• Journal of KIISE:Databases
• /
• v.32 no.2
• /
• pp.111-119
• /
• 2005

Electronic catalog contains ich semantics associated with products, and serves as a challenging practical domain for ontology application. Ontology is concerned with the nature and relations of being. It can play a crucial role in e-commerce as a formalization of e-Catalogs. Description Logics provide a theoretical core for most of the current ontology languages. In this paper, we present an ontological model of e-Catalogs in DL. We take an Extended Entity Relationship approach for conceptual modeling method, and present the fundamental set of modeling constructs and corresponding description language representation for each construct. Additional semantic knowledge can be represented directly in DL. Our modeling language stands within SHIQ(d) which is known reasonably practical with regard to its expressiveness and complexity. We illustrate sample scenarios to show how our approach may be utilized in modeling e-Catalogs, and also implement the scenarios through a DL inference tool to see the practical feasibility.

• Journal of KIISE:Computing Practices and Letters
• /
• v.13 no.2
• /
• pp.100-109
• /
• 2007

Object recognition of service robots is very important for most of services such as delivery, and errand. Conventional methods are based on the geometric models in static industrial environments, but they have limitations in indoor environments where the condition is changable and the movement of service robots occur because the interesting object can be occluded or small in the image according to their location. For solving these uncertain situations, in this paper, we propose the method that exploits observed objects as context information for predicting interesting one. For this, we propose the method for modeling domain knowledge in probabilistic frame by adopting Bayesian networks and ontology together, and creating knowledge model dynamically to extend reasoning models. We verify the performance of our method through the experiments and show the merit of inductive reasoning in the probabilistic model

• Journal of Information Management
• /
• v.39 no.4
• /
• pp.121-154
• /
• 2008

In this paper, a ontology-based keyword structuring method is proposed to represent the knowledge structure of scholarly documents and to make inferences from the semantic relationships holding among them. The characteristics of thesaurus as a knowledge organization system(KOS) for subject heading is critically reviewed from the information retrieval point of view. The domain concepts are identified and classified by analysis of the information activities occurring in a general research process based on scholarly sensemaking model. The ontological structure of keyword set is defined in terms of the semantic relationship of the canonical concepts which constitute scholarly documents such as journal articles. As a result, each ontologically structured keyword set of a document represents the knowledge structure of the corresponding document as semantic index. By means of the axioms and inference rules defined for information needs, users can efficiently explore the scholarly communication network built on the semantic relationship among documents in an analytic way based on the scholarly sensemaking model in oder to efficiently retrieve the relevant information for problem solving.

• Journal of KIISE:Software and Applications
• /
• v.34 no.3
• /
• pp.196-211
• /
• 2007

In the Product Line Engineering (PLE), current studies about an analysis of the feature have uncertain and ad-hoc criteria of analysis based on developer’s intuition or domain expert’s heuristic approach and difficulty to extract explicit features from a product in a product line because the stakeholders lack comprehensive understanding of the features in feature modeling. Therefore, this paper proposes a model of the analyzing commonality and variability of the feature based on the Ontology. The proposed model in this paper suggests two approaches in order to solve the problems mentioned above: First, the model explicitly expresses the feature by making an individual feature attribute list based on the meta feature modeling to understand common feature. Second, the model projects an analysis model of commonality and variability using the semantic similarity between features based on the Ontology to the stakeholders. The main contribution of this paper is to improve the reusability of distinguished features on developing products of same line henceforth.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2007.05a
• /
• pp.237-240
• /
• 2007

지식을 재사용하는 온톨로지 모델은 재사용 수준을 높여줄 수 있는 지식 모델이라 할 수 있다. 본 논문에서는 소프트웨어의 재사용 보다 지식을 재사용하기 위해 개발프로세스에서 지식과 소프트웨어모델 사이에 관련성 있는 매핑을 만들고자 한다. 또한 UML을 온톨로지 모델 언어로 사용하여 UML 기반 온톨로지 모델로부터 시스템 모델을 추출하기위해 온톨로지 도메인 시스템 방법을 제안한다.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2003.10a
• /
• pp.86-94
• /
• 2003

