• Asia pacific journal of information systems
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• v.30 no.3
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• pp.497-512
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• 2020

Anonymity is one of the key factors that influence communication and the work behaviours of people. It is even more evident in an online community where the role of anonymity can be akin to a double-edged sword: it can increase participation while at the same time having detrimental effects due to irresponsible and disruptive behaviour. Most studies on anonymous participation in groups or communities have reported this ambivalent view of anonymity: positive or negative. Furthermore, the effects of anonymous participation may be different in a dynamic sense because the task characteristics of participation can vary across time. In this study, we hypothesise that the effects of anonymity in online collaboration differ across the stages of collaboration. We analysed 2,978 featured articles on the English-language Wikipedia website and investigated the contributions of anonymous participants. While the contributions of anonymous participants were negative to collaboration efficiency as a whole, the negative effect of anonymous participants was stronger in the earlier stage than the later stage of collaboration. These findings indicate that the effect of anonymity has two sides in terms of collaboration efficiency in the same collaborative environment.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.107-123
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• 2014

Current text mining techniques suffer from the problem that the conventional text representation models cannot express the semantic or conceptual information for the textual documents written with natural languages. The conventional text models represent the textual documents as bag of words, which include vector space model, Boolean model, statistical model, and tensor space model. These models express documents only with the term literals for indexing and the frequency-based weights for their corresponding terms; that is, they ignore semantical information, sequential order information, and structural information of terms. Most of the text mining techniques have been developed assuming that the given documents are represented as 'bag-of-words' based text models. However, currently, confronting the big data era, a new paradigm of text representation model is required which can analyse huge amounts of textual documents more precisely. Our text model regards the 'concept' as an independent space equated with the 'term' and 'document' spaces used in the vector space model, and it expresses the relatedness among the three spaces. To develop the concept space, we use Wikipedia data, each of which defines a single concept. Consequently, a document collection is represented as a 3-order tensor with semantic information, and then the proposed model is called text cuboid model in our paper. Through experiments using the popular 20NewsGroup document corpus, we prove the superiority of the proposed text model in terms of document clustering and concept clustering.

• Journal of KIISE
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• v.44 no.8
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• pp.813-821
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• 2017

Entity Linking find the meaning of an entity mention, which indicate the entity using different expressions, in a user's query by linking the entity mention and the entity in the knowledge base. This task has four challenges, including the difficult knowledge base construction problem, multiple presentation of the entity mention, ambiguity of entity linking, and NIL entity recognition. In this paper, we first construct the entity name dictionary based on Wikipedia to build a knowledge base and solve the multiple presentation problem. We then propose various methods for NIL entity recognition and solve the ambiguity of entity linking by training the support vector machine based on several features, including the similarity of the context, semantic relevance, clue word score, named entity type similarity of the mansion, entity name matching score, and object popularity score. We sequentially use the proposed two methods based on the constructed knowledge base, to obtain the good performance in the entity linking. In the result of the experiment, our system achieved 83.66% and 90.81% F1 score, which is the performance of the NIL entity recognition to solve the ambiguity of the entity linking.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.979-991
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• 2016

Measuring nation images was a challenging task when employing offline surveys was the only option. It was not only prohibitively expensive, but too much time-consuming and therefore unfitted to this rapidly changing world. Although demands for monitoring real-time nation images were ever-increasing, an affordable and reliable solution to measure nation images has not been available up to this date. The researcher in this study developed a semi-automatic ontology construction algorithm, named "double-crossing double keyword collection (or DCDKC)" to measure nation images from Wikipedia in real-time. The ontology, WikiOnto, can be used to reflect dynamic image changes. In this study, an instance of WikiOnto was constructed by applying the algorithm to the big-three exporting countries in East Asia, Korea, Japan, and China. Then, the numbers of page views for words in the instance of WikiOnto were counted. A collection of the counting for each country was compared to each other to inspect the possibility to use for dynamic nation images. As for the conclusion, the result shows how the images of the three countries have changed for the period the study was performed. It confirms that DCDKC can very well be used for a real-time nation-image monitoring system.

