• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.202-204
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• 2005

상품 온톨로지는 온톨로지 (Ontology) 구성요소인 개념 (Concept) 과 속성 (Property)이 상품 도메인에 특화된 온틀로지이다. 본 논문에서는 대용량을 특징으로 하는 상품 온톨로지를 표현함에 적용되어 질 수 있는 Hyperbolic Tree, Cluster Map, 그래프 비주얼라이제이션을 살펴보고, 계층을 갖는 개념을 표현하는 데 좋은 Hyperbolic과, 속성을 잘 표현 할 수 있는 Cluster Map을 상품 온톨로지에 유리한 비주얼라이제이션으로서 제안한다.

• Journal of KIISE
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• v.43 no.10
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• pp.1115-1123
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• 2016

Assumption-based truth maintenance system (ATMS) is a tool that maintains the reasoning process of inference engine. It also supports non-monotonic reasoning based on dependency-directed backtracking. Bookkeeping all the reasoning processes allows it to quickly check and retract beliefs and efficiently provide solutions for problems with large search space. However, the amount of data has been exponentially grown recently, making it impossible to use a single machine for solving large-scale problems. The maintaining process for solving such problems can lead to high computation cost due to large memory overhead. To overcome this drawback, this paper presents an approach towards incrementally maintaining the reasoning process of inference engine on cluster using Spark. It maintains data dependencies such as assumption, label, environment and justification on a cluster of machines in parallel and efficiently updates changes in a large amount of inferred datasets. We deployed the proposed ATMS on a cluster with 5 machines, conducted OWL/RDFS reasoning over University benchmark data (LUBM) and evaluated our system in terms of its performance and functionalities such as assertion, explanation and retraction. In our experiments, the proposed system performed the operations in a reasonably short period of time for over 80GB inferred LUBM2000 dataset.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.29-45
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• 2015

This paper proposes an implementation of SPARQL-DL, which is a query language for OWL ontologies, for query-answering over the OWL ontologies virtually generated from existing RDBs. The proposed SPARQL-DL processor internally translates input SPARQL-DL queries into SQL queries and then executes the translated queries. There are two advantages in the query processing method. First, another repository to store OWL ontologies generated from RDBs is not required. Second, a large ABox generated from an RDB instance is able to be served without using Tableau algorithm based reasoners which have a problem in large ABox reasoning. Our algorithm for query rewriting is designed to create one corresponding SQL query from one input SPARQL-DL query to minimize the overhead by establishing connections with RDBs.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.240-247
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• 2014

Data which is created and shared on the Web is characterized by the massive amount of user generated content on various applications and dynamically evolving content on the basis of user interests. Thus, in order to benefit from Web data, it is essential to provide (a) the mechanisms which enable scalable processing of large data collections and (b) the organization schemes which reduce the navigational overhead within complex and dynamically growing content. Between these two impending needs, in this paper, we are interested in developing an indexing scheme which aims to reduce the time and effort needed to access the relevant piece of information by leveraging ontologies. In particular, considering evolving nature of Web contents, the proposed technique in this paper computes the sub-ontology, which best matches a given data collection, from the existing large size of ontology. Case studies show that the proposed indexing scheme in this paper indeed helps organize dynamically evolving content.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.153-173
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• 2013

The ontology has been gaining increasing interests by recent arise of the semantic web and related technologies. The focus is mostly on inference query processing that requires high-level techniques for storage and searching ontologies efficiently, and it has been actively studied in the area of semantic-based searching. W3C's recommendation is to use RDFS and OWL for representing ontologies. However memory-based editors, inference engines, and triple storages all store ontology as a simple set of triplets. Naturally the performance is limited, especially when a large-scale ontology needs to be processed. A variety of researches on proposing algorithms for efficient inference query processing has been conducted, and many of them are based on using proven relational database technology. However, none of them had been successful in obtaining the complete set of inference results which reflects the five characteristics of the ontology properties. In this paper, we propose a new index structure called hyper cube index to efficiently process inference queries. Our approach is based on an intuition that an index can speed up the query processing when extensive inferencing is required.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.178-180
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• 2012

