• Korean Linguistics
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• v.48
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• pp.223-268
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• 2010

The purpose of this paper is to build a semantic networks of the 'cooking class' verb (based on 'CoreNet' of KAIST). This proceedings needs to adjust the concept classification. Then sub-categories of [Cooking] and [Foodstuff] hierarchy of CoreNet was adjusted for the construction of verb semantic networks. For the building a semantic networks, each meaning of 'Cooking verbs' of Korean has to be analyzed. This paper focused on the Korean 'heating' verbs and 'non-heating'verbs. Case frame structure and argument information were inserted for the describing verb information. This paper use a Propege 3.3 as a tool for building "cooking verb" semantic networks. Each verb and noun was inserted into it's class, and connected by property relation marker 'HasThemeAs', 'IsMaterialOf'.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.321-329
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• 2007

This paper reports on the application of network analysis approaches to investigate the characteristics of graph representations of Japanese word associations. Two semantic networks are constructed from two separate Japanese word association databases. The basic statistical features of the networks indicate that they have scale-free and small-world properties and that they exhibit hierarchical organization. A graph clustering method is also applied to the networks with the objective of generating hierarchical structures within the semantic networks. The method is shown to be an efficient tool for analyzing large-scale structures within corpora. As a utilization of the network clustering results, we briefly introduce two web-based applications: the first is a search system that highlights various possible relations between words according to association type, while the second is to present the hierarchical architecture of a semantic network. The systems realize dynamic representations of network structures based on the relationships between words and concepts.

• Journal of Information Technology Applications and Management
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• v.9 no.2
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• pp.71-80
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• 2002

Automatic language translation has greatly advanced due to the increased user needs and Information retrieval in WWW. This paper utilizes the integrated knowledge bases of noun and verb networks for automatic translation of ambiguous words in the Korean sentences, through the selectional restriction relation in the sentences. And this paper presents the method to verify validity of Korean noun semantic networks that are used for the construction of the selectional restriction relation by applying the networks to the syntactic and semantic properties Integration of Korean Noun Networks into the SENKOV system will provide the accurate and efficient knowledge bases for the semantic analysis of Korean NLP.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.1-13
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• 2004

This paper presents a method for the multilingual product retrieval agent through XML and the semantic networks in e-commerce. Retrieval for products is an important process, since it represents interfaces of the customer contact to the e-commerce. Keyword-based retrieval is efficient as long as the product information is structured and organized. But when the product information is expressed across many online shopping malls, especially when it is expressed in different languages with cultural backgrounds, buyers' product retrieval needs language translation with ambiguities resolved in a specific context. This paper presents a RDF modeling case that resolves semantic problems in the representation of product information and across the boundaries of language domains. With adoption of UNSPSC code system, this paper designs and implements an architecture for the multilingual product retrieval agents. The architecture is based on the central repository model of product catalog management with distributed updating processes. It also includes the perspectives of buyers and suppliers. And the consistency and version management of product information are controlled by UNSPSC code system. The multilingual product names are resolved by semantic networks, thesaurus and ontology dictionary for product names.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3942-3961
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• 2019

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ${\times}8$, our method performs favorably against other methods in terms of gradients similarity.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1318-1330
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• 2018

Video captioning refers to the process of extracting features from a video and generating video captions using the extracted features. This paper introduces a deep neural network model and its learning method for effective video captioning. In this study, visual features as well as semantic features, which effectively express the video, are also used. The visual features of the video are extracted using convolutional neural networks, such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction network proposed in this paper. Further, an attention-based caption generation network is proposed for effective generation of video captions using the extracted features. The performance and effectiveness of the proposed model is verified through various experiments using two large-scale video benchmarks such as the Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.727-731
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• 2010

Recently, mobile life-log data are collected by mobile devices and used to recode one's life. In order to help a user search data, a mobile life-log semantic network is introduced for storing logs and retrieving associative information. However, associative search systems on common semantic networks in previous studies provide for a user with only found data as text to users. This paper proposes an associative search system for mobile life-log semantic network that supports selection and keyword associative search of which a process and result are a visualized graph representing associative data and their relationships when a user inputs a keyword for search. In addition, by using semantic abstraction, the system improves user's understanding of search result and simplifies the resulting graph. The system's usability was tested by an experiment comparing the system and a text-based search system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1807-1824
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• 2015

As the technologies advance, mobile peer-to-peer (MP2P) networks or systems become one of the major ways to share resources and information. On such a system, the information retrieval (IR), including the development of scalable infrastructures for indexing, becomes more complicated due to a huge increase on the amount of information and rapid information change. To keep the systems on MP2P networks more reliable and consistent, the index structures need to be updated frequently. For a semantic IR system, the index structure is even more complicated than a classic IR system and generally has higher update cost. The most well-known indexing technique used in semantic IR systems is Latent Semantic Indexing (LSI), of which the index structure is generated by singular value decomposition (SVD). Although LSI performs well, updating the index structure is not easy and time consuming. In an MP2P environment, which is fully distributed and dynamic, the update becomes more challenging. In this work, we consider how to update the sematic index generated by LSI and keep the index consistent in the whole MP2P network. The proposed Concept Space Update (CSU) protocol, based on distributed 2-Phase locking strategy, can effectively achieve the objectives in terms of two measurements: coverage speed and update cost. Using the proposed effective synchronization mechanism with the efficient updates on the SVD, re-computing the whole index on the P2P overlay can be avoided and the consistency can be achieved. Simulated experiments are also performed to validate our analysis on the proposed CSU protocol. The experimental results indicate that CSU is effective on updating the concept space with LSI/SVD index structure in MP2P semantic IR systems.

• Journal of Multimedia Information System
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• v.4 no.3
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• pp.123-144
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• 2017

Personalized media includes user-targeted and user-generated content (UGC) exchanged through social media and interactive applications. The increased consumption of UGC presents challenges and opportunities to multimedia information systems. We work towards modeling a deep structure for content networks. To gain insights, a hybrid practice with Media Framework (MF) is presented for network creation of personalized media, which leverages the authoring methodology with user-generated semantics. The system's vertical integration allows users to audition their personalized media networks in the context of a global system network. A navigation scheme with dynamic GUI shifts the interaction paradigm for content query and sharing. MF adopts a multimodal architecture anticipating emerging use cases and genres. To model diversification of platforms, information processing is robust across multiple technology configurations. Physical and virtual networks are integrated with distributed services and transactions, IoT, and semantic networks representing media content. MF applies spatiotemporal and semantic signal processing to differentiate action responsiveness and information responsiveness. The extension of multimedia information processing into authoring enables generating interactive and impermanent media on computationally enabled devices. The outcome of this integrated approach with presented methodologies demonstrates a paradigmatic shift of the concept of UGC as personalized media network, which is dynamical and evolvable.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.238-246
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• 2011

This paper proposes a new semantic representation and its associated similarity measure. The representation expresses textual context observed in a context of a certain term as a network where nodes are terms and edges are the number of cooccurrences between connected terms. To compare terms represented in networks, a graph kernel is adopted as a similarity measure. The proposed representation has two notable merits compared with previous semantic representations. First, it can process polysemous words in a better way than a vector representation. A network of a polysemous term is regarded as a combination of sub-networks that represent senses and the appropriate sub-network is identified by context before compared by the kernel. Second, the representation permits not only words but also senses or contexts to be represented directly from corresponding set of terms. The validity of the representation and its similarity measure is evaluated with two tasks: synonym test and unsupervised word sense disambiguation. The method performed well and could compete with the state-of-the-art unsupervised methods.