• Journal of the Korean Institute of Telematics and Electronics
• /
• 제25권10호
• /
• pp.1193-1201
• /
• 1988

In this paper, we describe the syntactic and semantic parsing methods which use the case frames. The case structures based on obligatory cases of verbs. And, we use a small set of partial-garammar rules based on simple sentence to represent such case structures. Also, we enhance the efficiency by constructing independent procedure for particle classification and ambiguity resolution of major particle considering the importance of Japanese particle process in the generation. And we construct the generation table considering the combination possibility between the verbs and auxiliary verbs for processing the termination phrase. Therefore we can generate more natural translated sentence according to unique decision with information of syntactic analysis and simplify the generating process.

• The Journal of Korean Institute of Next Generation Computing
• /
• 제13권2호
• /
• pp.102-110
• /
• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권3호
• /
• pp.1722-1741
• /
• 2017

Intrusion detection techniques based on virtual machine introspection (VMI) provide high temper-resistance in comparison with traditional in-host anti-virus tools. However, the presence of semantic gap also leads to the performance and compatibility problems. In order to map raw bits of hardware to meaningful information of virtual machine, detailed knowledge of different guest OS is required. In this work, we present VDSM, a lightweight and general approach based on driver separation mechanism: divide semantic view reconstruction into online driver of view generation and offline driver of semantics extraction. We have developed a prototype of VDSM and used it to do intrusion detection on 13 operation systems. The evaluation results show VDSM is effective and practical with a small performance overhead.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 한국지능정보시스템학회 2002년도 추계정기학술대회
• /
• pp.311-320
• /
• 2002

시맨틱 웹은 인간이 이해하는 것처럼 웹 문서의 의미를 컴퓨터가 처리할 수 있도록 하는데 있다. 그러나 인터넷 등 정보통신 기술의 발전으로 인해 정보량이 급증함으로써 이들 정보 자원을 효과적으로 검색하기에는 많은 어려움이 있다. 이러한 문제점을 해결하기 위해 본 논문에서는 주석 에디터를 사용하여 논문에 대한 RDF 메타데이타의 자동 생성 방법을 제안한다. 사용자가 논문을 주석 처리할 때, 문서에 대한 특징을 추출하고 온토로지 인터페이스를 사용하여 문서를 분류한다. 구현된 시스템을 통해 사용자는 추출된 메타데이타를 메타데이타 뷰를 통해 볼 수 있으며, HTML 뷰를 통해 메타데이타를 수동으로 수정이 가능하다. 이 메타데이타는 RDF Repository로 저장할 수 있으며, 주석 뷰를 통하여 RDF 메타데이타 생성을 확인할 수 있다. 이렇게 생성된 RDF 메타데이타는 웹 로봇이 내용의 의미 파악 및 카테고리 정보를 쉽게 알 수 있도록 해준다. 본 논문은 검색 엔진을 통하여 논문 검색시 전체 내용보다 RDF 메타데이타 정보만으로 효율적인 검색을 할 수 있는 방법에 초점을 둔다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 한국정보처리학회 2002년도 추계학술발표논문집 (상)
• /
• pp.805-808
• /
• 2002

현재까지의 웹은 HTML을 기반으로 이루어져 왔으나, HTML은 주로 표현 중심으로 사용되어 사용자의 목적을 만족시키기에 부족하다는 문제점을 지니고 있다. 최근 목적에 부합된 정보를 효율적으로 추출하여 적절한 정보를 생성하는 문제가 점차 중요시되고 있고, 이를 통해서 웹 상의 정보에 컴퓨터가 이해할 수 있는 의미(Semantic)를 부여하여 사람과 컴퓨터간의 협동 작업을 원활하게 하기 위한 시맨틱 웹이 제안되었다. 본 논문에서는 웹 상의 모든 정적인 성격의 정보와 동적인 성격의 서비스를 하나의 데이터베이스처럼 운용할 수 있는 이상을 실현하는데 가교 역할을 할 시맨틱 웹을 사용하여 e-Business 플랫폼을 제시하였다.

