• Journal of KIISE
• /
• v.43 no.4
• /
• pp.470-479
• /
• 2016

As the number of various types of media data such as UCC (User Created Contents) increases, research is actively being carried out in many different fields so as to provide meaningful media services. Amidst these studies, a semantic web-based media classification approach has been proposed; however, it encounters some limitations in video classification because of its underlying ontology derived from meta-information such as video tag and title. In this paper, we define recognized objects in a video and activity that is composed of video objects in a shot, and introduce a reasoning approach based on description logic. We define sequential rules for a sequence of shots in a video and describe how to classify it. For processing the large amount of increasing media data, we utilize Spark streaming, and a distributed in-memory big data processing framework, and describe how to classify media data in parallel. To evaluate the efficiency of the proposed approach, we conducted an experiment using a large amount of media ontology extracted from Youtube videos.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.10b
• /
• pp.352-356
• /
• 2006

현존하는 추론 엔진들은 대부분 Tableaux 알고리즘 기반의 TBox의 최적화를 위한 연구를 진행하였다. 하지만 현실에서 대용량의 ABox를 추론하기 위한 유한한 시간 내에 결정 가능성을 보장하지 못한다. 따라서 실용성 있는 추론 엔진 효율을 위해서는 대용량 데이터를 가지는 ABox를 위한 최적화된 추론 기법이 필요하다. 본 논문에서는 OWL-DL 기반의 온톨로지(Ontology)를 데이터로그(Datalog)와 같은 규칙(Rule) 형태로 변형하여 관계형 데이터베이스와 같은 저장 시스템과 연동하기 위한 방법을 이용한다. 최종적으로 실세계의 환경에서의 데이터타입 속성(Datatype Property)이 포함된 SHIQ(D) 구성의 실용적인 추론 시스템을 수행하고자 한다. 따라서 OWL이 가지는 공리(Axiom)를 이용하여 데이터타입 속성이 포함된 규칙을 적용한 추론 방법에 대해서 제안하였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.10b
• /
• pp.16-19
• /
• 2006

본 논문은 2006년부터 시작된 "국가 IT 온톨로지 인프라 기술개발" 과제의 온톨로지 구축 부분을 소개한다. 이 과제는 2006년부터 2011년까지의 5년 과제로 산학연이 참여하여, 국가 IT 분야에 범용적으로 활용이 가능한 IT 온톨로지를 구축하고 인터넷, 인트라넷, 유비쿼터스 환경에서 제공되는 각종 IT 서비스에 적용하여 seamless 서비스를 제공하는 것을 목표로 하고 있다. 이 온톨로지는 한-영 2개의 언어로 제작되며, 국제표준 언어인 OWL을 사용하여 국내외적으로 널리 사용될 수 있는 대용량 IT 온톨로지를 목표로 한다.

• Journal of Intelligence and Information Systems
• /
• v.16 no.1
• /
• pp.1-16
• /
• 2010

Several techniques and methods for ontology modularization have been proposed recently. However, there are few ontology evaluation frameworks to evaluate these techniques and methods. Most researches on ontology modularization have not been focused on ontology modularization evaluation but ontology modularization process itself. In this paper, we devise a novel ontology modularization evaluation framework to measure the quality of ontology modules, logical integrity during modularization process and modularization tools. Experiments were conducted to validate the proposed framework. Three representative modularization approaches SWOOP, Prompt, and PATO were chosen and used to partition or extract modules from an ontology. Then the proposed evaluation framework is applied to these modules. The experiment results indicate that the modularization framework works well. The proposed framework would help ontology engineers improve ontology module quality, anticipate and reduce future maintenance as well as help ontology users to choose ontology modules that best meet their requirements.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.9 no.2
• /
• pp.415-421
• /
• 2008

The increasing interest in ontologies is driven by the large volumes of information now available as well as by the increasing complexity and diversity of this information. These trends have also increased interest in automating many activities that were traditionally performed manually. We are currently engaged in this paper that have realized benefits in productivity and clarity by utilizing UML class diagrams to develop and to display complex OWL ontologies. UML has many features, such as profiles, global modularity and extension mechanisms that are not generally available in most ontology languages. However, ontology languages have some features that UML does not support. Our paper identifies the similarities and differences between UML and the ontology languages RDF and OWL. To reconcile these differences, we propose a modification to the UML metamodel to address some of the most problematic differences.

