• Journal of Digital Convergence
• /
• v.11 no.9
• /
• pp.147-151
• /
• 2013

On retrieving high frequency keywords in information retrieval system, mismatchings to user's request are problems because of the various meanings of keywords in the existing ontology configuration. In this paper, it is to construct personnel selection ontology and rules in personnel management which are composed of various concepts and knowledges based on semantic web technology and suggest selection procedures to support these rules and knowledge retrieval system to verify suitability of selection results. This system utilizes a method of extraction of speech features by using non-parametric correlation coefficient. This proposed method has been validated by showing that the result average SNR of the experiment evaluation of the proposed techniques was shown to be decreased by .752dB.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.17 no.4
• /
• pp.103-108
• /
• 2021

Knowledge graph is a technology that improves search results by using semantic information based on various resources. Therefore, due to these advantages, the knowledge graph is being defined as one of the core research technologies to provide AI-based services recently. However, in the case of the knowledge graph, since the form of knowledge collected from various service domains is defined as plain text, it is very important to be able to analyze the text and understand its meaning. Recently, various lexical dictionaries have been proposed together with the knowledge graph, but since most lexical dictionaries are defined in a language other than Korean, there is a problem in that the corresponding language dictionary cannot be used when providing a Korean knowledge service. To solve this problem, this paper proposes an ontology based on the parts of speech of Korean. The proposed ontology uses 9 parts of speech in Korean to enable the interpretation of words and their semantic meaning through a semantic connection between word class and word class. We also studied various scenarios to apply the proposed ontology to vehicle services.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.752-759
• /
• 2022

The instantiation of spaces as a discrete entity allows users to utilize BIM models in a wide range of analyses. However, in practice, their utility has been limited as spaces are erroneously entered due to human error and often omitted entirely. Recent studies attempted to automate space allocation using artificial intelligence approaches. However, there has been limited success as most studies focused solely on the use of geometric features to distinguish spaces. In this study, in addition to geometric features, semantic relations between spaces and elements were modeled and used to improve space classification in BIM models. Graph Convolutional Networks (GCN), a deep learning algorithm specifically tailored for learning in graphs, was deployed to classify spaces via a similarity graph that represents the relationships between spaces and their surrounding elements. Results confirmed that accuracy (ACC) was +0.08 higher than the baseline model in which only geometric information was used. Most notably, GCN was able to correctly distinguish spaces with no apparent difference in geometry by discriminating the specific elements that were provided by the similarity graph.

• The Journal of Society for e-Business Studies
• /
• v.23 no.2
• /
• pp.83-96
• /
• 2018

Extracting keywords representing documents is very important because it can be used for automated services such as document search, classification, recommendation system as well as quickly transmitting document information. However, when extracting keywords based on the frequency of words appearing in a web site documents and graph algorithms based on the co-occurrence of words, the problem of containing various words that are not related to the topic potentially in the web page structure, There is a difficulty in extracting the semantic keyword due to the limit of the performance of the Korean tokenizer. In this paper, we propose a method to select candidate keywords based on semantic similarity, and solve the problem that semantic keyword can not be extracted and the accuracy of Korean tokenizer analysis is poor. Finally, we use the technique of extracting final semantic keywords through filtering process to remove inconsistent keywords. Experimental results through real web pages of small business show that the performance of the proposed method is improved by 34.52% over the statistical similarity based keyword selection technique. Therefore, it is confirmed that the performance of extracting keywords from documents is improved by considering semantic similarity between words and removing inconsistent keywords.

• IE interfaces
• /
• v.25 no.1
• /
• pp.31-39
• /
• 2012

Discovering the suppliers capable of manufacturing the parts that satisfy buyer requirements via current online market places remains difficult due to semantic differences between what the suppliers can produce and what the buyer wants to acquire. One of the promising approaches to overcome the semantic difference is to adopt an ontology to describe the suppliers' manufacturing capabilities and the buyer requirements that range widely from manufacturing costs to eco-friendly design. Such an ontology dedicated to supplier discovery has yet to be developed. MSDL(Manufacturing Service Description Language) provides the basis for defining terms and their relationships in the ontology. Thus, the objective of this paper is to extend MSDL into a new ontology suitable for supplier discovery in mold manufacturing industry. In addition, a new ontology development method for supplier discovery will be proposed. Finally prototype demonstrations are provided to show a feasibility of the proposed ontology in mold manufacturing domains.

