• Proceedings of the Korea Information Processing Society Conference
• /
• 2000.04a
• /
• pp.94-99
• /
• 2000

컴퓨터의 보급과 함께 인터넷의 대중화로 많은 정보가 인터넷을 통해 제공되면서 많은 사람들이 정보통신 기반 서비스를 이용하게 되었다. 하지만 이런 서비스에 익숙하지 않은 사용자가 자신이 원하는 정보를 찾는 것은 그리 쉬운 일이 아니다. 그래서 ISP나 PC통신 업체들은 사용자들이 겪는 어려움을 해결해 주기 위한 서비스를 제공하고 있다. 그러나 사용자들의 엄청난 증가로 인해 이런 서비스를 유지하는데 많은 인력과 시간이 필요하게 되면서 질의 응답 자동화에 대한 필요성이 대두되었다. 본 논문에서는 ISP 업체 중 하나인 한메일넷의 자동 응답 시스템을 위한 FAQ 브라우징 시스템을 개발하였다. 기존의 많은 검색 서비스가 키워드들을 단순히 나열하고 이 키워드의 링크를 따라가면서 검색을 하게 하였으나 이 방식은 검색 대상에 대한 키워드 정보만을 제공하기 때문에, 문제에 대한 배경 지식이 적거나 검색 서비스 사용에 익숙치 않은 사용자가 이용하기에는 쉽지 않다. 본 시스템에서는 사용자에게 이차원상에 표현된 문서 지도를 제공해서 사용자가 쉽게 전체 검색 자료의 분포를 파악하고 검색하도록 하였다. 또한 단계별 검색이 가능하도록 해서 사용자가 효율적으로 검색할 수 있다.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.253-266
• /
• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• Journal of the Korean Society for information Management
• /
• v.25 no.4
• /
• pp.43-66
• /
• 2008

Currently be proceeded a lot of researchers for 'user information demand description' for interface of an information retrieval system or Web search engines, but user information demand description for a natural language form is a difficult situation. These reasons are as they cannot provide the semantic similarity that an information retrieval model can be completely satisfied with variety regarding an information demand expression and semantic relevance for user information description. Therefore, this study using the description logic that is a knowledge representation base of OWL and a vector model-based weight between concept, and to be able to satisfy variety regarding an information demand expression and semantic relevance proposes a decision way for perfect assistances of user information demand description. The experiment results by proposed method, semantic similarity of a polyseme and a synonym showed with excellent performance in decision.

• Journal of Korea Multimedia Society
• /
• v.3 no.5
• /
• pp.506-515
• /
• 2000

In this paper a web-based real-time education system, which is able to support education through multimedia, is suggested for the expansion of learner's creative ability in the school. This system is designed so that it can support three things: 1) a real time interaction between interaction between instructors and learners, 2) individual learning through such an interaction, and 3) a coercive distribution of display by instructions for preventing the deviation of learners from learning. Also, the system, which UML is applied to, makers efficient interaction possible through the module for the real-time exchange and management of messages even in the multi-user environment. Through this system, not only the simulation by learners can be made for experiments and practices, but also questions and respondence can be supported on the procedure of experiments and the analysis of their results. This system is bulit on constructivism, and aimed at helping the learning progress and knowledge formation of learners.

• Annual Conference on Human and Language Technology
• /
• 2002.10e
• /
• pp.92-97
• /
• 2002

의미기반 정보 검색, 자연어 질의 응답, 지식 자동 습득, 담화 처리 등 높은 수준의 자연언어처리 시스템에서 의미처리를 위한 대용량의 지식 베이스가 필요하다. 이러한 지식 베이스 중에서 가장 기본적인 것이 워드넷이다. 이러한 워드넷을 이용함으로써 여러 의미 사이의 의미 유사도를 구할 수 있고, 속성을 물려받을 수 있기 때문에 비슷한 속성을 가진 의미들을 한꺼번에 다루는 데 유용하다. 본 논문에서는 기본 어휘를 바탕으로 기존의 명사 워드넷과 단일어 사전을 이용하여 한국어 동사 워드넷을 구축하는 방법을 제시한다. 본 논문에서 1차 작업을 통하여 구축한 동사 워드넷에는 동사 1,757개에 대한 4,717개의 의미(중복을 포함하면 모두 5,235개의 의미)를 포함하고 있으며 특별히 의미가 많이 편중된 14개의 개념에 속한 571개의 의미를 53개의 세부 개념으로 재분류하여 최종적으로 모두 767개의 계층적 개념으로 구성된 동사 워드넷이 만들어 졌다.

