• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.131-150
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• 2015

Omission of noun phrases for obligatory cases is a common phenomenon in sentences of Korean and Japanese, which is not observed in English. When an argument of a predicate can be filled with a noun phrase co-referential with the title, the argument is more easily omitted in Encyclopedia texts. The omitted noun phrase is called a zero anaphor or zero pronoun. Encyclopedias like Wikipedia are major source for information extraction by intelligent application systems such as information retrieval and question answering systems. However, omission of noun phrases makes the quality of information extraction poor. This paper deals with the problem of developing a system that can restore omitted noun phrases in encyclopedia documents. The problem that our system deals with is almost similar to zero anaphora resolution which is one of the important problems in natural language processing. A noun phrase existing in the text that can be used for restoration is called an antecedent. An antecedent must be co-referential with the zero anaphor. While the candidates for the antecedent are only noun phrases in the same text in case of zero anaphora resolution, the title is also a candidate in our problem. In our system, the first stage is in charge of detecting the zero anaphor. In the second stage, antecedent search is carried out by considering the candidates. If antecedent search fails, an attempt made, in the third stage, to use the title as the antecedent. The main characteristic of our system is to make use of a structural SVM for finding the antecedent. The noun phrases in the text that appear before the position of zero anaphor comprise the search space. The main technique used in the methods proposed in previous research works is to perform binary classification for all the noun phrases in the search space. The noun phrase classified to be an antecedent with highest confidence is selected as the antecedent. However, we propose in this paper that antecedent search is viewed as the problem of assigning the antecedent indicator labels to a sequence of noun phrases. In other words, sequence labeling is employed in antecedent search in the text. We are the first to suggest this idea. To perform sequence labeling, we suggest to use a structural SVM which receives a sequence of noun phrases as input and returns the sequence of labels as output. An output label takes one of two values: one indicating that the corresponding noun phrase is the antecedent and the other indicating that it is not. The structural SVM we used is based on the modified Pegasos algorithm which exploits a subgradient descent methodology used for optimization problems. To train and test our system we selected a set of Wikipedia texts and constructed the annotated corpus in which gold-standard answers are provided such as zero anaphors and their possible antecedents. Training examples are prepared using the annotated corpus and used to train the SVMs and test the system. For zero anaphor detection, sentences are parsed by a syntactic analyzer and subject or object cases omitted are identified. Thus performance of our system is dependent on that of the syntactic analyzer, which is a limitation of our system. When an antecedent is not found in the text, our system tries to use the title to restore the zero anaphor. This is based on binary classification using the regular SVM. The experiment showed that our system's performance is F1 = 68.58%. This means that state-of-the-art system can be developed with our technique. It is expected that future work that enables the system to utilize semantic information can lead to a significant performance improvement.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.709-711
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• 2005