• Architectural research
• /
• v.12 no.2
• /
• pp.15-22
• /
• 2010

Our prediction model is based on the development of "Semantic Location Model." It embodies geometrical and topological information which can increase the efficiency in prediction and make it easy to manipulate the prediction model. Data mining is being implemented to extract the inhabitant's location patterns generated day by day. As a result, the self-learning system will be able to semantically predict the inhabitant's location in advance. This context-aware system brings about the key component of the ubiquitous computing environment. First, we explain the semantic location model and data mining methods. Then the location prediction model for the ubiquitous computing system is described in details. Finally, the prototype system is introduced to demonstrate and evaluate our prediction model.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2016.07a
• /
• pp.263-264
• /
• 2016

이메일은 모바일 기기의 발달과 SNS의 확산에도 불구하고 전 세계의 기업에게 가장 중요한 통신 및 업무수단으로 여전히 중요한 역할을 하고 있다. 이메일의 사용 증가로 인해 보안 및 보관 문제가 발생하였으며, 모든 이메일을 수년에 걸쳐 보관할 의무를 지는 기업이 점점 늘어나고 있다. 본 논문에서는 의미추론규칙을 이용하여 온톨로지 기반의 이메일 아카이빙 시스템에 대해 제안한다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.04a
• /
• pp.538-540
• /
• 2002

무선 환경에서 데이터 통신을 원하는 사용자의 요구는 점차 증가하고 있다. 현재까지 셀룰러 환경은 음성통신을 위해 주로 이용되어 왔으나 앞으로 주파수 대역폭을 높여 데이터 통신을 용이하게 할 수 있는 환경이 요구되고 있다. 이러한 환경을 위해 인포스테이션 개념이 도입되었고, 사용자가 필요로 하는 위치종속 데이터는 인포스테이션 환경을 기반으로 관리 할 수 있다. 인포스테이션에서 기존의 프리페칭 기법은 많은 양의 데이터를 이동 클라이언트에게 전송할 수 있었지만, 실제 사용자의 기호에 맞는 질의가 분석되어 제공하는데는 어려움이 있나. 따라서, 본 논문에서는 사용자가 필요로 하는 위치종속 데이터와 세만틱 서술(semantic description)을 인포스테이션을 통해 프리페칭하는 기법으로 세만틱 프리페칭(semantic prefetching)을 제안한다. 또한, 인포스테이션을 통한 위치종속 데이터의 세막틱 프리페칭 기법과 이동 사용자가 필요로 하는 데이터를 관리할 수 있는 새로운 캐쉬 관리 기법을 제시한다. 본 논문은 세만틱 프리페칭 기법과 새로운 캐쉬관리기법을 통해 사용자가 필요로 하는 위치종속 데이터에 대한 캐쉬 적중률을 향상시키고 적절한 캐쉬 교체를 가능하게 한다.

• Information Systems Review
• /
• v.2 no.2
• /
• pp.245-253
• /
• 2000

The paper presents methodology and techniques for design and implementation of the SENKOV System based on the validation of set membership and dictionaries. And it performs verb concept classification available for establishing the selectional restriction relationships among adverbs and verbs. The paper is important in that it has made the first attempt at classifying Korean verb concepts for the semantic analysis. We select about 600 Korean verbs which are commonly used in the daily life, and implements the SENKOV System. According to results of the experiments, SENKOV has 44 top nodes and depth of average 2.35, and that it can be utilized to classify Korean verb concept for the selectional restrictions among adverbs and verbs.

• Journal of Internet Computing and Services
• /
• v.13 no.1
• /
• pp.65-74
• /
• 2012

Cloud services are used for improving business. Moreover, customer relationship management(CRM) approaches use social networking as tools to enhance services to customers. However, most cloud systems do not support the semantic structures, and because of this, vital information from social network sites is still hard to process and use for business strategy. This paper proposes a collaboration framework based on social semantic web for cloud system. The proposed framework consists of components to support social semantic web to provide an efficient collaboration system for cloud consumers and service providers. The knowledge acquisition module extracts rules from data gathered by social agents and these rules are used for collaboration and business strategy. This paper showed the implementations of processing of social network site data in the proposed semantic model and pattern extraction which was used for the virtual grouping of cloud service providers for efficient collaboration.