• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2122-2140
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• 2020

Proper and precise embedding of commercial ads within Webpages requires Ad-hoc analysis and understanding of their content. By the successful implementation of this step, both publishers and advertisers gain mutual benefits through increasing their revenues on the one hand, and improving user experience on the other. In this research work, we propose a novel multi-level context-based ads serving approach through which ads will be served at generic publisher websites based on their contextual relevance. In the proposed approach, knowledge encoded in domain-specific and generic semantic repositories is exploited in order to analyze and segment Webpages into sets of contextually-relevant segments. Semantically-enhanced indexes are also constructed to index ads based on their textual descriptions provided by advertisers. A modified cosine similarity matching algorithm is employed to embed each ad from the Ads repository into one or more contextually-relevant segments. In order to validate our proposal, we have implemented a prototype of an ad serving system with two datasets that consist of (11429 ads and 93 documents) and (11000 documents and 15 ads), respectively. To demonstrate the effectiveness of the proposed techniques, we experimentally tested the proposed method and compared the produced results against five baseline metrics that can be used in the context of ad serving systems. In addition, we compared the results produced by our system with other state-of-the-art models. Findings demonstrate that the accuracy of conventional ad matching techniques has improved by exploiting the proposed semantically-enhanced context-based ad serving model.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.111-123
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• 2013

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called 'World Knowledge.' World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human's cultural knowledge may also change. Today's society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.343-349
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• 2009

In this paper, it would be shown that individuals can have different responses to the same email based on their preferences through computing the distributions of user clusters' email responses from clustering results based on email users' preference information. This paper presents an approach that incorporates user preferences to construct an anti-spam mail system, which is different from the conventional content-based ones. We consider email category information derived from the email content as well as user preference information. We also build a user preference ontology to formally represent the important concepts and rules derived from a data mining process and then apply a rule optimization procedure to exclude unnecessary rules. Experimental results show that our user preference based system achieves good performance in terms of accuracy, the rules derived from the system and human comprehensibility.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.515-517
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• 2006

Recently, there is a rapidly growing demand for efficient real-tine playback and transmission of large amounts of multimedia data. But most users' connections are not fast enough to download large chunks of multimedia data. Therefore a streaming technology is needed in which users enable the real-time playback of multimedia date without having to download the whole of the multimedia date. In this paper, we propose a personalized IP streaming system. The proposed IP streaming system enables users to got an intelligent recommendation of multimedia contents based on the user preference information stored on the streaming server or the home media server. Moreover, users are assured of seamless access of streamed content event if they switch to another client device by implementing streaming system based on user identification and device information. We evaluate our approach with simulation results.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.259-262
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• 2003

A efficient P2P (peer to peer) networking for sharing multimedia contents is proposed to overcome the limitations of unstructured approaches. The proposed approach is attempting to organize the participating P2P nodes by modifying a n-dimensional cartesian-coordinate space DHT (distributed hash table), CAN (content addressable network). The network identifiers (e.g., network prefix of IP address) of participating nodes are mapped into the CAN virtual coordinate space (in 2-d) and nodes with similar identifiers are grouped into the same zone. The proposed scheme is expected to show some level of concentration reflecting the network identifiers. Network simulator-based evaluation is performed to verify the effectiveness of the proposed scheme.

• ETRI Journal
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• v.28 no.6
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• pp.697-708
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• 2006

Various adaptive mesh refinement techniques are often employed in numerical simulations for increasing spatial and temporal resolution beyond the limits imposed by available CPU time and memory space. Recently, an octree-based adaptive mesh structure was successfully used in fluid animation to place more grid cells efficiently in visually interesting regions of fluids. In an attempt to optimize the use of computational resources further in fluid animation, this paper extends this adaptive technique by modifying the mesh refinement scheme so that the camera's viewing properties are dynamically exploited during the simulation. Based on a simple adaptive mesh structure, we show that the new meshing strategy can save a substantial amount of computation time and memory space by using a view-dependent adaptive approach. The experimental results reveal that the proposed technique provides a good compromise between the computational effort and the simulation's fidelity, and may be used quite effectively in 3D animation production.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.203-215
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• 2006

The advent of KM(knowledge management) concept have led many organizations to seek an effective way to make use of their knowledge. But the absence of right tools for systematic handling of unstructured information makes it difficult to automatically retrieve and share relevant information that exactly meet user's needs. we propose a systematic method to enable content-based information retrieval from corpus of unstructured documents. In our method, a document is represented by using several key terms which are automatically selected based on their quantitative relevancy to the document. Basically, the relevancy is calculated by using a traditional TFIDF measure that are widely accepted in the related research, but to improve effectiveness of the measure, we exploited 'concept network' that represents term-term relationships. In particular, in constructing the concept network, we have also considered relative position of terms occurring in a document. A prototype system for experiment has been implemented. The experiment result shows that our approach can have higher performance over the conventional TFIDF method.

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.432-440
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• 2018

In this study, we propose a marine character prototype study for development of a marine character as digital contents. This study is a precedent study to build a marine character database with the production of digital contents based on "Jasan-urbo". "Jasan-urbo" is a representative cultural heritage that can highlight the value of Korean marine culture as the first illustrated book of marine creatures in Korea. Therefore, we examined the use value of "Jasan-urbo" through the content approach and looked at the visualization for character utilization and then designed a marine fish species classification standard model. Finally, this study proposed the possibility of discovering prototype sources for digitalization of Korean marine creature's resources.

• Proceedings of the Korea Water Resources Association Conference
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• 1991.07a
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• pp.221-230
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• 1991

Various Models of the moisture content-capillary pressure hysteresie based on the approach of domain concept in porous media are compared with each other, Model III-1(Mualem, 1984) is superio to others as expected. A new model based on Model III-1 is proposed of which asuumption is the linearization of P$$($\theta$) accounting for the pore blockage effect against air-entry. The feature of this model is that only one branch of boundary curve is needed to calibrate it, which is the advantage to Model III-1. The prediction of boundary drying curve from boundary wetting curve using this model is better than that using Model I-1, II-1 compared with the experimental data. This model is to simulate scanning curves, while Model I-1, II-1 is not.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.9
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• pp.1680-1691
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• 1994

We propose an extended hypertext data model based on object oriented paradigm that can easily the real world and semantics. We use the BNF notation to formalize the model. In our model, We introduce conceptional navigation by associating semantics on links and drive intelligent navigation using weights on links to alleviate user disorientation problem which is currently somewhat vague. We functionally classify the hypertext node into three types:Indexing node, Content node, Extract node and likewise classify the link into Alink type, Rlink type, Slink type. We believe that the typed node and typed link approach accommodate efficient query/search in hypertext.