• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.10
• /
• pp.3230-3255
• /
• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• Annual Conference on Human and Language Technology
• /
• 2004.10d
• /
• pp.275-282
• /
• 2004

본 논문은 3단계 정답 추출 방법을 통해 백과사전 인물분야 질의응답 시스템을 구현하는 방법을 제안한다. 논문에서 제안한 3단계 정답 추출 방법은 1) 백과사전 문서 내에서 정형화 될 수 있는 지식들을 추출한 백과사전 KB 기반 정답 추출 방법, 2) 문장을 언어분석 하여 LF(Logical Form)구조를 추출하여 색인한 LF 기반 정답추출 방법, 3) 각 문장을 주제 태깅을 하여, 주제별로 묶어 의미적 단락으로 구분하고 단락 검색을 기반으로 정답을 추정하는 의미적 단락 기반 정답 추출 방법으로 구성되어 있다. 이러한 방법론은 백과사전이라는 문서 도메인의 특성을 반영하고. 사용자 질문의 난이도 또는 형태에 따라서 정답을 제공할 수 있는 백과사전 인물분야 질의응답 시스템에 적합하다.

• Annual Conference on Human and Language Technology
• /
• 2007.10a
• /
• pp.140-144
• /
• 2007

시맨틱 웹 기술의 주축을 이루는 온톨로지의 구축시에 인스턴스를 생성하기 위하여 대상 문서를 구성하는 자연어 문장을 텍스트 마이닝 기술을 이용하여 트리플을 추출한다. 인스턴스를 생성할 때 보다 많은 정보를 추출하기 위해서 문장에 나타나는 상호참조 해결이 필요하다. 본 연구에서는 문서에서 많이 나타나는 명사구로 이루어진 대용어를 해석하기 위하여 언어 분석된 다양한 결과 정보를 이용한다. 본 연구에서는 계층적인 의미구조와 청킹을 이용한 규칙기반의 상호참조 해결 방법을 제안하고 실험을 통해 알고리즘의 정확도를 제시한다.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.209-213
• /
• 2021

Data mining is the application of specific algorithms for extracting patterns from data and KDD is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories or data streams. Data mining can be used for decision making in educational system. But educational institution does not use any knowledge discovery process approach on these data; this knowledge can be used to increase the quality of education. The problem was happening in the educational management system, but to make education system more flexible and discover knowledge from it huge data, we will use data mining techniques to solve problem.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.125-133
• /
• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.03a
• /
• pp.125-133
• /
• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• The KIPS Transactions:PartD
• /
• v.9D no.3
• /
• pp.365-380
• /
• 2002

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from large quantities of temporal data. Temporal knowledge, expressible in the form of rules, is knowledge with temporal semantics and relationships, such as cyclic pattern, calendric pattern, trends, etc. There are many examples of temporal data, including patient histories, purchaser histories, and web log that it can discover useful temporal knowledge from. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering temporal knowledge from temporal data, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treated data in database at best as data series in chronological order and did not consider temporal semantics and temporal relationships containing data. In order to solve this problem, we propose a theoretical framework for temporal data mining. This paper surveys the work to date and explores the issues involved in temporal data mining. We then define a model for temporal data mining and suggest SQL-like mining language with ability to express the task of temporal mining and show architecture of temporal mining system.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.884-888
• /
• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• Educational Technology International
• /
• v.23 no.1
• /
• pp.97-127
• /
• 2022

With the recent advancement of technology, there has been an increase in professional development activities, including teachers using blogs to share practical knowledge and reflect on teaching and learning. This study was conducted to identify the contents of practical knowledge shared through the K-12 teachers' blogs. To achieve the research objective, 70,571 blog posts were collected from 329 blogs of K-12 teachers in Korean and analyzed using text mining techniques. The results of the study are as follows. First, practical knowledge sharing activities using teacher blogs have increased. Teachers posted a lot of blogs during the semester. Second, primary school teachers share various curriculum activities, reflections on project classes, class management, opinions related to education, and personal. Third, secondary school teachers share summaries and reviews of curriculum, materials related to college entrance exams, various instructional materials, opinions related to education, and personal experiences on their blogs. This study suggested that blogs are widely used as a venue for sharing practical knowledge of teachers, and that blogs can be a useful way to develop professionalism.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2006.11a
• /
• pp.298-302
• /
• 2006

As the age of information technology is coming, companies stress the need of knowledge management. Companies construct ERP system including knowledge management. But, it is not easy to formalize knowledge in organization. we focused on data mining system by using genetic programming. But, we don't have enough data to perform the learning process of genetic programming. We have to reduce input parameter(s) or increase number of learning or training data. In order to do this, the enhanced data mining system by using GP combined with SOM(Self organizing map) is adopted in this paper. We can reduce the number of learning data by adopting SOM.