웨어러블 디바이스를 이용한 반려동물 원격 모니터링 시스템

정한조(Hanjo Jeong),이정훈(Jeong-Hun Lee),이지형(Ji-Hyeong Lee),김세윤(Se-Yun Kim),정지원(Ji-Won Jung) 한국컴퓨터정보학회 2020 한국컴퓨터정보학회 학술발표논문집 Vol.28 No.2

인스타그램 포스트 데이터를 이용한 협업 필터링 기반 맛집 추천 시스템

정한조(Hanjo Jeong),송은수(Eunsu Song),최현승(Hyun-Seung Choi),박원정(Won-Jeong Park) 한국컴퓨터정보학회 2020 한국컴퓨터정보학회 학술발표논문집 Vol.28 No.2

사전과 말뭉치를 이용한 한국어 단어 중의성 해소

정한조(Hanjo Jeong),박병화(Byeonghwa Park) 한국지능정보시스템학회 2015 지능정보연구 Vol.21 No.1

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 Naive 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. Naive Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naive Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naive Bayes classifier is not realistic and ignores the correlation between attributes, Naive 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 dependenc

온톨로지와 토픽모델링 기반 다차원 연계 지식맵 서비스 연구

정한조(Hanjo Jeong) 한국지능정보시스템학회 2015 지능정보연구 Vol.21 No.4

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 const

공유메모리 다중처리기에서 상호연결망의 통신량을 고려하는 선인출 기법

박정우(Jeong-Woo Park),손영철(Youngchul Sohn),정한조(Hanjo Jung),맹승렬(Seungryoul Maeng) 한국정보과학회 2000 한국정보과학회 학술발표논문집 Vol.27 No.1A

상호연결망을 기반으로 하는 공유메모리 다중처리기의 성능은 공유메모리 접근 속도에 많은 영향을 받는다. 선인출 기법은 프로세서의 계산과 데이터의 접근을 중첩시켜 메모리의 접근 속도를 줄인다. 기존의 선인출 기법들은 캐쉬미스 양을 줄이는 것만을 생각하여 상호연결망의 상황을 고려하지 않은 문제점이 있다. 본 논문에서는 응답이 늦은 선인출 이용하여 선인출 양을 조절함으로써 상호연결망의 경쟁을 줄이는 새로운 선인출 기법을 제안하고 프로그램 구동 모의실험을 통해 기존의 선인출 기법[1]에 비해 더 좋은 성능을 나타냄을 보인다.