-Based Framework for Bridging Learning Design and Learning Content

Abstract: The paper describes an ontology-based framework for bridging learning design and learning object content. In present solutions, researchers have proposed conceptual models and developed tools for both of those subjects, but without detailed discussions of how they can be used together. In this paper we advocate the use of ontologies to explicitly specify all learning designs, learning objects, and the relations between them, and show how this use of ontologies can result in more effective (semi-)automatic tools and services that increase the level of reusability. We first define a three-part conceptual model that introduces an intermediate level between learning design and learning objects called the learning object context. We then use ontologies to facilitate the representation of these concepts: LOCO is a new ontology based on IMS-LD, ALOCoM is an existing ontology for learning objects, and LOCO-Cite is a new ontology for the learning object contextual model. We conclude by showing the applicability of the proposed framework in a use case study.

From thesaurus to ontology

Abstract: Thesauri such as the Art and Architecture Thesaurus (AAT) provide structured vocabularies for describing art objects. However, if we want to create a knowledge-rich description of an (image of an) art object, such as required by the "semantic web", thesauri turn out to provide only part of the knowledge needed. In this paper we look at problems related to capturing background knowledge for art resources. We describe a case study in which we attempt to construct an ontology for a subset of art-object descriptions, namely antique furniture, using AAT as well as metadata standards as input. We discuss the representation requirements for such an ontology as well as representational problems for our sample ontology with respect to the emerging web standards for knowledge representation (RDF, RDFS, OIL).

To see or not to see: analyzing difficulties in geometry from the perspective of visual perception

Abstract: In this paper, we consider theories about processes of visual perception and perception-based knowledge representation (VPR) in order to explain difficulties encountered in figural processing in junior high school geometry tasks. In order to analyze such difficulties, we take advantage of the following perspectives of VPR: (1) Perceptual organization: Gestalt principles, (2) recognition: bottom-up and top-down processing; and (3) representation of perception-based knowledge: verbal vs. pictorial representation, mental images and hierarchical structure of images. Examples given in the paper were mostly taken from Gal's study (2005) which aimed at identifying and analyzing Problematic Learning Situations (after Gal & Linchevski, 2000) in junior high school geometry classes. Gal's study (2005) suggests that while this theoretical perspective became part of teachers' pedagogic content knowledge, the teachers were aware of their students' thinking processes and their ability to analyze and cope with their students’ difficulties in geometry was improved.

Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies

Abstract: Among many other knowledge representations formalisms, Ontologies and Formal Concept Analysis (FCA) aim at modeling ‘concepts’. We discuss how these two formalisms may complement another from an application point of view. In particular, we will see how FCA can be used to support Ontology Engineering, and how ontologies can be exploited in FCA applications. The interplay of FCA and ontologies is studied along the life cycle of an ontology: (i) FCA can support the building of the ontology as a learning technique. (ii) The established ontology can be analyzed and navigated by using techniques of FCA. (iii) Last but not least, the ontology may be used to improve an FCA application.