• Korean Journal of Oriental Medicine
• /
• v.18 no.1
• /
• pp.45-52
• /
• 2012

Objectives : Our study aims to provide a collaborative Internet terminology dictionary like Wikipedia, where about 30,000 concept terminologies with respect to traditional Korean medicine (TKM) are shared and TKM experts can edit the terminologies. Methods : The concept terminologies have been collected and refined for three years by the terminology management system, a custom-made software built upon the Oracle database, where each terminology is divided and normalized into one or more tables. The operation of Wikipedia depends on MediaWiki, a free and open source wiki software built upon the MySQL database. The database schema of our terminology management system is different from that of MediaWiki so that MediaWiki cannot used as our terminology dictionary. Thus, we propose a way to share and edit TKM terminologies with wiki-like user interface. Results : We devise a new terminology dictionary system to search and edit terminology upon the database of the terminology management system. The online terminology dictionary of TKM has the user interface and functions which is similar to Wikipedia to support collaborative works. Conclusions : Wikipedia is operated on MediaWiki which is can be downloaded and used freely under the GNU General Public License. However, there occur problems to use MediaWiki upon the legacy system. Thus, other wiki projects start, they should be considered.

• Journal of KIISE
• /
• v.42 no.11
• /
• pp.1397-1403
• /
• 2015

A named entity(NE) dictionary is an important resource for the performance of NE recognition. However, it is not easy to construct a NE dictionary manually since human annotation is time consuming and labor-intensive. To save construction time and reduce human labor, we propose a semi-automatic system for the construction of a NE dictionary. The proposed system constructs a pseudo-document with Wiki-categories per NE class by using an active learning technique. Then, it calculates similarities between Wiki entries and pseudo-documents using the BM25 model, a well-known information retrieval model. Finally, it classifies each Wiki entry into NE classes based on similarities. In experiments with three different types of NE class sets, the proposed system showed high performance(macro-average F1-score of 0.9028 and micro-average F1-score 0.9554).

• Journal of KIISE:Software and Applications
• /
• v.41 no.8
• /
• pp.580-585
• /
• 2014

This paper conducts a variety of experiments for "the extraction of Korean parallel sentences using Wikipedia data". We refer to various methods that were previously proposed for other languages. We use two approaches. The first one is to use translation probabilities that are extracted from the existing resources such as Sejong parallel corpus, and the second one is to use dictionaries such as Wiki dictionary consisting of Wikipedia titles and MRDs (machine readable dictionaries). Experimental results show that we obtained a significant improvement in system using Wikipedia data in comparison to one using only the existing resources. We finally achieve an outstanding performance, an F1-score of 57.6%. We additionally conduct experiments using a topic model. Although this experiment shows a relatively lower performance, an F1-score of 51.6%, it is expected to be worthy of further studies.

• Journal of KIISE
• /
• v.42 no.7
• /
• pp.901-909
• /
• 2015

In this paper, to build a parallel corpus between Korean and English in Wikipedia. We proposed a method to find similar sentences based on language resources and topic modeling. We first applied language resources(Wiki-dictionary, numbers, and online dictionary in Daum) to match word sequentially. We construct the Wiki-dictionary using titles in Wikipedia. In order to take advantages of the Wikipedia, we used translation probability in the Wiki-dictionary for word matching. In addition, we improved the accuracy of sentence similarity measuring method by using word distribution based on topic modeling. In the experiment, a previous study showed 48.4% of F1-score with only language resources based on linear combination and 51.6% with the topic modeling considering entire word distributions additionally. However, our proposed methods with sequential matching added translation probability to language resources and achieved 9.9% (58.3%) better result than the previous study. When using the proposed sequential matching method of language resources and topic modeling after considering important word distributions, the proposed system achieved 7.5%(59.1%) better than the previous study.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.9
• /
• pp.17-27
• /
• 2012

Open Market is one of the key factors to accelerate the profit. Usually retailers sell items in several Open Market. One of the challenges for retailers is to assign categories of items with different classification systems. In this research, we propose an item category recommendation method to support appropriate products category registration. Our recommendations are based on semantic relation between existing and any other Open Market categorization. In order to analyze correlations of categories, we use Morpheme analysis, Korean Wiki Dictionary, WordNet and Google Translation API. Our proposed method recommends a category, which is most similar to a guide word by measuring semantic similarity. The experimental results show that, our system improves the system accuracy in term of search category, and retailers can easily select the appropriate categories from our proposed method.