• Annual Conference on Human and Language Technology
• /
• 한국정보과학회언어공학연구회 2020년도 제32회 한글 및 한국어 정보처리 학술대회
• /
• pp.215-219
• /
• 2020

현재 한글 언어학 영역에서는 많은 언어 분석 연구가 수행되었다. 또한 소프트웨어공학의 요구공학 영역에서는 명료한 요구사항 정의와 분석이 필요하고, 비정형화된 요구사항 명세서로부터 테스트 케이스 추출이 매우 중요한 이슈이다. 즉, 자연어 기반의 요구사항 명세서로부터 원인-결과 그래프(Cause-Effect Graph)를 통한 의사 결정 테이블(Decision Table) 기반 테스트케이스(Test Case)를 자동 생성하는 방법이 거의 없다. 이런 문제를 해결하기 위해 '한글 언어 의미 분석 기법'을 '요구공학 영역'에 적용하는 방법이 필요하다. 본 논문은 비정형화된 요구사항으로부터 테스트케이스 생성하는 과정의 중간 단계인 요구사항에서 문장 의미 모델(Sentence Semantic Model)을 자동 생성하는 방법을 제안 한다. 이는 요구사항으로부터 생성된 원인-결과 그래프의 정확성을 검증할 수 있다.

• The KIPS Transactions:PartB
• /
• 제14B권3호
• /
• pp.199-214
• /
• 2007

Recently, there have been a lot of works trying to realize automated composition of semantic web services though application of AI planning techniques. The traditional AI planning techniques, however, have some limitations: it is not easy to represent a web service process with complex control constructs as an action or a plan; it is hardly possible to consider enough the rich information contained in domain ontologies during the planning process; it is impossible to model directly the data flow from the outputs of a web service to the inputs of another web service; it is difficult to predict and deal with uncertainty and dynamics of the environment because the plan generation phase is supposed to be separated from the plan execution phase. In order to overcome some of these limitations, this paper suggests a reactive planning approach to automated composition of semantic web services. Through some experiments using several e-commerce web services, we found that the reactive planning is an effective way to realize automated composition of semantic web services.

• The KIPS Transactions:PartA
• /
• 제14A권4호
• /
• pp.209-214
• /
• 2007

In conventional game systems, virtual environments usually have just the role of a background without the direct relationships for game characters. nev do not consider the semantics about virtual environments. In this paper, we develop a game navigation system that provides semantic information about virtual environments including geographical, historical or my other location-dependent information. Then, the game character obtains the geographical location and its related information when it navigates through a virtual environment. It can be an implementation method for a semantic virtual environment because it can have the environment maintain its semantics depending on the specific location. In addition, we describe a method that can control a character's motion in the semantic virtual environment interactively, and that can input specific information according to the location of the character.

• Journal of KIISE:Software and Applications
• /
• 제33권11호
• /
• pp.986-998
• /
• 2006

There has been an increasing necessity of Semantic Web-based metadata that helps computers efficiently understand and manage an information increased with the growth of Internet. However, it seems inevitable to face some semantically ambiguous information when metadata is generated. Therefore, we need a solution to this problem. This paper proposes a new method for automated metadata generation with the help of a concept of class, in which some ambiguous words imbedded in information such as documents are semantically more related to others, by using probability model of consequent words. We considers ambiguities among defined concepts in ontology and uses the Hidden Markov Model to be aware of part of a named entity. First of all, we constrict a Markov Models a better understanding of the named entity of each class defined in ontology. Next, we generate the appropriate context from a text to understand the meaning of a semantically ambiguous word and solve the problem of ambiguities during generating metadata by searching the optimized the Markov Model corresponding to the sequence of words included in the context. We experiment with seven semantically ambiguous words that are extracted from computer science thesis. The experimental result demonstrates successful performance, the accuracy improved by about 18%, compared with SemTag, which has been known as an effective application for assigning a specific meaning to an ambiguous word based on its context.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권3호
• /
• pp.877-893
• /
• 2022

With the development of deep learning, face inpainting has been significantly enhanced in the past few years. Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc. To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting. The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network. Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information. To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model. We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset. The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet. Our model surpasses the compared models on L1, PSNR and SSIM metrics. When the masked region is less than 20%, L1 loss reduce by more than 4.3%.