협업은 둘 이상의 사람들이 하나의 업무 또는 목적을 달성하기위해 공동으로 협력하여 일하는 것이다. 최근 개인 및 조직 간 협업 범위가 공동분석, 데이터 연계, 서비스 조합 등으로 확장되고 대용량 데이터 공유 및 실시간 연계분석 활동이 증대되면서 협업 지향적인 시스템 설계와 개발이 중요시 되고 있다. 특히 스마트워크와 지능화된 협업 기반은 데이터, 프로세스, 서비스, 사람 간의 다차원 연계와 실시간 활용, 의미 기반의 기계적 협력을 전재로 하고 있다. 본 연구에서는 Data, Process, Service, People 측면의 4가지 계층으로 전사적 자원을 설계하고 메타 메타데이터 기반의 온톨로지 분석을 통해 자원 간의 연계와 조합을 지원하는 시스템을 설계했다. Data 계층은 프로세스별 Input, Output 정보를 식별하여 Data의 메타 정보를 정의하고 이를 검색 에이전트가 색인하여 모델링에 참조할수록 한다. Process 계층은 BPMN모델을 확장한 exCPM의 개선 모델을 바탕으로 프로세스를 수행주체 간, 정보공유측면에서 프로세스를 분석했다. Service 계층은 협업지향적인 프로세스를 구성하는 컴포넌트를 서비스로 인식하고 프로파일을 통해 협업을 위한 검색과 프로세스를 연계지원하도록 설계 했다. 마지막으로 People계층은 자원, 프로세스, 서비스 등 시스템에 관여하는 참여자들의 메타정보를 정의하고 이를 온톨로지 기반의 모델에 통합하여 자동 검색되도록 설계했다. 이를 통해 프로세스와 서비스 측면에서 협업을 요구하는 에이전트와 일반 검색 사용자들이 프로세스 간 협업 자원을 파악하고 상호 관계를 분석할 수 있도록 하는 한편, 프로세스를 지원하는 컴포넌트와 서비스 간의 자동적인 조합을 통해 통합적 자원 협력과 실시간 협업 지원 기반을 제시했다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.32-37
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• 2008

To ensure access to rapidly growing video collection, video indexing is becoming more and more important. In this paper, video ontology system for retrieving a video data based on a scene unit is proposed. The proposed system creates a semantic scene as a basic unit of video retrieval, and limits a domain of retrieval through a subject of that scene. The content of semantic scene is defined using the relationship between object and event included in the key frame of shots. The semantic gap between the low level feature and the high level feature is solved through the scene ontology to ensure the semantic-based retrieval.

• Journal of KIISE
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• v.42 no.9
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• pp.1109-1116
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• 2015

In this paper, we propose a query processing approach that uses the Spark functional programming and distributed memory system to solve the computational overhead of SPARQL. In the semantic web, RDF ontology data is produced at large scale, and the main challenge for the semantic web is to query and manipulate such a large ontology with a high throughput. The most existing studies on SPARQL have focused on deploying the Hadoop MapReduce framework, and although approaches based on Hadoop MapReduce have shown promising results, they achieve a low level of throughput due to the underlying distributed file processes. Therefore, in order to speed up the query processes, we suggest query- processing methods that are based on memory caching in distributed memory system. Our approach is also integrated with a clause unification method for propagating between the clauses that exploits Spark join, map and filter methods along with caching. In our experiments, we have achieved a high level of performance relative to other approaches. In particular, our performance was nearly similar to that of Sempala, which has been considered to be the fastest query processing system.

• Journal of KIISE
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• v.45 no.2
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• pp.113-125
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• 2018

Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.

• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.118-123
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• 2008

온톨로지란 실세계에 존재하는 사물 및 개념, 그리고 용어들 간의 관계들을 컴퓨터가 이해할 수 있는 형태로 표현한 것이다. 온톨로지 구축에 있어서 대용량 코퍼스의 활용은 해당코퍼스에서 등장하는 용어들과 이들 사이에서 나타나는 문자열을 일종의 패턴으로 취급하여 특정 패턴과 함께 나타나는 용어 쌍들을 해당 패턴이 대표하는 의미 관계로 설정하는 방식을 취한다. 그러나 기존의 방법은 주로 두 용어들 사이에서 나타나는 문자열만을 고려하여 패턴을 추출하기 때문에 해당 문장에 포함된 보다 다양한 문장 정보들을 활용할 수 없다. 본 논문은 이러한 한계점을 감안하여, 용어 쌍 사이에서 나타나는 문자열과 주변 동사 정보를 함께 고려함으로써 패턴의 정교성을 향상시키는 방법을 제안한다. 또한 동사들의 동의어를 활용하여 다양한 용어들을 포괄할 수 있는 일반화된 패턴을 구축한다. 본 방법론은 is-a 관계의 경우 64%, part-of 관계의 경우 83%, made-of 관계의 경우 73%, use 관계의 경우 72%의 정확률을 보였으며 모두 기존 방법보다 향상된 결과를 가져왔다.