Earticle

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document, and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment, namely IVSM (Inverse Vector Space Model). The model is based on a vector space model, which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

본 논문에서는 개인의 취향과 관심이 반영 되어있는 소셜 정보를 활용하여 사용자에게 영화를 추천할 수 있는 시스템을 제안하였다. 시스템에서 데이터 구축은 포털사이트에서 영화 정보를 수집하 고 페이스북과 트위터 같은 SNS를 통해 소셜 정보를 수집한다. 본 논문에서는 사용자의 감정에 따른 보다 정교한 처리를 위하여 6단계의 감정단계로 분류한 소셜 정보의 벡터공간 모형의 구축방법을 제 안한다. 추천을 위한 유사도 측도 방법은 2단계로 구성되어 있다. 첫 번째는 일반적인 코사인 측도를 통한 영화 목록의 구축 단계이고, 두 번째는 기존의 코사인 측도(Cosine measure)를 활용한 좌표평 면에서 감정 단계별 벡터 정보 표현 방법 및 유사도 측도 방법을 통해 추천 영화 목록의 결정 단계이 다. 본 논문의 추천 시스템의 성능을 평가하기 위하여 기존의 추천 시스템과 비교 실험을 통하여 본 연구의 추천 시스템의 유용성을 검증하였다.

This paper proposes a system which recommends movies using information from social network services containing personal interest and taste. Method for establishing data is as follows. The system gathers movies’ information from web sites and user’s information from social network services such as Facebook and twitter. The data from social network services is categorized into six steps of emotion level for more accurate processing following users’ emotional states. Gathered data will be established into vector space model which is ideal for analyzing and deducing the information with the system which is suggested in this paper. The existing similarity measurement method for movie recommendation is presentation of vector information about emotion level and similarity measuring method on the coordinates using Cosine measure. The deducing method suggested in this paper is two-phase arithmetic operation as follows. First, using general cosine measurement, the system establishes movies list. Second, using similarity measurement, system decides recommendable movie list by vector operation from the coordinates. After Comparative Experimental Study on the previous recommendation systems and new one , it turned out the new system from this study is more helpful than existing systems.

정보통신의 발달로 인해 우리의 생활은 많은 편리함을 얻었으나 다른 한편으로는 바이러스, 해킹 등 새로운 사이버 공격에 위협받고 있다. 특히 인터넷 보급률이 높아짐에 따라 악성코드가 네트워크를 통해 무차별 확산되고 있으며 이를 분석하기 위한 다양한 방법이 연구되고 있다. 이중 역공학 및 가상머신을 이용한 악성코드 분석기법은 많은 연구가 이루어지고 있지만 악성코드간 연관성이나 유사도에 대한 연 구는 미비한 실정이다. 따라서 본 논문에서는 N-Gram과 벡터공간 모델(Vector Space Model)을 이용하 여 악성코드간 유사도분석 모델을 제시하였으며 이를 통해 악성코드 유사도를 계산하여 신뢰성 있는 결과를 도출하였다.

The development of Information and Communication gives a lot of convenience in our lives, but on the other hand, the new cyber threat like viruses, computer intrusions and so on also increases. Especially, malicious codes such as viruses over the internet are distributed in large quantities. Several studies using reverse engineering or virtual machine have been carried out to analyze malicious codes, but studies on the analysis of the implications or the similarities between malicious codes have not been carried out in variety. Therefore, we suggest the analysis of similarity using N-Gram and Vector Space Model in this paper. The results shows that we can distinguish malicious codes from others using this method.

Text understanding is a hot research in Natural Language Processing and Information Retrieval. In recent years, it has received wide attention and research. In the era of big data, Understanding text in large-scale datasets is a challenge. Although the earliest systems designed for these workloads, such as MapReduce, gave users a powerful, but low-level, procedural programming interface. So, MapReduce doesn’t compose well for lager text applications. Recently, Spark, an in-memory cluster-computing platform, has been proposed. It has emerged as a popular framework for large-scale data processing and analytics. It provides a general-purpose efficient cluster computing engine and simpler for the end users. In this work, we consider using Vector Space Model (VSM) and TF-IDF weighting schema and feature hashing feature extraction techniques in order to solve the problem of large-scale text data similarity computing by Spark. As a result, Experimental results that using Spark in order to solve document similarity computation problems as soon as quickly by 20Newsgroups. In additions, It is more benefit from document classification and clustering of machine learning tasks.

