• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.18-30
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• 2020

Recently, as graph-structured data is widely used in various fields such as social networks and semantic Webs, needs for an effective and efficient search on a large amount of graph data have been increasing. Previous keyword-based search methods often find results by considering only the relevance to a given query. However, they are likely to produce semantically similar results by selecting answers which have high query relevance but share the same content nodes. To improve the diversity of search results, we propose a top-k search method that finds a set of subtrees which are not only relevant but also diverse in terms of the content nodes by controlling their similarity. We define a criterion for a set of diverse answer trees and design two kinds of diversified top-k search algorithms which are based on incremental enumeration and A^⁎ heuristic search, respectively. We also suggest an improvement on the A^⁎ search algorithm to enhance its performance. We show by experiments using real data sets that the proposed heuristic search method can find relevant answers with diverse content nodes efficiently.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of KIISE:Software and Applications
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• v.32 no.3
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• pp.202-214
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• 2005

The semantic web is the web paradigm that represents not general link of documents but semantics and relation of document. In addition it enables software agents to understand semantics of documents. We propose a semantic search based on inference with ontologies, which has the following characteristics. First, our search engine enables retrieval using explicit ontologies to reason though a search keyword is different from that of documents. Second, although the concept of two ontologies does not match exactly, can be found out similar results from a rule based translator and ontological reasoning. Third, our approach enables search engine to increase accuracy and precision by using explicit ontologies to reason about meanings of documents rather than guessing meanings of documents just by keyword. Fourth, domain ontology enables users to use more detailed queries based on ontology-based automated query generator that has search area and accuracy similar to NLP. Fifth, it enables agents to do automated search not only documents with keyword but also user-preferable information and knowledge from ontologies. It can perform search more accurately than current retrieval systems which use query to databases or keyword matching. We demonstrate our system, which use ontologies and inference based on explicit ontologies, can perform better than keyword matching approach .

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.19-28
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• 2015

Korea Digital Copyright Exchange of Korea Copyright Commission runs and manages copyrighted works and copyright license agreement, as well as assigns Integrated Copyright Number(ICN) to copyrighted works, also offers copyrighted works searching service. Keyword-based searching method based on ICN metadata provides results depending on the inclusion of metadata keyword. However, keyword-based searching method has limitations that cannot provide exact results that users need. Therefore, the essential method of offering exact result is needed. In this paper, we propose the semantic-based searching method about copyrighted works to increase the accuracy of searching results by extending ICN matadata to ontology, and implement the ontology of copyrighted works by defining relation and semantics about metadata elements. In order to compare semantic-based searching based on the ontology of copyrighted works with keyword-based searching method, the search results of each method according to 5 scenarios are presented and the accuracy of each method is compared.

• Journal of the Korean Society for information Management
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• v.28 no.1
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• pp.7-22
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• 2011

Internet portals have been revolutionized not only as simple search engines but also as a new space for the Internet users. They have developed to give satisfying search results for academic information users. academic fields. However, their attention was given to the quantity rather than the quality of the results. This tendency is now changing. This study addresses the problems in the search process using the current portal sites and presents an integrated scholarly information service where users can access more organized and trustworthy information linked with online technical keyword dictionary. When a user enter a keyword on a portal site, he/she can access to high quality scholarly information resources linked with keyword. This could assure the user to get an expanded knowledge with confirmation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.5
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• pp.3-13
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• 2011

In 2004, Boneh et.al. proposed a public key encryption with keyword search (PEKS) scheme which enables a server to test whether a keyword used in generating a ciphertext by a sender is identical to a keyword used in generating a query by a receiver or not. Yang et. al. proposed a probabilistic public key encryption with equality test (PEET) scheme which enables to test whether one message of ciphertext generated by one public key is identical to the other message generated by the other public key or not. If the message is replaced to a keyword, PEET is not secure against keyword guessing attacks and does not satisfy IND-CP A security which is generally considered in searchable encryption schemes. In this paper, we propose a public key encryption with equality test with designated tester (dPEET) which is secure against keyword guessing attacks and achieves IND-CPA security.

• Journal of Information Technology Services
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• v.19 no.4
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• pp.49-61
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• 2020

Prior studies on technology predictions attempted to predict the emergence and spread of emerging technologies through the analysis of correlations and changes between data using objective data such as patents and research papers. Most of the previous studies predicted future technologies only from the viewpoint of technology development. Therefore, this study intends to conduct technical forecasting from the perspective of the consumer by using keyword search frequency of search portals such as NAVER before and after the introduction of emerging technologies. In this study, we analyzed healthcare technologies into three types : measurement technology, platform technology, and remote service technology. And for the keyword analysis on the healthcare, we converted the classification of technology perspective into the keyword classification of consumer perspective. (Blood pressure and blood sugar, healthcare diagnosis, appointment and prescription, and remote diagnosis and prescription) Naver Trend is used to analyze keyword trends from a consumer perspective. We also used the ARIMA model as a technology prediction model. Analyzing the search frequency (Naver trend) over 44 months, the final ARIMA models that can predict three types of healthcare technology keyword trends were estimated as "ARIMA (1,2,1) (1,0,0)", "ARIMA (0,1,0) (1,0,0)", "ARIMA (1,1,0) (0,0,0)". In addition, it was confirmed that the values predicted by the time series prediction model and the actual values for 44 months were moving in almost similar patterns in all intervals. Therefore, we can confirm that this time series prediction model for healthcare technology is very suitable.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.93-102
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• 2007

Metadata and ontology can be used to retrieve related information through the inference mure accurately and simply on the Semantic Web. RDF and RDF Schema are general languages for representing metadata and ontology. An enormous number of keywords on the Semantic Web are very important to make practical applications of the Semantic Web because most users prefer to search with keywords. In this paper, we consider a resource as a unit of query results. And we classily queries with keyword conditions into three patterns and propose indexing techniques for keyword-search considering both metadata and ontology. Our index maintains resources that contain keywords indirectly using conceptual relationships between resources as well as resources that contain keywords directly. So, if user wants to search resources that contain a certain keyword, all resources are retrieved using our keyword index. We propose a structure of table for storing RDF Schema information that is labeled using some simple methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.875-878
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• 2007

Up until now, when a user search product information, the keyword-based search that mainly uses frequency of words or vocabulary information has been utilized in large. In the keyword-based research, the user should have to bear additional burden in order to search the displayed results manually once again because it shows those files that have no connection at all with the inquiries made by the user. To resolve such a problem, ontology has been emerged. In this paper, product search system using ontology was constructed directly and also tested how accurate search it does perform through the searching according to classification. To test this, about 40,000 product data of A discount store, which was operating on/off line discount stores, were constructed as database, and developmental environment for User Interface was tested by having developed the search system using JSP and PowerBuilder 9.0. Results from the test proved that the search method using Domain Ontology for product presented and designed in this paper was superior to the existing keyword-based search method.

• Korean Journal of Oriental Medicine
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• v.17 no.2
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• pp.57-62
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• 2011

We in this paper propose a semantic search system using the basic ontology in Korean medicine field. The basic ontology provides a formalization of medicinal materials, formulas, and diseases of Korean medicine. Recently, many studies for the semantic search system have been proposed. However, they do not support the semantic search and reasoning in the domain of Korean medicine because they do not have the Korean medicine ontology. Our system provides the semantic search features of semantic keyword recommendation, associated information browsing, and ontology reasoning based on the basic ontology. In addition, they also have the features of ontology search of a form of table and graph, synonym search, and external Open API supports. The general search engines usually provide search results for the simple keyword, while our system can also provide the associated information with respect to search results by using ontology so that can recommend more exact results to users.