• 한국IT서비스학회지
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• 제13권2호
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• pp.173-184
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• 2014

As social network services has become one of the most successful web-based business, recommendation in social network sites that assist people to choose various products and services is also widely adopted. Collaborative Filtering is one of the most widely adopted recommendation approaches, but recommendation technique that use explicit or implicit social network information from social networks has become proposed in recent research works. In this paper, we reviewed and compared research works about recommendation using social network analysis and collaborative filtering in social network sites. As the results of the analysis, we suggested the trends and implications for future research of recommendation in SNSs. It is expected that graph-based analysis on the semantic social network and systematic comparative analysis on the performances of social filtering and collaborative filtering are required.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2014년도 제26회 한글 및 한국어 정보처리 학술대회
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• pp.56-60
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• 2014

기계학습 방법에 기반한 자연어 분석은 학습 데이터가 필요하다. 학습 데이터가 구축된 소스 도메인이 아닌 다른 도메인에 적용할 경우 한국어 의미역 인식 기술은 15% 정도 성능 하락이 발생한다. 본 논문은 이러한 다른 도메인에 적용시 발생하는 성능 하락 현상을 극복하기 위해서 기존의 소스 도메인 학습 데이터를 활용하여, 소규모의 타겟 도메인 학습 데이터 구축만으로도 성능 하락을 최소화하기 위해 한국어 의미역 인식 기술에 prior 모델을 제안하며 기존의 도메인 적응 알고리즘과 비교 실험하였다. 추가적으로 학습 데이터에 사용되는 자질 중에서, 형태소 태그와 구문 태그의 자질 값을 기존보다 단순하게 적용하여 성능의 변화를 실험하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 추계학술발표대회
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• pp.363-366
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• 2016

추천 기법은 개인의 관심사와 상황을 고려한 개인화된 아이템을 제공함으로써 아이템의 소비과정에서 발생하는 부하를 줄여주고 정보 소비의 효율성을 증대시키는데 중요한 역할을 한다. 본 연구에서는 전통적인 추천 기법인 Content-Based(CB)기법과 최근 온라인 소셜 네트워크의 경향을 반영한 Social Network-based(SN)기법을 접목하여 새로운 복합방식의 정보 추천 기법을 제시한다. CB 기법의 대표적인 한계점인 cold start problem과 SN 기법의 추천 아이템의 전문성 문제를 상호 보완하며, 특히 최근 소셜 네트워크의 특징인 비신뢰 (non-trust) 기반의 영향력 있는 정보 확산자가 존재하는 환경에서 기법을 적용할 수 있도록 하였다. 또한 대부분 사람 추천 중심인 기존의 SN 기법들과는 달리 사람에게 제공할 정보의 추천에 초점을 두며, 정보 선정과정에서 개인의 온라인과 현실(real world)에서의 사회 활동 정보를 모두 활용하여 더육 더 개인화된 가치 정보를 제공하고자 한다.

• 디지털산업정보학회논문지
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• 제7권3호
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• pp.11-23
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• 2011

Recently, Open APIs are getting attention with the advent of Web 2.0. Open APIs are used to combine services and generate new services by Mashup. However, the growing number of available Open APIs raises a challenging issue how to locate the desired APIs. We automatically build ontologies from WSDL, WADL, HTML, and their underlying semantics. The key ingredient of our method is a technique that clusters input/output parameters in the collection of API methods into semantically meaningful concepts, and captures the hierarchical relationships between the terms contained in a parameter. These semantic ontologies allow search engines to support a similarity search for Open APIs based on various protocols such as SOAP, REST, JavaScript, and XML-RPC, and significantly improve the quality of APIs matching by the clustering and hierarchical relationships mechanism.

