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ViCToRSpaces: Cooperative knowledge spaces for mathematics and natural sciences
 Proceedings of ELearn 2006
, 2006
"... Abstract: Cooperative knowledge spaces have a high potential to improve eLTR (eLearning, eTeaching and eResearch) at universities. Since there are few implementations deployed, notably in the fields of mathematics, natural sciences, and engineering, we propose a concept for fieldspecific knowledge ..."
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Abstract: Cooperative knowledge spaces have a high potential to improve eLTR (eLearning, eTeaching and eResearch) at universities. Since there are few implementations deployed, notably in the fields of mathematics, natural sciences, and engineering, we propose a concept for fieldspecific knowledge spaces for those disciplines the ”ViCToR”Spaces (Virtual Cooperation in Teaching and Research for Mathematics, Natural Sciences and Engineering). ViCToRSpaces present novel collaborative working environments for knowledge gain and research, supporting wellestablished forms of scientific and technological cooperation without geographical or technological boundaries. In this article, we will describe the requirements for ViCToRSpaces, the components that are already available, and further developments.
Intelligent Training Courses in Virtual Laboratories
 PROC. OF WORLD CONFERENCE ON EDUCATIONAL MULTIMEDIA, HYPERMEDIA AND TELECOMMUNICATIONS (EDMEDIA
"... The major challenge for eLearning courses on undergraduate mathematics is the broadness of the audience they target at and the varying demands brought up by distinct interest groups. Our proposal how to deal with these challenges is the deployment of intelligent assistants that, given some initial k ..."
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The major challenge for eLearning courses on undergraduate mathematics is the broadness of the audience they target at and the varying demands brought up by distinct interest groups. Our proposal how to deal with these challenges is the deployment of intelligent assistants that, given some initial knowledge on the audience, explore user behavior to build up a sophisticated model of the learner within the system. Starting with models for learner and course, we present a prototypical implementation of such a system within the virtual laboratory VIDEOEASEL developed at the TU Berlin.
MARACHNA: Automated Creation of Knowledge Representations for Mathematics
"... Automated extraction of knowledge from natural language texts is a major technical challenge that remains largely unsolved. Scientific texts in general, and mathematical texts in particular, are characterised by the use of complex language constructs with the intent to transfer knowledge. To a large ..."
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Cited by 1 (1 self)
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Automated extraction of knowledge from natural language texts is a major technical challenge that remains largely unsolved. Scientific texts in general, and mathematical texts in particular, are characterised by the use of complex language constructs with the intent to transfer knowledge. To a large extend, mathematical texts possess a strict internal structuring and can be separated into text elements such as definitions, theorems etc. These text elements are principal carriers of mathematical information. In addition, these elements show a characteristic linguistic structuring well suited for natural language processing techniques. In this paper we present MARACHNA, a system for extracting mathematical relations from texts and integrating them into a knowledge base. In response to user queries, parts of the knowledge base are visualised using XML Topic Maps. In particular, MARACHNA aims to provide an overview of single fields of mathematics, as well as showing intrafield relations between mathematical objects and concepts.
Natural Language Processing of Mathematical Texts in mArachna
"... AbstractmArachna is a technical framework designed for the extraction of mathematical knowledge from natural language texts. mArachna avoids the problems typically encountered in automatedreasoning based approaches through the use of natural language processing techniques taking advantage of the s ..."
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AbstractmArachna is a technical framework designed for the extraction of mathematical knowledge from natural language texts. mArachna avoids the problems typically encountered in automatedreasoning based approaches through the use of natural language processing techniques taking advantage of the strict formalized language characterizing mathematical texts. Mathematical texts possess a strict internal structuring and can be separated into text elements (entities) such as definitions, theorems etc. These entities are the principal carriers of mathematical information. In addition, Entities show a characteristic coupling between the presented information and their internal linguistic structure, well suited for natural language processing techniques. Taking advantage of this structure, mArachna extracts mathematical relations from texts and integrates them into a knowledge base. Identifying sub
Integration of Virtual Laboratories in Intelligent Training Courses for undergraduate mathematics classes
"... Abstract: The major challenge for eLearning courses on undergraduate mathematics is the broadness of the audience they target at and the varying demands brought up by distinct interest groups. Our proposal how to deal with these challenges is the deployment of intelligent assistants that, given some ..."
