• Journal of the Korean Academy of Esthetic Dentistry
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• v.23 no.1
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• pp.34-40
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• 2014

There are several treatment options for rehabilitation of partial edentulism including the use of conventional or implant-retained fixed prostheses. However, such prosthetic options cannot always be possible because of compromised general and oral health (i.e. loss of supporting tissues, medical reasons, extensive surgical protocol and osseointegration failure of dental implant) as well as the affordability of patients. In some cases, removable partial denture provides easier access for oral hygiene procedures and the ability to correct discrepancies in dental arch relationships than implant fixed prosthesis. Recently, Implant Supported Removable Partial Denture (ISRPD) where to place dental implant in strategic position has been suggested to improve the limitation and shortcomings of conventional RPD. ISPRD can overcome mechanical limition of conventional RPD by placing implant in a favorable position and can be cost-effective, prosthetic solution for partially edentulous patients who are not immediate candidates for extensive, fixed implant supported restorations. Incorporation of dental implants to improve the RPD support and retention and to enhance patient acceptance should be considered when treatment planning for RPD. In this case, 59 years old male patient who received dental treatment of implant fixed prosthesis on both side of the upper jaw and implant overdenture on lower jaw showed implant abutment screw fracture on #15i and osseointegration failure on multiple number of implants. After removing failed implants, we planned ISRPD using #15i,24i,25i,26i and #23 natural tooth for RPD abutment. We fabricated #23 surveyed crown,#24i=25i=26i surveyed bridge and #15i gold coping for support,retention and stability for RPD. Periodic follow up check for 2years has been performed since the ISRPD delivery to the patient. No sign of screw loosening, fracture or bone resorption around abutment implants were detected.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Korean Journal of Plant Resources
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• v.28 no.2
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• pp.145-152
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• 2015

The present study aimed to investigate comparative anti-inflammatory effects of Litsea japonica fruit and leaf extract considering the balance of safety and efficacy. Dose response studies were performed to determine the inhibitory effects of 70% EtOH extract of leaf (L70%) on the pro-inflammatory enzymes expression, COX-2/PGE2 and NO/iNOS, and pro-inflammatory cytokines production, IL-$1{\beta}$, IL-6, and TNF-$\alpha$ in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. We also examined comparative effects of 30 and 70% EtOH extract of fruits (F30% and F70%) at low concentration ($10{\mu}g/ml$ ) in the same conditions. L70% at 50 and $100{\mu}g/ml$ showed inhibitory effects on almost all the inflammatory mediators we examined except for COX-2 regulation, but there were no effects at $10{\mu}g/ml$. Since $100{\mu}g/ml$ of L70% have 18.2% cytotoxicity, we compared the effects of fruit extract, F30% and F70% at $10{\mu}g/ml$ on the regulation of NO/iNOS, PGE2, IL-$1{\beta}$, IL-6, and TNF-$\alpha$ and obtained that fruit extacts are more efficacious and safe than leaf. This study suggests that the 30% EtOH fraction of L. japonica fruit could be a good candidate for development as a functional food supplement in the prevention of inflammatory disorders.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.