• 한국컴퓨터정보학회논문지
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• 제22권6호
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• pp.57-68
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• 2017

The Linked Open Data(LOD) cloud is quickly becoming one of the largest collections of interlinked datasets and the de facto standard for publishing, sharing and connecting pieces of data on the Web. Data publishers from diverse domains publish their data using Resource Description Framework(RDF) data model and provide SPARQL endpoints to enable querying their data, which enables creating a global, distributed and interconnected dataspace on the LOD cloud. Although it is possible to extract structured data as query results by using SPARQL, users have very poor in analysis and visualization of RDF data from SPARQL query results. Therefore, to tackle this issue, based on Formal Concept Analysis, we propose a novel approach for analyzing and visualizing useful information from the LOD cloud. The RDF data analysis and visualization technique proposed in this paper can be utilized in the field of semantic web data mining by extracting and analyzing the information and knowledge inherent in LOD and supporting classification and visualization.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2015년도 제27회 한글 및 한국어 정보처리 학술대회
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• pp.246-249
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• 2015

기계학습 방법에 기반한 자연어 분석은 학습 데이터가 필요하다. 학습 데이터가 구축된 소스 도메인이 아닌 다른 도메인에 적용할 경우 한국어 의미역 인식 기술은 10% 정도 성능 하락이 발생한다. 본 논문은 기존 도메인 적응 기술을 이용하여 도메인이 다르고, 문장의 형태도 다를 경우에 도메인 적응 알고리즘을 적용하여, 질의응답 시스템에서 필요한 질문 문장 의미역 인식을 위해, 소규모의 질문 문장에 대한 학습 데이터 구축만으로도 한국어 질문 문장에 대해 성능을 향상시키기 위한 방법을 제안한다. 한국어 의미역 인식 기술에 prior 모델을 제안한다. 제안하는 방법은 실험결과 소스 도메인 데이터만 사용한 실험보다 9.42, 소스와 타겟 도메인 데이터를 단순 합하여 학습한 경우보다 2.64의 성능향상을 보였다.

• Industrial Engineering and Management Systems
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• 제10권4호
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• pp.238-246
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• 2011

Given a directed graph, we can determine how the user's preference moves from one product item to another. In this graph called "preference transition network", each node represents the product item while its edge pointing to the other nodes represents the transition of user's preference. However, with the large number of items make the network become more complex, unclear and difficult to be interpreted. In order to address this problem, this paper proposes a visualization technique in preference transition analysis based on recency and frequency. By adapting these two elements, the semantic meaning of each item and its transition can be clearly identified by its different types of node size, color and edge style. The experiment in a sales data has shown the results of the proposed approach.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1999년도 KOBA 방송기술 워크샵 KOBA Broadcasting Technology Workshop
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• pp.43-48
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• 1999

To clarify the motion characteristics of free-style Okinawan dance“Kachaasi”, first the subjective impression was quantitatively evaluated with semantic differential technique to cluster its types. Then, the contingency of joint rotation in shoulder, elbow and wrist joints was examined with multivariate autoregressive model. The time-series data of positions and angels of three joints were calculated according to the deforming conditions and shielding directions of the ring lights. As the results, in an excellent dancer, the motions of shoulder and elbow were highly synchronized and smoothly controlled. The low-frequency output of the shoulder and elbow were mutually interacted. Meanwhile, the wrist behaved independently of other joints' rotation.

• 한국언어정보학회지:언어와정보
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• 제6권2호
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• pp.105-128
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• 2002

Morphologically annotated corpora are the basis for many tasks of computational linguistics. Most current approaches use statistically driven methods of morphological analysis, that provide just POS-tags. While this is sufficient for some applications, a rule-based full morphological analysis also yielding lemmatization and segmentation is needed for many others. This work thus aims at 〔1〕 introducing a rule-based Korean morphological analyzer called Kormoran based on the principle of linearity that prohibits any combination of left-to-right or right-to-left analysis or backtracking and then at 〔2〕 showing how it on be used as a POS-tagger by adopting an ordinary technique of preprocessing and also by filtering out irrelevant morpho-syntactic information in analyzed feature structures. It is shown that, besides providing a basis for subsequent syntactic or semantic processing, full morphological analyzers like Kormoran have the greater power of resolving ambiguities than simple POS-taggers. The focus of our present analysis is on Korean text.

• 한국멀티미디어학회논문지
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• 제20권1호
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• pp.66-73
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• 2017

Due to the recent rapid changes in society and wide spread of information devices, diverse digital information is utilized in a variety of economic and social analysis. Information related to the crime statistics by type of crime has been used as a major factor in crime. However, statistical analysis using only the structured data has the difficulty in the investigation by providing limited information to investigators and users. In this paper, structured data and unstructured data are analyzed by applying Korean Natural Language Processing (Ko-NLP) and the Latent Semantic Analysis (LSA) technique. It will provide a crime profile optimum system that can be applied to the crime profiling system or statistical analysis.