컴퓨터 그래픽스 분야의 발전으로 3D 모델의 수가 기하급수적으로 늘고 있다. 기존의 텍스트나 2D 이미지만을 검색하는 시스템으로는 정확한 3D 모델의 검색이 힘들다. 따라서 3D 모델 검색 시스템의 필요성이 대두되고 많은 분야에서 그 정확도와 속도향상을 위한 3D 모델 검색 연산자(Descriptor)와 검색 알고리즘을 개발하기 위한 연구가 진행 중이다. 본 논문에서는 VRML 모델을 XML 데이터로 변환하여 3D 모델 검색에 사용하는 것이 주요 목표이다. 검색 방법은 크게 VRML의 노드 분류화를 통한 기본 도형에 대한 검색과 XML로 변환하면서 생성하는 무게중심(Mass-Center)을 이용한 검색 두 가지이다. 즉, 3D 모델 데이터베이스를 구축함으로써 VRML 노드를 통한 분류화와 라벨화된 3D 모델 데이터베이스 지원 등의 장점을 활용한다. 3D 모델을 Key값(Descriptor)을 생성하여 분류화된 XML 데이터로 저장하고, 처리하여 유사도 비교의 대상과 횟수가 많아질수록, 3D 모델을 바로 데이터베이스에서 검색에 사용할 수 있어 검색의 속도와 성능을 보다 증가시킬 수 있다. 보다 복잡한 3D 모델의 유사도 비교에 있어서는 Princeton Shape Benchmark(PSB)[1]에서 정확도가 가장 높게 평가된 방법인 LFD(Light Field Descriptor)[6] 검색 연산자를 사용한다. 이 방법은 3D 모델에서 2D 이미지를 얻어 검색하는 방법으로 많은 2D 이미지 관측점(View-Point)과 관측된 2D 이미지의 적합도를 비교하는 계산량이 많은 단점이 있다. 그래서 3D 모델 검색을 위한 2D 이미지 관측에 있어 x, y, z축 방향의 관측점을 얻는 방법을 제안함으로써 2D 이미지의 관측점을 줄여 계산량을 대폭 감소시키는 장점을 갖는다.것으로 조사되었으며 40대 이상의 연령층은 점심비용으로 더 많은 지출을 하고 있는 것으로 나타났다. 4) 끼니별 한식에 대한 선호도는 아침식사의 경우가 가장 높았으며, 이는 40대와 50대에서 높게 나타났다. 점심 식사로 가장 선호되는 음식은 중식, 일식이었으며 저녁 식사에서 가장 선호되는 메뉴는 전 연령층에서 일식, 분식류 이었으며, 한식에 대한 선택 정도는 전 연령층에서 매우 낮게 나타났다. 5) 각 연령층에서 선호하는 한식에 대한 조사에서는 된장찌개가 전 연령층에서 가장 높은 선호도를 나타내었고, 김치는 40대 이상의 선호도가 30대보다 높게 나타났으며, 흥미롭게도 30세 이하의 선호도는 30대보다 높게 나타났다. 그 외에도 떡과 죽에 대한 선호도는 전 연령층에서 낮게 조사되었다. 장아찌류의 선호도는 전 연령대에서 낮았으며 특히 30세 이하에서 매우 낮게 조사되었다. 한식의 맛에 대한 만족도 조사에서는 연령이 올라갈수록 한식의 맛에 대한 만족도는 낮아지고 있었으나, 한식의 맛에 대한 만족도가 높을수록 양과 가격에 대한 만족도는 높은 경향을 나타내었다. 전반적으로 한식에 대한 선호도는 식사 때와 식사 목적에 따라 연령대 별로 다르게 나타나고 있으나, 선호도는 성별이나 세대에 관계없이 폭 넓은 선호도를 반영하고 있으며, 이는 대학생들을 대상으로 하는 연구 등에서도 나타난바 같다. 주 5일 근무제의 확산과 초 중 고생들의 토요일 휴무와 더불어 여행과 엔터테인먼트산업은 더욱 더 발전을 거듭하고 있으며, 외식은 여행과 여가 활동의 필수적인 요소로써 그 역할을 일조하고 있다. 이와 같은 여가시간의 증가는 독신자들에게는 좀더 많은 여유시간을 가족을 이루고 있는 가족구성원들에게는 가족과의 유대를 강화하는 휴식과 오락의 소비 트렌드를 창출시켰다. 이와 더불어 외식은 식사를 해결하기 위한

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.697-705
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• 2009

An unsupervised (blind) method is proposed aiming at extracting rhythmic sources from commercial polyphonic music whose number of channels is limited to one. Commercial music signals are not usually provided with more than two channels while they often contain multiple instruments including singing voice. Therefore, instead of using conventional modeling of mixing environments or statistical characteristics, we should introduce other source-specific characteristics for separating or extracting sources in the under determined environments. In this paper, we concentrate on extracting rhythmic sources from the mixture with the other harmonic sources. An extension of nonnegative matrix factorization (NMF), which is called nonnegative matrix partial co-factorization (NMPCF), is used to analyze multiple relationships between spectral and temporal properties in the given input matrices. Moreover, temporal repeatability of the rhythmic sound sources is implicated as a common rhythmic property among segments of an input mixture signal. The proposed method shows acceptable, but not superior separation quality to referred prior knowledge-based drum source separation systems, but it has better applicability due to its blind manner in separation, for example, when there is no prior information or the target rhythmic source is irregular.