• Journal of KIISE
• /
• v.42 no.7
• /
• pp.852-859
• /
• 2015

As the amount of knowledge information significantly increases, a lot of progress has been made in the studies focusing on how to reason large scale ontology effectively at the level of RDFS or OWL. These reasoning methods are divided into TBox classifications and ABox realizations. A TBox classification mainly deals with integrity and dependencies in schema, whereas an ABox realization mainly handles a variety of issues in instances. Therefore, the ABox realization is very important in practical applications. In this paper, we propose a realization method for analyzing the constraint of the specified class, so that the reasoning system automatically infers the classes to which instances belong. Unlike conventional methods that take advantage of the object oriented language based distributed file system, we propose a large scale ontology reasoning method using spark, which is a functional programming-based in-memory system. To verify the effectiveness of the proposed method, we used instances created from the Wine ontology by W3C(120 to 600 million triples). The proposed system processed the largest 600 million triples and generated 951 million triples in 51 minutes (696 K triple / sec) in our largest experiment.

• Journal of Intelligence and Information Systems
• /
• v.13 no.4
• /
• pp.91-104
• /
• 2007

Ontology matching technique is one of the most important techniques in the Semantic Web as well as in other areas. Ontology matching algorithm takes two ontologies as input, and finds out the matching relations between the two ontologies by using some parameters in the matching process. Ontology matching is very useful in various areas such as the integration of large-scale ontologies, the implementation of intelligent unified search, and the share of domain knowledge for various applications. In general cases, the performance of ontology matching is estimated by measuring the matching results such as precision and recall regardless of the requirements that came from the matching environment. Therefore, most research focuses on controlling parameters for the optimization of precision and recall separately. In this paper, we focused on the harmony of precision and recall rather than independent performance of each. The purpose of this paper is to propose a methodology that determines parameters for the desired ratio of precision and recall that is appropriate for the requirements of the matching environment.

• Journal of KIISE
• /
• v.43 no.1
• /
• pp.87-95
• /
• 2016

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.

• Language and Information
• /
• v.9 no.1
• /
• pp.19-34
• /
• 2005

Ontology is an indispensable component in intelligent and semantic processing of knowledge and information, such as in semantic web. However, ontology construction requires vast amount of data collection and arduous efforts in processing these un-structured data. This study proposed a methodology to automatically construct and generate ontologies from Sejong Electronic Dictionary. As Sejong Electronic Dictionary is structured in XML format, it can be processed automatically by computer programmed tools into an OWL(Web Ontology Language)-based ontologies as specified in W3C . This paper presents the process and concrete application of this methodology.

• Proceedings of the Korean Information Science Society Conference
• /
• 2010.06c
• /
• pp.353-357
• /
• 2010

많은 연구로부터 다양한 온톨로지들이 구축되었다. 온톨로지가 표현하는 영역이 점차 넓어짐에 따라 온톨로지의 크기가 증가하였으나 이를 위한 효율적인 저장방법은 연구되지 않았다. 또한 다양한 온톨로지의 사용방법 중 서술 논리를 사용한 추론은 온톨로지의 크기가 작아도 연산이 매우 많이 필요하여 실제로 사용하기가 매우 어렵다. 본 논문에서는 점차 커지는 온톨로지를 효과적으로 저장하기 위하여 온톨로지를 컴퓨터 클라우드에 저장하는 방법과 컴퓨터 클라우드에 저장된 온톨로지를 추론하기 위한 프레임워크를 제안한다. 그리고 실험을 통하여 제안한 방법이 기존의 방법에 비하여 효율적임을 보였다.