• Journal of the Korean Society for Library and Information Science
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• v.47 no.3
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• pp.343-361
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• 2013

We proposed and evaluated a tag semantic analysis method in which original tags are expanded and the semantic relations between original or expanded tags are used to extract key sentences from lecture speech transcripts. To do that, we first investigated how useful Flickr tag clusters and WordNet synonyms are for expanding tags and for detecting the semantic relations between tags. Then, to evaluate our proposed method, we compared it with a latent semantic analysis (LSA) method. As a result, we found that Flick tag clusters are more effective than WordNet synonyms and that the F measure mean (0.27) of the tag semantic analysis method is higher than that of LSA method (0.22).

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.91-103
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• 2010

This paper introduces efficient rule-based semantic search techniques to ontology-based knowledge commerce services. Primarily, the search techniques presented in this paper define rules of reasoning that are required for users to search using the concept of ontology, multiple characteristics, relations among concepts and data type. In addition, based on the defined rules, the rule-based reasoning techniques search ontology for knowledge commerce services. This paper explains the conversion rules of query which convert user's query language into semantic search words, and transitivity rules which enable users to search related tags, knowledge products and users. Rule-based sematic search techniques are also presented; these techniques comprise knowledge search modules that search ontology using validity examination of queries, query conversion modules for standardization and expansion of search words and rule-based reasoning. The techniques described in this paper can be applied to sematic knowledge search systems using tags, since transitivity reasoning, which uses tags, knowledge products, and relations among people, is possible. In addition, as related users can be searched using related tags, the techniques can also be employed to establish collaboration models or semantic communities.

• Journal of Korea Multimedia Society
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• v.7 no.10
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• pp.1339-1353
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• 2004

< TABLE > tags in HTML documents are widely used for formatting layout of Web documents as well as for describing genuine tables with relational information. As a prerequisite for information extraction from the Web, this paper presents an efficient method for sophisticated table detection. The proposed method consists of two phases: preprocessing and attribute-value relations extraction. For the preprocessing where genuine or ungenuine tables are filtered out, appropriate rules are devised based on a careful examination of general characteristics of < TABLE > tags. The remaining is detected at the attribute-value relations extraction phase. Specifically, a value area is extracted and checked out whether there is a syntactic coherency Futhermore, the method looks for a semantic coherency between an attribute area and a value area of a table that may be inappropriate for the syntactic coherency checkup. Experimental results with 11,477 < TABLE > tags from 1,393 HTML documents show at the method has performed better compared with previous works, resulting in a precision of 97.54% and a recall of 99.22% in average.

• Journal of Advanced Navigation Technology
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• v.12 no.5
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• pp.516-523
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• 2008

Recently, the Web has been constructed based on collective intel1igence and growing up quickly. User created contents have been made the mainstream in this environments. So it's required to make an efficient technique of searching for the contents. The current searching technique mainly is achieved by key words. Semantic Web based on similarity and relationship of a language and using user tags in web2.0 also have been researched with activity. Generally, the web of the participation architecture has a lot of user created contents, various forms and classification. Therefore, it is necessary to classify and to efficiently search for a lot of user created contents. In this paper, we propose a depth searching technique considering the relationship among the tags that descript user contents. It is expected that the proposed depth searching techniques can reduce the time taken to search for the unwanted contents and the increase the efficiency of the contents searching using a service of suggestion words in tags groups.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.15-24
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• 2010

In this paper, we propose an image classification which combines semantic relations of tags with contents of images to improve the satisfaction of image retrieval on application domains as huge image sharing sites. To make good use of image retrieval or classification algorithms on huge image sharing sites as Flickr, they are applicable to real tagged Web images. To classify the Web images by 'bag of visual word' based image content, our algorithm includes training the category model by utilizing the preliminary retrieved images with semantically related tags as training data and classifying the test images based on PLSA. In the experimental results on the Flickr Web images, the proposed method produced the better precision and recall rates than those from the existing method using tag information.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.1-10
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• 2007

