• Annual Conference on Human and Language Technology
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• 2014.10a
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• pp.228-232
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• 2014

지식 베이스(Knowledge Base)는 주어진 질의 문에 대한 잠재적인 답과 답에 대한 단서가 될 수 있는 구조화된 형태의 정보를 포함하고 있기 때문에 질의응답 시스템에서 매우 중요하다. 하지만 비록 DBpedia, Freebase, YAGO 등과 같이 이용 가능한 여러 개의 지식 베이스가 존재함에도 불구하고 이러한 지식 베이스에 포함되어 있는 정보는 웹(Web)상에 존재하는 정보에 비하면 매우 제한적이다. 본 논문에서는 무제한 정보 추출 기술을 이용하여 정형화되지 않은 텍스트로부터 트리플(Triple)을 추출하고, 추출된 트리플의 각 개체 및 관계 어휘를 대상 온톨로지(Ontology) 상의 어휘에 사상시킴으로써 지식 베이스를 확장 시키는 방법을 제안한다. 이를 통하여 무제한 정보 추출 방법과 명확화(Disambiguation) 기술이 지식 베이스를 확장시키는데 어떻게 사용될 수 있고, 어떠한 요소가 전체 시스템의 주된 성능 저하를 일으키며 개선되어야 하는지 알아보도록 한다.

• Molecular & Cellular Toxicology
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• v.1 no.4
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• pp.281-283
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• 2005

Extraction of biologically meaningful data and their validation are very important for toxicogenomics study because it deals with huge amount of heterogeneous data. BINGO is an annotation mining tool for biological interpretation of gene groups. Several statistical modeling approaches using Gene Ontology (GO) have been employed in many programs for that purpose. The statistical methodologies are useful in investigating the most significant GO attributes in a gene group, but the coherence of the resultant GO attributes over the entire group is rarely assessed. BINGO complements the statistical methods with graph-theoretic measures using the GO directed acyclic graph (DAG) structure. In addition, BINGO visualizes the consistency of a gene group more intuitively with a group-based GO subgraph. The input group can be any interesting list of genes or gene products regardless of its generation process if the group is built under a functional congruency hypothesis such as gene clusters from DNA microarray analysis.

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

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.295-302
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• 2007

It is very iterative and complicated work to enter the personal information every time one fills the form-based resume or one joins the new membership page on the internet. Although there are some systems that have the personal information on the computer and fill the membership page automatically, their accuracies are not often satisfactory in that the fields and their values do not match exactly. The research proposes and implements a system that has user's information on the computer and reasons and fills the information automatically that a membership web page(target page) requests using the personal information ontology. During the reasoning process, the target page is analyzed to extract the requested fields. Then the requested field names are converted to the standard field names using synonym ontology. The converted requested fields find the appropriate level in the personal information ontology using ontology match making to generate the final field value. The system not only finds the similar fields but also generates the exact field values by reasoning on the information ontology hierarchy. By experimenting with several membership pages on the web, the system showed higher accuracy over the existing systems. The system can be easily applicable to the cases where one iteratively fills the same information such as resume form.

• Journal of KIISE:Software and Applications
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• v.36 no.1
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• pp.70-81
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• 2009

Ontology is an explicit specification of concepts and relationships between concepts in an interest domain. As considered as one of typical knowledge representation methods, ontology is applied to various studies such as information extraction, information integration, information sharing, or knowledge management. In IT based industries, ontology is applied to research on information integration and sharing in order to enhance interoperability between enterprises. In supply chains or logistics, several enterprises participate as business partners to plan movements of goods, and control goods and logistics flows. A number of researches on information integration and sharing for the effective and efficient management of logistics or supply chains have been addressed. In this paper, we address an ontology as a knowledge-base for semantic-based integration of logistics information distributed in the logistics flow. Especially, we focus on developing an ontology that enables to represent and translate semantic meaning of EPC data in the EPC Network applied logistics. We present a scenario for tracing products in logistics in order to show the value of our ontology.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.652-654
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• 2005

정보 추출은 텍스트로 되어 있는 비 정형화된 데이터로부터 정형화된 정보를 추출하는 분야이다. 기존의 정보 추출이 구문 중심의 방법인데 비해 본 논문에서는 시맨틱 웹과 온톨로지를 이용한 의미 기반의 정보 추출을 시도한다. 또한 본 논문에서는 기존의 정보 추출 모델을 분류해 보고 반자동 정보 추출이라는 새로운 모델을 제시한다. 이 모델에 기반하여 개인 정보를 자동으로 정형화 시켜주는 정보 추출 도구를 개발하고 이를 소개한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.289-290
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• 2009

