Relevance of a Set of Topical Texts to a Knowledge Unit and the Estimation of the Closeness of Linguistic Forms of Its Expression to a Semantic Pattern

This paper describes a learning/adaptive approach to automatically building knowledge bases for information extraction from text based web pages. A frame based representation is introduced to represent domain knowledge as knowledge unit frames. A frame learning algorithm is developed to automatically learn knowledge unit frames from training examples. Some training examples can be obtained by automatically parsing a number of tabular web pages in the same domain, which greatly reduced the amount of time consuming manual work. This approach was investigated on ten web sites of real estate advertisements and car advertisements and nearly all the information was successfully extracted with very few false alarms. These results suggest that both the knowledge unit frame representation and the frame learning algorithm work well, domain specific knowledge bases can be learned from training examples, and the domain specific knowledge base can be used for information extraction from flexible text-based semi-structured Web pages on multiple Web sites. The investigation of the knowledge representation on five other domains suggests that this approach can be easily applied to other domains by simply changing the training examples.

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A Study on Knowledge Unit for High-Performance Computing in Computational Science

Journal of Digital Contents Society ◽

10.9728/dcs.2018.19.5.1021 ◽

2018 ◽

Vol 19 (5) ◽

pp. 1021-1026

Author(s):

Heejun Yoon ◽

Seongjin Ahn

Keyword(s):

High Performance Computing ◽

High Performance ◽

Computational Science ◽

Knowledge Unit ◽

Performance Computing

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Learning to identify core term of knowledge unit from short text

2012 9th International Conference on Fuzzy Systems and Knowledge Discovery ◽

10.1109/fskd.2012.6233797 ◽

2012 ◽

Author(s):

Zhenhua Tian ◽

Zhen Wang ◽

Ziqi Liu ◽

Hengheng Xiang ◽

Jun Liu ◽

...

Keyword(s):

Short Text ◽

Knowledge Unit

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Extracting Learning Features of Knowledge Unit in Knowledge Map

2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing ◽

10.1109/iih-msp.2014.92 ◽

2014 ◽

Author(s):

XiangJun Huang ◽

QingHua Zheng ◽

Chao Zhang

Keyword(s):

Knowledge Map ◽

Knowledge Unit

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Knowledge Representation Method of Product Design Based on Ontology

Materials Science Forum ◽

10.4028/www.scientific.net/msf.697-698.774 ◽

2011 ◽

Vol 697-698 ◽

pp. 774-778

Author(s):

J. Sun ◽

Wei Guo ◽

Lei Wang ◽

D.M. Zhang

Keyword(s):

Knowledge Representation ◽

Product Design ◽

Knowledge Networks ◽

Spiral Spring ◽

Output Module ◽

Agile Design ◽

Knowledge Unit ◽

Representation Method ◽

And Function ◽

Cylindrical Spiral

According to the diversity, dynamic and correlation of product design knowledge; this paper put forward a knowledge representation method for product design based on ontology. Established a knowledge unit with the core of concepts set, property set, knowledge set and function set, introduced input and output module, to realize the information transmission between knowledge units, and also lays a foundation for knowledge networks construction, intelligent and agile design. Finally, this paper take cylindrical spiral spring design for example, verified the effectiveness of this method.

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Constraint-based knowledge representation for individualized instruction

Computer Science and Information Systems ◽

10.2298/csis0601001s ◽

2006 ◽

Vol 3 (1) ◽

pp. 1-22 ◽

Cited By ~ 11

Author(s):

Ohlsson Stellan ◽

Antonija Mitrovic

Keyword(s):

Intelligent Tutoring Systems ◽

Individualized Instruction ◽

Learning Curves ◽

Student Modeling ◽

Partial Knowledge ◽

Tutoring Systems ◽

Knowledge Representations ◽

Knowledge Unit ◽

Executable Programs ◽

Constraint Based Models

Traditional knowledge representations were developed to encode complete explicit and executable programs, a goal that makes them less than ideal for representing the incomplete and partial knowledge of a student. In this paper, we discuss state constraints, a type of knowledge unit originally invented to explain how people can detect and correct their own errors. Constraint-based student modeling has been implemented in several intelligent tutoring systems (ITS) so far, and the empirical data verifies that students learn while interacting with these systems. Furthermore, learning curves are smooth when plotted in terms of individual constraints, supporting the psychological appropriateness of the representation. We discuss the differences between constraints and other representational formats, the advantages of constraint-based models and the types of domains in which they are likely to be useful.

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CLASSIFICATION OF THE EDUCATIONAL TEXTS STYLES WITH THE METHODS OF ARTIFICIAL INTELLIGENCE

Journal of Baltic Science Education ◽

10.33225/jbse/17.16.324 ◽

2017 ◽

Vol 16 (3) ◽

pp. 324-336

Author(s):

Tereza Horáková ◽

Milan Houška ◽

Ludmila Dömeová

Keyword(s):

Artificial Intelligence ◽

Regression Trees ◽

Classification And Regression Trees ◽

Text Structure ◽

Educational Process ◽

Typical Structure ◽

Knowledge Unit ◽

Classification And Regression ◽

The Right

Modern educational methods emphasize the necessity to transfer knowledge instead of data or information within the educational process. Thus it is important to the educational texts supporting the educational process contain knowledge in a particular textual representation. But it is not trivial to decide whether the particular piece of text contain knowledge or not. The solution is to measure the similarity between the particular text structure and a typical structure of a knowledge-designed text. This research aims at analysing the classification ability of three commonly-used classification techniques: artificial neural networks (ANNs), classification and regression trees (CARTs) and decision trees (bigMLs) to separate texts or text fragments into two groups. The texts in the first group contain mainly data and information (common texts), the texts in the other group contain knowledge in one of the particular knowledge representations (knowledge texts). The sample of 120 text fragments was used for the analysis. The results show that the ANN techniques are significantly more able to make the right classification of the text than the CART or bigML ones, and evidence good classification abilities. Thus the ANN approach could broaden the set of methods used for evaluation of difficulty of educational texts or textbooks. Keywords: artificial intelligence, classification and regression trees, educational texts, knowledge representation, knowledge unit, production rules, stylometric analysis.

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