• Journal of the Speleological Society of Korea
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• no.82
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• pp.17-25
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• 2007

The use of a low-frequency cave radio can also verify survey accuracy. A receiving unit on the surface can pinpoint the depth and location of a transmitter in a cave passage by measurement of the geometry of its radio waves. A survey over the surface from the receiver back to the cave entrance forms an artificial loop with the underground survey, whose loop-closure error can then be determined. In the past, caves were reluctant to redraw complex cave maps after detecting survey errors. Today, computer cartography can automatically redraw cave maps after data has been corrected.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1898-1904
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• 2009

Recently, real documents such as newspapers as well as blogs include newly coined words such as "Wikipedia". However, most previous information processing technologies cannot deal with these newly coined words because they construct their dictionaries based on materials acquired during system development. In this paper, we propose a model to automatically recognize Korean unknown words excluded from the previously constructed dictionary. The proposed model consists of an unknown noun recognition phase based on full text analysis, an unknown verb recognition phase based on web document frequency, and an unknown noun recognition phase based on web document frequency. The proposed model can recognize accurately the unknown words occurred once and again in a document by the full text analysis. Also, the proposed model can recognize broadly the unknown words occurred once in the document by using web documents. Besides, the proposed model fan recognize both a Korean unknown verb, which syllables can be changed from its base form by inflection, and a Korean unknown noun, which syllables are not changed in any eojeol. Experimental results shows that the proposed model improves precision 1.01% and recall 8.50% as compared with a previous model.

• Informatization Policy
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• v.17 no.4
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• pp.61-79
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• 2010

This study focuses on the emergence of collective intelligence and its impact on the democracy in the information era. Scholars have posed very different-optimistic and pessimistic-views on the possibility of collective knowledge produced by the public. Focusing on the cases of a free online encylopedia known as wikipedia and 2008 Candlelight Demonstration against the imports of US beef in Korea, this paper analyzes the mechanism of collective intelligence and its political implications on the democracy. Specifically, this article approaches changes in new knowledge paradigm with two different variables: the degree of connectivity and the quality of deliberation. Applying two different sets of variables helps us to distinguish the possibilities of collective intelligence and anti-intelligence, which would suggest social and political implications for the democracy in a country. This study finds a critical difference in terms of the quality of deliberation, measured by the indicators such as diversity, independence, and integration mechanism for online deliberation.

• Journal of Korea Game Society
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• v.9 no.4
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• pp.21-32
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• 2009

In this paper, we present a model of a virtual world that incorporates different time periods, in contrast to current popular virtual worlds like Second Life, to utilize the digital space fully. The construction of a virtual world in which we include historical information in the virtual life simulation utilizing the world map and current space information is proposed. The reason for incorporating time is that the virtual world varies according to the politics, economics, society, and culture of a particular time period, so users are able to play in a distinct virtual world as a resident and make their communities of their own free will. Like the online encyclopedia Wikipedia, the model proposed in this paper is a project designed to be maintained by and expanded through the interactivity of users, but unlike Wikipedia, users of this virtual world will be able to live and interact in a world of their own creation in addition to contributing real information.

• Journal of KIISE
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• v.42 no.11
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• pp.1397-1403
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• 2015

A named entity(NE) dictionary is an important resource for the performance of NE recognition. However, it is not easy to construct a NE dictionary manually since human annotation is time consuming and labor-intensive. To save construction time and reduce human labor, we propose a semi-automatic system for the construction of a NE dictionary. The proposed system constructs a pseudo-document with Wiki-categories per NE class by using an active learning technique. Then, it calculates similarities between Wiki entries and pseudo-documents using the BM25 model, a well-known information retrieval model. Finally, it classifies each Wiki entry into NE classes based on similarities. In experiments with three different types of NE class sets, the proposed system showed high performance(macro-average F1-score of 0.9028 and micro-average F1-score 0.9554).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.203-207
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• 2023

Recently, real-time streaming services are gaining popularity among the MZ generation and the market size is continuously growing. Pre-recorded and edited videos only show one-way communication, but real-time streaming services have the advantage of responding immediately to questions and requests from users, as they enable two-way communication. With the transition from face-to-face culture to non-face-to-face culture due to the COVID-19 pandemic, the number of users communicating in real-time video for activities such as classes, meetings, and leisure has dramatically increased. However, as real-time streaming services become more active and diverse generations participate, there is a problem of conflicts arising from language differences, including the use of neologisms. To address this issue, this paper proposes a method of collecting the meaning of neologisms through the Wikipedia API and presenting them to each other, so that they can understand each other's intentions.