• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.891-894
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• 2021

온톨로지(Ontology)는 사람과 컴퓨터, 또는 컴퓨터 간의 개념 및 개념 표현을 공유하기 위한 개념화의 명시적 규약을 의미한다. 기존의 온톨로지 생성은 전문가에 의한 수작업에 의존되어 비용과 시간이 많이 드는 한계가 있다. 이에 본 논문에서는 딥러닝(Deep learning)기반의 기계번역 개념을 적용한 사례를 활용하여, 수작업의 의존성이 감소한 방법으로 텍스트로부터 온톨로지를 생성하는 방법을 구현하였다. 특히 기존 연구에서 제안한, 딥러닝을 이용해 텍스트로부터 지식 표현 시퀀스를 추출한 정보를 활용하여, 지식 표현 구조를 온톨로지로 변환하고 지식 베이스로 확장하는 과정을 통해 자동화 된 Text-to-Ontology 변환 방법론을 제안하고자 한다.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.485-495
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• 2009

We in this paper propose a social network based on ontology in Korea Institute of Oriental Medicine (KIOM). By using the social network, researchers can find collaborators and share research results with others. For this purpose, first, personal profiles, scholarships, careers, licenses, academic activities, research results, and personal connections for all of researchers in KIOM are collected. After relationship and hierarchy among ontology classes and attributes of classes are defined through analyzing the collected information, a social network ontology are constructed using FOAF and OWL. This ontology can be easily interconnected with other social network by FOAF and provide the reasoning based on OWL ontology.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.428-437
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• 2005

An ontology consists of a set and definition of concepts that represents the characteristics of a given domain and relationship between the elements. To reduce time-consuming and cost in building ontology, this paper proposes a semiautomatic method to build a domain ontology using the results of text analysis. To do this, we Propose a terminology processing method and use the extracted concepts and semantic relations between them to build ontology. An experiment domain is selected by the pharmacy field and the built ontology is applied to document retrieval. In order to represent usefulness for retrieving a document using the hierarchical relations in ontology, we compared a typical keyword based retrieval method with an ontology based retrieval method, which uses related information in an ontology for a related feedback. As a result, the latter shows the improvement of precision and recall by $4.97\%$ and $0.78\%$ respectively.

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

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

• Annual Conference on Human and Language Technology
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• 2003.10d
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• pp.173-176
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• 2003

본 논문은 언어학적 데이터로부터 자동으로 온톨로지를 생성하기 위한 모델을 제안하고 이를 구현한다. 모델 제안을 위해 온톨로지의 기본 구성 요소인 개념과 관계를 정의하고 이러한 개념과 관계 객체를 자동으로 추출하는 알고리즘을 제안한다. WordNet을 이용하여 개념을 자동으로 추출하고, 추출된 개념들간의 관계는 한국어의 구문적 특성을 이용하여 관계의 기본 형태를 정의하고 이를 기반으로 관계를 추출한다. 본 논문은 특허문서에서 전기통신기술문서를 대상으로 구현했으며, 제안된 알고리즘을 다른 영역으로 확장하여 이를 검증할 것이다.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.90-95
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• 2015

기계적 학습을 위해서는 일반적으로 많은 양의 수동 주석데이터(Manually Labeled Data)가 요구된다. 원격지도(Distant Supervision)는 현실적으로 부족한 주석데이터(Labeled Data)를 대신해 자동적으로 주석데이터를 수집하여 학습하는 접근 방식으로 관계 추출(Relation Extracion) 문제에 널리 활용되고 있다. 이때 필연적으로 많은 노이즈(Noise)가 발생되는데, 적합성 검증(Relevance Verification)을 통해 수집된 학습데이터를 정제함으로써 노이즈로 인한 변동성을 줄이고 결과적으로 향상된 성능을 보여주는 관계 추출 방법을 제시한다.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.10-23
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• 2019

Although social big data can provide a multi-faceted perspective on school bullying experiences among children and adolescents, the complexity and variety of unstructured text presents a challenge for systematic collection and analysis of the data. Development of an ontology, which identifies key terms and their intricate relationships, is crucial for extracting key concepts and effectively collecting data. The current study elaborated on the definition of an ontology, carefully described the 7 stage development process, and applied the ontology for collecting and analyzing school bullying social big data. As a result, approximately 2,400 key terms were extracted in top-, middle-, and lower-level categories, concerning domains of participants, causes, types, location, region, and intervention. The study contributes to the literature by explaining the ontology development process and proposing a novel alternative research model that uses social big data in school bullying research. Findings from this ontology study may provide a basis for social big data research. Practical implications of this study lie in not only helping to understand the experience of school bullying participants, but also in offering a macro perspective on school bullying as a social phenomenon.

• 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.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.35-45
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• 2012

Researchers and scientists spend huge amount of time in analyzing the previous studies and their results. In order to timely take the advantageous position, they usually analyze various resources such as paper, patents, and Web documents on recent research issues to preoccupy newly emerging technologies. However, it is difficult to select invest-worthy research fields out of huge corpus by using the traditional information search based on keywords and bibliographic information. In this paper, we propose a method for efficient creation, storage, and utilization of semantically relevant information among technologies, products and research agents extracted from 'big data' by using text mining. In order to implement the proposed method, we designed an ontology that creates technological knowledge for semantic web environment based on the relationships extracted by text mining techniques. The ontology was utilized for InSciTe Adaptive, a R&D trends analysis and forecast service which supports the search for the relevant technological knowledge.