Electronically available biological literature has been accumulated exponentially in the course of time. So, researches on automatically acquiring knowledge from these tremendous data by text mining technology become more and more prosperous. However, most of the previous researches are technology oriented and are not well focused in practical extraction target, hence result in low performance and inconvenience for the bio-researchers to actually use. In this paper, we propose a more biology oriented target domain specific text mining system, that is, POSTECH bio-text mining system (POSBIOTM), for signal transduction pathway extraction, especially for G protein-coupled receptor (GPCR) pathway. To reflect more domain knowledge, we specify the concrete target for pathway extraction and define the minimal pathway domain ontology. Under this conceptual model, POSBIOTM extracts interactions and entities of pathways from the full biological articles using a machine learning oriented extraction method and visualizes the pathways using JDesigner module provided in the system biology workbench (SBW) [14]

• Journal of Information Technology and Architecture
• /
• v.9 no.3
• /
• pp.233-241
• /
• 2012

Architecture framework (AF) is a guideline to define components needed to develop and operate enterprise architecture (EA), and to define relationships among the components. There are many architecture frameworks to operate EA of governments and businesses such as Zachman framework, DoDAF, TOGAF, FEAF, and TEAF. DoDAF is the most representative AF to support the development of the EA in the military domain. DoDAF is composed of eight viewpoints and 40 views that are affiliated with the viewpoints. To develop an AF for a specific goal, system architects decide a set of views. Furthermore, they determine data that are needed for a view modeling. However, views and data in DoDAF are structurally inter-related explicitly and/or implicitly. So, developing an AF for a specific goal is going to be a project to be carried out over a long haul. To reduce the burden of its development, in this paper, we develop ONT-SAF (Ontology for DoDAF) that can infer inter-relationships like referential and transitive relationships and the sequences among the views. Furthermore, to promote reusability and consistency of the views and the data within an AF, we adopt the view-data separation strategy. ONT-DAT contains classes like 'viewpoint', 'view', 'data', 'expression method', and 'reference model', and 11 properties including 'hasView.' To prove the effectiveness of ONT-SAF, we perform a case study.

• Genomics & Informatics
• /
• v.17 no.2
• /
• pp.14.1-14.6
• /
• 2019

The total number of scholarly publications grows day by day, making it necessary to explore and use simple yet effective ways to expose their metadata. Schema.org supports adding structured metadata to web pages via markup, making it easier for data providers but also for search engines to provide the right search results. Bioschemas is based on the standards of schema.org, providing new types, properties and guidelines for metadata, i.e., providing metadata profiles tailored to the Life Sciences domain. Here we present our proposed contribution to Bioschemas (from the project "Biotea"), which supports metadata contributions for scholarly publications via profiles and web components. Biotea comprises a semantic model to represent publications together with annotated elements recognized from the scientific text; our Biotea model has been mapped to schema.org following Bioschemas standards.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.06a
• /
• pp.65-70
• /
• 2005

This paper introduces an approach to multi-layered product knowledge model for collaborative engineering environment. The participants in collaborative engineering want to share and reason product knowledge through internet without any heterogeneity and ambiguity. However the previous knowledge models are limited in providing those aspects. In this paper, the collaborative engineering domain is analyzed and then the product knowledge is organized into four levels such as product context model, product specific model, product design model and product manufacturing model. The four levels are represented by first-order logic in layered fashion. The concepts and the instances of a formal ontology are used for recursive representation of the four levels. The instances of the concepts of an upper level like product context model are considered as the concepts of an adjacent lower level like product specific model, and this mechanism is applied to the other levels. These logic representations are integrated with the schema and the instances of a relational database. OWL representation of the four levels is defined through the integration of the logic representation and OWL primitives. The four product knowledge models have their major representation according to the characteristics of each model. This approach enables engineer to share product knowledge through internet without any ambiguity and utilize it as basis for additional reasoning.

• Journal of KIISE:Software and Applications
• /
• v.34 no.6
• /
• pp.549-561
• /
• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.