• Journal of KIISE
• /
• v.44 no.8
• /
• pp.813-821
• /
• 2017

Entity Linking find the meaning of an entity mention, which indicate the entity using different expressions, in a user's query by linking the entity mention and the entity in the knowledge base. This task has four challenges, including the difficult knowledge base construction problem, multiple presentation of the entity mention, ambiguity of entity linking, and NIL entity recognition. In this paper, we first construct the entity name dictionary based on Wikipedia to build a knowledge base and solve the multiple presentation problem. We then propose various methods for NIL entity recognition and solve the ambiguity of entity linking by training the support vector machine based on several features, including the similarity of the context, semantic relevance, clue word score, named entity type similarity of the mansion, entity name matching score, and object popularity score. We sequentially use the proposed two methods based on the constructed knowledge base, to obtain the good performance in the entity linking. In the result of the experiment, our system achieved 83.66% and 90.81% F1 score, which is the performance of the NIL entity recognition to solve the ambiguity of the entity linking.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.161-177
• /
• 2019

In this paper, we study the performance improvement of the answer extraction in Question-Answering system by using sentence dependency parsing result. The Question-Answering (QA) system consists of query analysis, which is a method of analyzing the user's query, and answer extraction, which is a method to extract appropriate answers in the document. And various studies have been conducted on two methods. In order to improve the performance of answer extraction, it is necessary to accurately reflect the grammatical information of sentences. In Korean, because word order structure is free and omission of sentence components is frequent, dependency parsing is a good way to analyze Korean syntax. Therefore, in this study, we improved the performance of the answer extraction by adding the features generated by dependency parsing analysis to the inputs of the answer extraction model (Bidirectional LSTM-CRF). The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. In this study, we compared the performance of the answer extraction model when inputting basic word features generated without the dependency parsing and the performance of the model when inputting the addition of the Eojeol tag feature and dependency graph embedding feature. Since dependency parsing is performed on a basic unit of an Eojeol, which is a component of sentences separated by a space, the tag information of the Eojeol can be obtained as a result of the dependency parsing. The Eojeol tag feature means the tag information of the Eojeol. The process of generating the dependency graph embedding consists of the steps of generating the dependency graph from the dependency parsing result and learning the embedding of the graph. From the dependency parsing result, a graph is generated from the Eojeol to the node, the dependency between the Eojeol to the edge, and the Eojeol tag to the node label. In this process, an undirected graph is generated or a directed graph is generated according to whether or not the dependency relation direction is considered. To obtain the embedding of the graph, we used Graph2Vec, which is a method of finding the embedding of the graph by the subgraphs constituting a graph. We can specify the maximum path length between nodes in the process of finding subgraphs of a graph. If the maximum path length between nodes is 1, graph embedding is generated only by direct dependency between Eojeol, and graph embedding is generated including indirect dependencies as the maximum path length between nodes becomes larger. In the experiment, the maximum path length between nodes is adjusted differently from 1 to 3 depending on whether direction of dependency is considered or not, and the performance of answer extraction is measured. Experimental results show that both Eojeol tag feature and dependency graph embedding feature improve the performance of answer extraction. In particular, considering the direction of the dependency relation and extracting the dependency graph generated with the maximum path length of 1 in the subgraph extraction process in Graph2Vec as the input of the model, the highest answer extraction performance was shown. As a result of these experiments, we concluded that it is better to take into account the direction of dependence and to consider only the direct connection rather than the indirect dependence between the words. The significance of this study is as follows. First, we improved the performance of answer extraction by adding features using dependency parsing results, taking into account the characteristics of Korean, which is free of word order structure and omission of sentence components. Second, we generated feature of dependency parsing result by learning - based graph embedding method without defining the pattern of dependency between Eojeol. Future research directions are as follows. In this study, the features generated as a result of the dependency parsing are applied only to the answer extraction model in order to grasp the meaning. However, in the future, if the performance is confirmed by applying the features to various natural language processing models such as sentiment analysis or name entity recognition, the validity of the features can be verified more accurately.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.43-61
• /
• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.6
• /
• pp.1532-1541
• /
• 1999

In this paper, we built an Object Oriented Framework for Web collaboration system which contains high level analysis information and design knowledge for java applets and applications that enable web clients to communicate and collaborate each other. Components of framework contain design information, source codes and executable codes for reuse. We had defined a development method for domain framework in related works and built a web collaboration system framework following it. We defined subsystem of web collaboration system for the communication and collaboration between web clients. We also BBS, Q&A system, board service system for the communication and collaboration between web clients. We also developed visual tools for framework usability : source code generator, class editor, knowledge supporting tools.

• The Journal of Korean Association of Computer Education
• /
• v.5 no.3
• /
• pp.127-133
• /
• 2002

In this paper a web-based real-time education system, which is able to support education through multimedia, is suggested for the expansion of learner's creative ability in the school. This system is designed so that it can support three things: 1) a real time interaction between instructors and learners, 2) individual learning through such an interaction, and 3) a coercive distribution of display by instructions for preventing the deviation of learners from learning. Also, this system, which UML is applied to, makes efficient interaction possible through the module for the real-time exchange and management of messages even in the multi-user environment. Through this system, not only the simulation by learners can be made for experiments and practices, but also Questions and respondence can be supported on the procedure of experiments and the analysis of their results. This system is built on constructivism, and aimed at helping the learning progress and knowledge formation of learners.