• Journal of Nutrition and Health
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• v.43 no.2
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• pp.197-206
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• 2010

Now a days, people eat outside of the home more and more frequently. Menu labeling can help people make more informed decisions about the foods they eat and help them maintain a healthy diet. This study was conducted to develop menu labeling system using Nutri-API (Nutrition Analysis Application Programming Interface). This system offers convenient user interface and menu labeling information with printout format. This system provide useful functions such as new food/menu nutrients information, retrieval food semantic service, menu plan with subgroup and nutrient analysis informations and print format. This system provide nutritive values with nutrient information and ratio of 3 major energy nutrients. MLS system can analyze nutrients for menu and each subgroup. And MLS system can display nutrient comparisons with DRIs and % Daily Nutrient Values. And also this system provide 6 different menu labeling formate with nutrient information. Therefore it can be used by not only usual people but also dietitians and restaurant managers who take charge of making a menu and experts in the field of food and nutrition. It is expected that Menu Labeling System (MLS) can be useful of menu planning and nutrition education, nutrition counseling and expert meal management.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.295-300
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• 2018

Net radiation is the total amount of radiation energy used as a heat flux for the Earth's energy cycle, and net radiation from the surface is an important factor in areas such as hydrology, climate, meteorological studies and agriculture. It is very important to monitoring the net radiation through remote sensing to be able to understand the trend of heat island and urbanization phenomenon. However, net radiation estimation using only remote sensing data is generally causes difference in accuracy depending on cloud. Therefore, in this paper, we retrieved and monitored high resolution surface net radiation at 1 hour interval in Eunpyeong New Town where urbanization using Communication, Ocean and Meteorological Satellite (COMS), Landsat-8 satellite and Computational Fluid Dynamics (CFD) model data reflecting the difference in building height. We compared the observed and estimated net radiation at the flux tower. As a result, estimated net radiation was similar trend to the observed net radiation as a whole and it had the accuracy of RMSE $54.29Wm^{-2}$ and Bias $27.42Wm^{-2}$. In addition, the calculated net radiation showed well the meteorological conditions such as precipitation, and showed the characteristics of net radiation for the vegetation and artificial area in the spatial distribution.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1449-1463
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• 2020

As recent global warming and climate changes become more serious, the importance of CO2 absorption by forests is increasing to cope with the greenhouse gas issues. According to the UN Framework Convention on Climate Change, it is required to calculate national CO2 absorptions at the local level in a more scientific and rigorous manner. This paper presents the gridded expansion of forest flux observations and mapping of daily CO2 absorption by the forests in Korea using numerical weather prediction data and satellite images. To consider the sensitive daily changes of plant photosynthesis, we built a machine learning model to retrieve the daily RACA (reference amount of CO2 absorption) by referring to the climax forest in Gwangneung and adopted the NIFoS (National Institute of Forest Science) lookup table for the CO2 absorption by forest type and age to produce the daily AACA (actual amount of CO2 absorption) raster data with the spatial variation of the forests in Korea. In the experiment for the 1,095 days between Jan 1, 2013 and Dec 31, 2015, our RACA retrieval model showed high accuracy with a correlation coefficient of 0.948. To achieve the tier 3 daily statistics for AACA, long-term and detailed forest surveying should be combined with the model in the future.

• Journal of KIISE:Information Networking
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• v.29 no.3
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• pp.286-298
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• 2002

The Remote Procedure Call(RPC) has been traditionally used for Inter Process Communication(IPC) among precesses in distributed computing environment. As distributed applications have been complicated more and more, the Mobile Agent paradigm for IPC is emerged. Because there are some paradigms for IPC, researches to evaluate and compare the performance of each paradigm are issued recently. But the performance models used in the previous research did not reflect real distributed computing environment correctly, because they did not consider the evacuation elements for providing security services. Since real distributed environment is open, it is very vulnerable to a variety of attacks. In order to execute applications securely in distributed computing environment, security services which protect applications and information against the attacks must be considered. In this paper, we evaluate and compare the performance of the Remote Procedure Call with that of the Mobile Agent in IPC paradigms. We examine security services to execute applications securely, and propose new performance models considering those services. We design performance models, which describe information retrieval system through N database services, using Petri Net. We compare the performance of two paradigms by assigning numerical values to parameters and measuring the execution time of two paradigms. In this paper, the comparison of two performance models with security services for secure communication shows the results that the execution time of the Remote Procedure Call performance model is sharply increased because of many communications with the high cryptography mechanism between hosts, and that the execution time of the Mobile Agent model is gradually increased because the Mobile Agent paradigm can reduce the quantity of the communications between hosts.