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A Binary Decision Diagram-based Modeling Rule for Object-Relational Transformation Methodology
Sooyoung Cha, Sukhoon Lee, Doo-Kwon Baik
In order to design a system, software developers use an object model such as the UML class diagram. Object-Relational Transformation Methodology (ORTM) is a methodology to transform the relationships that are expressed in the object model into relational database tables, and it is applied for the implementation of the designed system. Previous ORTM studies have suggested a number of transformation methods to represent one relationship. However, there is an implementation problem that is difficult to apply because the usage criteria for each transformation method do not exist. Therefore, this paper proposes a binary decision diagram-based modeling rule for each relationship. Hence, we define the conditions for distinguishing the transformation methods. By measuring the query execution time, we also evaluate the modeling rules that are required for the verification. After evaluation, we re-define the final modeling rules which are represented by propositional logic, and show that our proposed modeling rules are useful for the implementation of the designed system through a case study.
Design and Implementation of a Hybrid Spatial Reasoning Algorithm
In order to answer questions successfully on behalf of the human contestant in DeepQA environments such as ‘Jeopardy!’, the American quiz show, the computer needs to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a hybrid spatial reasoning algorithm, among various efficient spatial reasoning methods, for handling directional and topological relations. Our algorithm not only improves the query processing time while reducing unnecessary reasoning calculation, but also effectively deals with the change of spatial knowledge base, as it takes a hybrid method that combines forward and backward reasoning. Through experiments performed on the sample spatial knowledge base with the hybrid spatial reasoner of our algorithm, we demonstrated the high performance of our hybrid spatial reasoning algorithm.
Automatic Word Spacing Using Raw Corpus and a Morphological Analyzer
This paper proposes a method for the automatic word spacing of unsegmented Korean sentences. In our method, eojeol monograms are used for word spacing as opposed to the syllable n-grams that have been used in previous studies. The use of a Korean morphological analyzer is limited to the correction of typical word spacing errors. Our method gives a 98.06% syllable accuracy and a 94.15% eojeol recall, when 10-fold cross-validated with the Sejong corpus, after filtering out non-hangul eojeols. The processing rate is 250K eojeols or 1.8 MB per second on a typical personal computer. Syllable accuracy and eojeol recall are related to the size of the eojeol dictionary, better performance is expected with a bigger corpus.
Syllable-based Probabilistic Models for Korean Morphological Analysis
This paper proposes three probabilistic models for syllable-based Korean morphological analysis, and presents the performance of proposed probabilistic models. Probabilities for the models are acquired from POS-tagged corpus. The result of 10-fold cross-validation experiments shows that 98.3% answer inclusion rate is achieved when trained with Sejong POS-tagged corpus of 10 million eojeols. In our models, POS tags are assigned to each syllable before spelling recovery and morpheme generation, which enables more efficient morphological analysis than the previous probabilistic models where spelling recovery is performed at the first stage. This efficiency gains the speed-up of morphological analysis. Experiments show that morphological analysis is performed at the rate of 147K eojeols per second, which is almost 174 times faster than the previous probabilistic models for Korean morphology.
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