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Abstract: The major challenge for eLearning courses on undergraduate mathematics is the broadness of the audience they target at and the varying demands brought up by distinct interest groups. Our proposal how to deal with these challenges is the deployment of intelligent assistants that, given some initial knowledge on the audience, explore user behavior to build up a sophisticated model of the learner within the system. Starting with models for learner and course, we present a prototypical implementation of such a system within the virtual laboratory VIDEOEASEL developed at the Berlin University of Technology.
with Semantic Web Technologies
"... We present a knowledge management system suggesting a combination of Web 2.0 and Semantic Web technologies. With regard to the accumulation of knowledge as a consequence of the increasing amount of scientific publications, the demand for sophisticated knowledge management systems in the scientific e ..."
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We present a knowledge management system suggesting a combination of Web 2.0 and Semantic Web technologies. With regard to the accumulation of knowledge as a consequence of the increasing amount of scientific publications, the demand for sophisticated knowledge management systems in the scientific education has increased lately. Above all, mathematical publications remain a major challenge, caused by the need for complicated analysis of natural language texts and mathematical formulas. The webbased, mathematical knowledge management system KEA provides natural language processing techniques, and benefits from new web technologies. 1.
Equipping Virtual Laboratories with Intelligent Training Scenarios
"... Abstract: The major challenge for eLearning courses on undergraduate mathematics is the diversity of their audience and the varying demands posed by distinct interest groups. Our proposal to deal with these challenges is the deployment of intelligent assistants that, given some initial knowledge con ..."
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Abstract: The major challenge for eLearning courses on undergraduate mathematics is the diversity of their audience and the varying demands posed by distinct interest groups. Our proposal to deal with these challenges is the deployment of intelligent assistants that, given some initial knowledge concerning the audience, explore user behavior to build up a sophisticated model of the learner within the system. Starting with models for learner and course, we present a prototypical implementation of such a system within the virtual laboratory VIDEOEASEL developed at the TU Berlin.
Managing mathematical texts with OWL and their graphical representation
"... Mathematical knowledge contained in scientific digital publications poses a challenge for intelligent retrieval mechanisms. Many current approaches use statistical (e.g. Google) or natural language processing methods to find correlations in texts and annotate texts semantically. However both kinds o ..."
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Mathematical knowledge contained in scientific digital publications poses a challenge for intelligent retrieval mechanisms. Many current approaches use statistical (e.g. Google) or natural language processing methods to find correlations in texts and annotate texts semantically. However both kinds of approaches face the problem of extracting and processing knowledge from mathematical equations. The presented system is based on natural language processing techniques, and benefits from characteristic linguistic structures defined by the language used in mathematical texts. It accumulates extracted information snippets from texts, symbols, and equations in knowledge bases. These knowledge bases provide the foundation for the information retrieval. This article describes the concepts and the prototypical technical implementation.
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"... Automated extraction of information from natural language texts remains a largely unsolved problem. Scientific texts in general and mathematical texts in particular, are characterised by the use of complex language constructs, often requiring extensive background knowledge for comprehension. Fortuna ..."
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Automated extraction of information from natural language texts remains a largely unsolved problem. Scientific texts in general and mathematical texts in particular, are characterised by the use of complex language constructs, often requiring extensive background knowledge for comprehension. Fortunately, many mathematical texts contain special types of text elements, such as definitions and theorems, which we call entities. As these entities are some of the principal carriers of mathematical information, they have been selected as the basic content elements of the mathematical eLearning platform Mumie. We present a method to retrieve mathematical information from scientific textbooks, based on a primarily linguistic classification of the text contained within these entities. The information gained is gathered into a knowledge base that is suitable for an information retrieval system such as a mathematical encyclopaedia.
Knowledge Bases in mArachna
"... Automated extraction of knowledge from natural language texts is a major technical challenge that remains largely unsolved. Scientific texts in general, and mathematical texts in particular, are characterised by the use of complex language constructs with the intent to transfer knowledge. To a large ..."
Abstract
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Automated extraction of knowledge from natural language texts is a major technical challenge that remains largely unsolved. Scientific texts in general, and mathematical texts in particular, are characterised by the use of complex language constructs with the intent to transfer knowledge. To a large extend, mathematical texts possess a strict internal structuring and can be separated into text elements such as definitions, theorems etc. These text elements are principal carriers of mathematical information. In addition, these elements show a characteristic linguistic structuring well suited for natural language processing techniques. In this paper we present MARACHNA, a system for extracting mathematical relations from texts and integrating them into a knowledge base. In response to user queries, parts of the knowledge base are visualised using XML Topic Maps. In particular, MARACHNA aims to provide an overview of single fields of mathematics, as well as showing intrafield relations between mathematical objects and concepts.