Since text mining has been assumed to apply for unformatted text (document), it is necessary to represent text with simplified models. One of the most commonly used models is the vector space model, in which text is represented as a bag of words. Recently, many researches tried to apply a graph-based text model for representing semantic relationships between words. In this paper, we surveyed research trends of graph-based text representation models for text mining. We summarized the models, their features and forecasted further researches.

Since text mining has been assumed to apply for unformatted text (document), it is necessary to represent text with simplified models. One of the most commonly used models is the vector space model, in which text is represented as a bag of words. Recently, many researches tried to apply a graph-based text model for representing semantic relationships between words. In this paper, we surveyed research trends of graph-based text representation models for text mining. We summarized the models, their features and forecasted further researches.

텍스트 마이닝은 비정형화된 텍스트를 분석하여 그 안에 내재된 패턴, 추세, 분포 등의 고급정보들을 추출하 는 분야이다. 텍스트 마이닝은 기본적으로 비정형 데이터를 가정하므로 텍스트를 단순화된 모델로 표현하는 것이 필 요하다. 현재까지 가장 많이 사용되고 있는 모델은 텍스트를 단순한 단어들의 집합으로 표현한 벡터공간 모델이다. 그러나 최근 들어 단어들의 의미적 관계까지 표현하기 위해 그래프를 이용한 텍스트 표현 모델을 많이 사용하고 있 다. 본 논문에서는 텍스트 마이닝을 위한 기존의 연구 중에서 그래프에 기반한 텍스트 표현 모델의 방법들과 그들의 특징들을 기술한다. 또한 그래프 기반 텍스트 마이닝의 향후 발전방향에 대해서도 논한다.

Text Mining is a research area of retrieving high quality hidden information such as patterns, trends, or distributions through analyzing unformatted text. Basically, since text mining assumes an unstructured text, it needs to be represented as a simple text model for analyzing it. So far, most frequently used model is VSM(Vector Space Model), in which a text is represented as a bag of words. However, recently much researches tried to apply a graph-based text model for representing semantic relationships between words. In this paper, we survey research trends of graph-based text representation models for text mining. Additionally, we also discuss about future models of graph-based text mining.

Short text similarity measure is the basis of classification and duplicate checking of the short texts. Allowing for the insufficient consideration of the sentence semantic and structure information in similarity calculation between two short texts, we propose a novel method of short text similarity calculation based on double vector space model on the basis of traditional vector space model. Creatively transforming traditional vector space model into double vector space model. We utilize the numeral data link relations of Wikipedia to calculate semantic similarity between words, and calculate text structure similarity by dependency trees. Finally, we get the synthetic similarity by combining the semantic similar vector and structure similar vector. Our experiment results demonstrate that the proposed method has higher accuracy than other methods.

Now the network has become the main source of the information where people gain from. However there are various network information, include healthy and helpful information, at the same time; also include the bad and useless information. How to protect the security and quickly and exactly find the user need from this vast information has become the hot research. This article use the improve vector space model to filter bad information and use the semantic web technology to build a computer ontology. Aim at this area to information retrieval, through this ontology to handle users’ input, thus to improve the recall level and the precision rate.

This study is intended to compare the effectiveness of the neural network inductive learning model with a vector space model in information retrieval. As a result, searches responding to incomplete queries in the neural network inductive learning model produced a higher precision and recall as compared with searches responding to complete queries in the vector space model. The results show that the hybrid methodology of integrating an inductive learning technique with the neural network model can help solve information retrieval problems that are the results of inconsistent indexing and incomplete queries--problems that have plagued information retrieval effectiveness.

Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

본 논문은 특허 문서에 맞게 벡터스페이스 모델을 적용하여 특허정보 검색기를 구현한다. 기존의 상용 특허 검색 시스템의 문제점을 제시하고, 특허 문헌의 특징을 분석하여, 이를 반영한 특허 문헌 검색등의 벡터 스페이스 모델을 제시한다. 하나의 특허 문서는 서로 상이한 특성을 지닌 텍스트와 데이터의 조합으로 이루어져 있다. 따라서 이를 하나의 벡터로 표현하는 것이 용이하지 않다. 이에 대해 본 연구에서는 내용 필드들을 특성에 따라 둘 이상의 벡터로 표현하고, 수치 및 고유명 필드는 불린검색형태로 처리되는 혼합형 벡터 모델을 제안한다. 각 필드의 특징에 맞게 색인어를 추출하며, 텍스트 필드의 색인어률 벡터로 표현하는 과정에서는 잘 알려진 TF-IDF 가중치를 사용하되, 특허 문서가 IPC 특허 분류 기준에 따라 완전 분류되어 있는 문서라는 특징을 이용, 보다 정확한 가중치를 부여한다. 실험과 성능평가를 통하여 제안한 특허 모델의 유용성을 보인다.

지적재산권이 산업의 핵심으로 자리잡음으로써 특허의 중요성이 날로 증가하고 있다. 현재 특허문서 검색을 서비스하고 있는 상용시스템의 경우 문서간의 유사도나, 질의어에 따른 순위(Ranking)가 매겨지지 않는 불리언 모델이 검색에 사용되고 있다. 본 논문에서는 유사도에 기반 한 순위화가 가능한 벡터모델기반의 특허검색시스템을 개발하고 시계분야의 시소러스를 구축하여 시계분야의 특허검색 시스템에 적용하였다. 쿼리확장의 성능을 평가하기 위해 10개의 쿼리로 실험하였고 평균 36.2%의 정확도가 향상되었다. 그리고 검색결과의 오른쪽에 시소러스를 제시함으로써 특허검색시스템을 이용하는 사용자에게 추가 질의어를 쉴게 선택할 수 있도록 하여 인터페이스 부분의 향상을 추구하였다.

Sustainable development is no longer an option, but a requirement. Under this awareness, UN adopted 17 goals for a new sustainable development agenda on September 2015, named 'Sustainable Development Goals(SDGs)'. The Korean Society of Rural Planning(KSRP) is established on July 1994 for the sustainable development of rural areas. On the purpose to quantitatively analyze the research trend of KSRP's publications with the viewpoint of SDGs, the qualitative documents of 17 SDGs and 771 publications were mathematically transformed into vectors and the similarity was numerically measured with the 'Vector Space Model(VSM)'. The results show that 'Sustainable cities and communities(SDG 11)', 'Zero hunger(SDG 2)', 'Life on land(SDG 15)' and 'Responsible consumption and production(SDG 12)' have strong relationships with KSRP, while those of 'Affordable and clean energy(SDG 7)', 'Peace, justice and strong institution(SDG 16)' and 'Gender equality(SDG 5)' are weak. It is also found that the relationships of KSRP publications with 'energy' and 'climate change' issues(SDG 7, 13) were greatly increased during the period of 1995-2016, in spite of their weak relationships.

This paper proposes the model predictive control with space vector modulation (SVM) method for current control of voltage-source inverter. Unlike the conventional method using a limited number of voltage vectors by switching states, the proposed method can consider various voltage vectors to identify the optimized voltage vector. The various voltage vectors are obtained by subdividing existing voltage vectors. The optimized voltage vector that minimizes the cost function is selected and applied to the inverter by using the SVM. The various voltage vectors and SVM reduce current ripples in the output AC side of the inverter compared with the conventional method. The effectiveness and performance of the proposed method are verified through simulation and experiment with a three-phase two-level voltage-source grid-connected inverter.