Recently, the web 2.0 services which appear by exponential extension of the Internet can be expressed with the changes in the quality of structural evolution and in the quantity of increasing users. The structural base is in user participational architecture, the web 2.0 services such as Blog, UCC, SNS(Social Networking Service), Mash-up, Long tail, etc. play a important role in organization of web, and grouping and searching of user participational data in web 2.0 is broadly used by folksonomy. Folksonomy is a new form that categorizes by tags, not classic taxonomy skill. it is made by user participation. Searching based on tag is now done by a simple text or a tag cloud method. But searching to consider and express the relations among each tags is imperfect yet. Thus, this paper provides the relational searching based on tags using the relational graph of tags. It should improve the trust of the searching and provide the convenience of the searching.

• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.37-44
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• 2008

Data entered into RFID tags are used for saving costs and enhancing competitiveness in the development of applications in various industrial areas. RFID readers perform the identification and search of hundreds of objects, which are tags. RFID technology that identifies objects on request of dynamic linking and tracking is composed of application components supporting information infrastructure. Despite their many advantages, existing applications, which do not consider elements related to real.time data communication among remote RFID devices, cannot support connections among heterogeneous devices effectively. As different network devices are installed in applications separately and go through different query analysis processes, there happen the delays of monitoring or errors in data conversion. The present study implements a RFID database handling system in semantic Web environment for integrated management of information extracted from RFID tags regardless of application. Users’ RFID tags are identified by a RFID reader mounted on an application, and the data are sent to the RFID database processing system, and then the process converts the information into a semantic Web language. Data transmitted on the standardized semantic Web base are translated by a smart browser and displayed on the screen. The use of a semantic Web language enables reasoning on meaningful relations and this, in turn, makes it easy to expand the functions by adding modules.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.547-552
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• 2014

As social multimedia sites are getting popular such as Flickr and Facebook, the amount of image information has been increasing very fast. So there have been many studies for accurate social image retrieval. Some of them were web image classification using semantic relations of image tags and BoVW(Bag of Visual Words). In this paper, we propose a method to detect salient region in images using GBVS(Graph Based Visual Saliency) model which can eliminate less important region like a background. First, We construct BoVW based on SIFT algorithm from the database of the preliminary retrieved images with semantically related tags. Second, detect salient region in test images using GBVS model. The result of image classification showed higher accuracy than the previous research. Therefore we expect that our method can classify a variety of images more accurately.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1877-1893
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• 2009

Folksonomy is a user-generated taxonomy to organize information by which a user assigns tags to resources published on the web. Triadic datas that indicate relations of between users, tags, and resources, are created by collaborative tagging from many users in folksonomy-based system. Such the folksonomy data has been utilized in the field of the semantic web and web2.0 as metadata about web resources. In this paper, we propose FCA-based folksonomy data mining approach in order to extract the useful information from folksonomy data with various points of view. And we developed tool for supporting our approach. In order to verify the usefulness of our proposed approach and FMT, we have done some experiments for data of del.icio.us, which is a popular folksonomy-based bookmarking system. And we report about result of our experiments.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.3
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• pp.274-279
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• 2015

The main goal of this research is to build a sentiment analysis system which automatically determines user opinions of the Stanford Sentiment Treebank in terms of three sentiments such as positive, negative, and neutral. Firstly, sentiment sentences are POS tagged and parsed to dependency structures. All nodes of the Treebank and their polarities are automatically extracted from the Treebank. We train two Support Vector Machines models. One is for a node level classification and the other is for a sentence level. We have tried various type of features such as word lexicons, POS tags, Sentiment lexicons, head-modifier relations, and sibling relations. Though we acquired 74.2% in accuracy on the test set for 3 class node level classification and 67.0% for 3 class sentence level classification, our experimental results for 2 class classification are comparable to those of the state of art system using the same corpus.