시맨틱 웹의 발전과 더불어 소셜 네트워크 자동 구축에 대한 연구가 활발히 진행되고 있다. 본 논문은 온톨로지를 기반의 소셜 정보 추출에 대한 방법을 다루고 있으며, 특히, 이에 필요한 온톨로지 모델링, 사람들 사이의 관계 추출을 위한 패턴 정의에 대해 기술하고 있다. 온톨로지와 패턴을 기반으로 역사적 인물들의 친족관계를 파악함으로써 소설 정보의 추출에 대한 가능성을 미리 짐작해 본다.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.113-125
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• 2011

As information resources have become more various and the number of the resources has increased, knowledge customization on the social web has been becoming more difficult. To reduce the burden, we offer a framework for context-based similarity calculation for knowledge customization using ontology on the CBR. Thereby, we newly developed context- and intensity-based similarity calculation methods which are applied to extraction of the most similar case considered semantic similarity and syntactic, and effective creation of the user-tailored knowledge using the selected case. The process is comprised of conversion of unstructured web information into cases, extraction of an appropriate case according to the user requirements, and customization of the knowledge using the selected case. In the experimental section, the effectiveness of the developed similarity methods are compared with other edge-counting similarity methods using two classes which are compared with each other. It shows that our framework leads higher similarity values for conceptually close classes compared with other methods.

• Journal of Information Science Theory and Practice
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• v.6 no.4
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• pp.28-38
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• 2018

Wikipedia is composed of millions of articles, each of which explains a particular entity with various languages in the real world. Since the articles are contributed and edited by a large population of diverse experts with no specific authority, Wikipedia can be seen as a naturally occurring body of human knowledge. In this paper, we propose a method to automatically identify key entities and relations in Wikipedia articles, which can be used for automatic ontology construction. Compared to previous approaches to entity and relation extraction and/or identification from text, our goal is to capture naturally occurring entities and relations from Wikipedia while minimizing artificiality often introduced at the stages of constructing training and testing data. The titles of the articles and anchored phrases in their text are regarded as entities, and their types are automatically classified with minimal training. We attempt to automatically detect and identify possible relations among the entities based on clustering without training data, as opposed to the relation extraction approach that focuses on improvement of accuracy in selecting one of the several target relations for a given pair of entities. While the relation extraction approach with supervised learning requires a significant amount of annotation efforts for a predefined set of relations, our approach attempts to discover relations as they occur naturally. Unlike other unsupervised relation identification work where evaluation of automatically identified relations is done with the correct relations determined a priori by human judges, we attempted to evaluate appropriateness of the naturally occurring clusters of relations involving person-artifact and person-organization entities and their relation names.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.297-306
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• 2007

지능정보시스템 구축에 있어서 자동화가 어려운 단계중의 하나인 규칙 습득을 위해 활용되는 방법중의 하나가 제한된 언어집합 기법을 이용하는 것이다. 그러나 제한된 언어집합 기법을 이용해 규칙을 생성하기 위해서는 규칙을 구성하는 변수와 그 값들에 대한 정보가 사전에 정의되어 있어야 하는데, 유동성이 큰 웹 환경에서 예상 가능한 모든 변수와 그 값을 사전에 정의하는 것이 매우 어렵다. 이에 본 연구에서는 이러한 한계를 극복하기 위해 제한된 언어집합 기법과 온톨로지를 이용한 규칙 생성 방법론을 제시하였다. 이를 위해 지식의 습득 대상이 되는 특정 문장은 문법구조 분석기를 이용해 파싱을 수행하며, 파싱된 단어들을 이용해 규칙의 구성 요소인 변수와 그 값을 식별한다. 그러나 규칙을 내포한 자연어 문장의 불완전성으로 인해 변수가 명확하지 않거나 완전히 빠져 있는 경우가 흔히 발생하며, 이로 인해 온전한 형식의 규칙 생성이 어렵게 된다. 이 문제는 도메인 온톨로지의 생성을 통해 해결하였다. 이 온톨로지는 특정 도메인을 구성하고 있는 개념들간의 관계를 포함하고 있다는 점에서는 기존의 온톨로지와 유사하지만, 규칙을 완성하는 과정에서 사용된 개념들의 사용빈도를 기반으로 온톨로지의 구조를 변경하고, 결과적으로 더 정확한 규칙의 생성을 지원한다는 점에서 기존의 온톨로지와 차별화된다. 이상의 과정을 통해 식별된 규칙의 구성요소들은 제한된 언어집합 기법을 이용해 구체화된다. 본 연구에서 제안하는 방법론을 설명하기 위해 임의의 인터넷 쇼핑몰에서 수행되는 배송관련 웹 페이지를 선정하였다. 본 방법론은 XRML에서의 지식 습득 과정의 효율성 제고에 기여할 수 있을 것으로 기대된다.