• The Journal of the Korea Contents Association
• /
• v.8 no.11
• /
• pp.41-48
• /
• 2008

This study focuses on the modeling file search engine and suggesting modified file search schema based on weight value using file contents in order to improve the performance in terms of search accuracy and matching time. Most of the file search engines have used string matching algorithms like KMP(Knuth.Morris.Pratt), which may limit portability and fast searching time. However, this kind of algorithms don't find exactly the files what you want. Hence, the file search engine based on weight value using file contents is proposed here in order to optimize the performance for mobile environments. The Comparison with previous research shows that the proposed schema provides better.

• Journal of Internet Computing and Services
• /
• v.10 no.4
• /
• pp.45-54
• /
• 2009

In this paper we propose a new approach that recognizes the similar categories by weighting distinctive features. The approach is based on the PLSA that is one of the effective methods for the object categorization. PLSA is introduced from the information retrieval of text domain. PLSA, unsupervised method, shows impressive performance of category recognition. However, it shows relatively low performance for the similar categories which have the analog distribution of the features. In this paper, we consider the effective object categorization for the similar categories by weighting the mainly distinctive features. We present that the proposed algorithm, weighted PLSA, recognizes similar categories. Our method shows better results than the standard PLSA.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.3
• /
• pp.61-67
• /
• 2009

The proliferation of the Internet grows, according to electronic documents, along with increasing importance of technology in information retrieval. This research is possible to build a more efficient and accurate knowledge-base with unstructured text documents from the Web using to extract knowledge of the core meaning of LGG (Local Grammar Graph). We have built a ontology based on OWL(Web Ontology Language) using the areas of particular stocks up/down patterns created by the extraction and grammar patterns. It is possible for the user can search for meaning and quality of information about the user wants.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.5
• /
• pp.1-10
• /
• 2007

Many types of information provided through the web including news contents contain unnecessary clutters. These clutters make it difficult to build automated information processing systems such as the summarization, extraction and retrieval of documents. We propose a system that extracts and summarizes news contents from the web. The extraction system receives news contents in HTML as input and builds an element tree similar to DOM tree, and extracts texts while removing clutters with the hyperlink attribute in the HTML tag from the element tree. Texts extracted through the extraction system are transferred to the summarization system, which extracts key sentences from the texts. We implement the summarization system using co-occurrence relation graph. The summarized sentences of this paper are expected to be transmissible to PDA or cellular phone by message services such as SMS.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.8 no.3
• /
• pp.630-636
• /
• 2004

According to the increase of audio and video data utilization, the presentation of multimedia data contents and the work of retrieving, storing and manipulating a multimedia data have been the focus of recent work. The display for multimedia data should retrieve and access the contents easily that users want to present. This study is about the design and implementation of a system to retrieve multimedia data based on the contents of documentation or the caption information of a multimedia data for retrieving documentation including multimedia data. It intends to develop an filtering step to retrieve all of keyword within the caption information of multimedia data and text of a documentation. Also, the system is designed to retrieve a large amount of data quickly using an inverted file structure available for B+ tree.

• Proceedings of the Korean Information Science Society Conference
• /
• 2010.06c
• /
• pp.62-65
• /
• 2010

Image annotations allow users to access a large image database with textual queries. But since the surrounding text of Web images is generally noisy. an efficient image annotation and retrieval system is highly desired. which requires effective image search techniques. Data mining techniques can be adopted to de-noise and figure out salient terms or phrases from the search results. Clustering algorithms make it possible to represent visual features of images with finite symbols. Annotationbased image search engines can obtains thousands of images for a given query; but their results also consist of visually noise. In this paper. we present a new algorithm Double-Circles that allows a user to remove noise results and characterize more precise representative annotations. We demonstrate our approach on images collected from Flickr image search. Experiments conducted on real Web images show the effectiveness and efficiency of the proposed model.

• Journal of Korea Multimedia Society
• /
• v.22 no.3
• /
• pp.374-385
• /
• 2019

A Web Crawler is a program, which is commonly used by search engines to find the new brainchild on the internet. The use of crawlers has made the web easier for users. In this paper, we have used unstructured data by structuralization to collect data from the web pages. Our system is able to choose the word near our keyword in more than one document using unstructured way. Neighbor data were collected on the keyword through word2vec. The system goal is filtered at the data acquisition level and for a large taxonomy. The main problem in text taxonomy is how to improve the classification accuracy. In order to improve the accuracy, we propose a new weighting method of TF-IDF. In this paper, we modified TF-algorithm to calculate the accuracy of unstructured data. Finally, our system proposes a competent web pages search crawling algorithm, which is derived from TF-IDF and RL Web search algorithm to enhance the searching efficiency of the relevant information. In this paper, an attempt has been made to research and examine the work nature of crawlers and crawling algorithms in search engines for efficient information retrieval.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.10
• /
• pp.3230-3255
• /
• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• Journal of Broadcast Engineering
• /
• v.19 no.5
• /
• pp.578-589
• /
• 2014

Window image is displayed through a monitor screen when we execute the application programs on the computer. This includes webpage, video player and a number of applications. The webpage delivers a variety of information by various types in comparison with other application. Unlike a natural image captured from a camera, the window image like a webpage includes diverse components such as text, logo, icon, subimage and so on. Each component delivers various types of information to users. However, the components with different characteristic need to be divided locally, because text and image are served by various type. In this paper, we divide window images into many sub blocks, and classify each divided region into background, text and subimage. The detected subimages can be applied into 2D-to-3D conversion, image retrieval, image browsing and so forth. There are many subimage classification methods. In this paper, we utilize AdaBoost for verifying that the machine learning-based algorithm can be efficient for subimage detection. In the experiment, we showed that the subimage detection ratio is 93.4 % and false alarm is 13 %.

• Journal of KIISE:Software and Applications
• /
• v.31 no.5
• /
• pp.595-604
• /
• 2004

Automatic analysis of concepts or semantic relations from text documents enables not only an efficient acquisition of relevant information, but also a comparison of documents in the concept level. We present a multiple cause model-based approach to text analysis, where latent topics are automatically extracted from document sets and similarity between documents is measured by semantic kernels constructed from the extracted topics. In our approach, a document is assumed to be generated by various combinations of underlying topics. A topic is defined by a set of words that are related to the same topic or cooccur frequently within a document. In a network representing a multiple-cause model, each topic is identified by a group of words having high connection weights from a latent node. In order to facilitate teaming and inferences in multiple-cause models, some approximation methods are required and we utilize an approximation by Helmholtz machines. In an experiment on TDT-2 data set, we extract sets of meaningful words where each set contains some theme-specific terms. Using semantic kernels constructed from latent topics extracted by multiple cause models, we also achieve significant improvements over the basic vector space model in terms of retrieval effectiveness.