• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.3
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• pp.721-726
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• 2008

This research suggests real-time people-powered Q&A system that overcoming limitation of natural language handling technology that Q&A system has and demerits that unrelated documents are included in the results of searching in existing information retrieval system and can adapt to change to Web2.0 environment by actively applying users' participation and providing real-time information to users' request of information.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.133-136
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• 2003

Due to recent explosion of information available online, question- answering (Q&A) systems are becoming a compelling framework for finding relevant information in a variety of domains. Question-answering system is one of the best ways to introduce a novice customer to a new domain without making him/her to obtain prior knowledge of its overall structure improving search request with specific answer. However, the current web poses serious problem for finding specific answer for many overlapped meanings for the same questions or duplicate questions also retrieved answer for many overlapped meanings fer the same questions or duplicate questions also retrieved answer is slow due to enhanced network traffic, which leads to wastage of resource. In order to avoid wrong answer which occur due to above-mentioned problem we propose the system using ontology by RDF, RDFS and mobile agent based on JAVA. We also choose wireless internet based embedded device as our test bed for the system and apply the system in E-commerce information domain. The mobile agent provides agent routing with reduced network traffic, consequently helps us to minimize the elapsed time for answers and structured ontology based on our proposed algorithms sorts out the similarity between current and past question by comparing properties of classes.

• Journal of KIISE
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• v.45 no.2
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• pp.134-140
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• 2018

A chat system is a computer program that understands user's miscellaneous utterances and generates appropriate responses. Sometimes a chat system needs to answer users' simple information-seeking questions. However, previous generative chat systems do not consider how to embed knowledge entities (i.e., subjects and objects in triple knowledge), essential elements for question-answering. The previous chat models have a disadvantage that they generate same responses although knowledge entities in users' utterances are changed. To alleviate this problem, we propose a knowledge entity embedding method for improving question-answering accuracies of a generative chat system. The proposed method uses a Siamese recurrent neural network for embedding knowledge entities and their synonyms. For experiments, we implemented a sequence-to-sequence model in which subjects and predicates are encoded and objects are decoded. The proposed embedding method showed 12.48% higher accuracies than the conventional embedding method based on a convolutional neural network.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.1005-1007
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• 2015

사물 인터넷(Internet of Things)의 확산으로 가까운 미래에는 사물 인터넷 환경에서 질의 응답 시스템이 활발하게 이용될 것으로 예상된다. 본 논문에서는 사물 인터넷 환경에 적합한 초소형, 저사양 하드웨어를 이용하여 커뮤니티 질의 응답 시스템(Community Question Answering System)을 구축하는 방안에 대해 살펴본다. 하드웨어는 700Mhz 싱글 코어 CPU와 512MB의 메인 메모리를 장착한 라즈베리 파이를 이용하였고, 질의 응답 시스템으로는 Apache Solr를 기본 시스템으로 활용하였다. 성능 분석 결과 실시간 응답성은 매우 훌륭하지만 정확도는 앞으로 보완이 필요한 것으로 분석되었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.89-92
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• 2016

The latest the siri and like this is offering services that recognize and respond to words in the smartphone or web services. In order to handle intelligently these voices, It needs to search big data in the cloud and requires the implementation of parsing context accuracy given. In this paper, I would like to propose the study on the intelligent personal assistant development method base on the Open source with ASR(Automatic Speech Recognition), QAS(Question Answering System) and TTS(Text To Speech).

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.73-79
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• 2024

The smart tourism chatbot service provide smart tourism services to users easily and conveniently along with the smart tourism app. In this paper, the tourism information QA (Question Answering) service is proposed based on the task-oriented smart tourism chatbot system [13]. The tourism information QA service is an MRC (Machine reading comprehension)-based QA system that finds answers in context and provides them to users. The tourism information QA system consists of NER (Named Entity Recognition), DST (Dialogue State Tracking), Neo4J graph DB, and QA servers. We propose tourism information QA service uses the tourism information NER model and DST model to identify the intent of the user's question and retrieves appropriate context for the answer from the Neo4J tourism knowledgebase. The QA model finds answers from the context and provides them to users through the smart tourism app. We develop the tourism information QA model by transfer learning the bigBird model, which can process the context of 4,096 tokens, using the tourism information QA dataset.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.140-143
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• 2006

In this paper, we describe a question-answering system which extracts answers for the superlative questions which include the superlative words such as "the most", "the best", "the first", "the largest", "the least", and so on. The superlative questions are composed of four main components and others. Four main components are the superlative word, answer type, regional information, and a verb modified by the superlative word. We classify the superlative words into two types as to whether the verb has to be needed to be a question or not. The superlative word, answer type and regional information are essential elements to extract answer for all superlative questions. But the verb may be an essential element by the type of superlative word. Our system analyzes input question, and finds four main components of the superlative question. Also, our system searches relative documents and candidate sentences using them, and extracts answers from candidate sentences. Empirical result shows that our system has high precision and high recall for the superlative questions.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.81-88
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• 2024

Despite the rapid growth of the generative AI market and significant interest from domestic companies and institutions, concerns about the provision of inaccurate information and potential information leaks have emerged as major factors hindering the adoption of generative AI. To address these issues, this paper designs and implements a question-answering system based on the Retrieval-Augmented Generation (RAG) architecture. The proposed method constructs a knowledge database using Korean sentence embeddings and retrieves information relevant to queries through optimized searches, which is then provided to the generative language model. Additionally, it allows users to directly manage the knowledge database to efficiently update changing business information, and it is designed to operate in a private network to reduce the risk of corporate confidential information leakage. This study aims to serve as a useful reference for domestic companies seeking to adopt and utilize generative AI.

• The Journal of Korean Association of Computer Education
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• v.6 no.4
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• pp.23-30
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• 2003

This paper proposes a passage retrieval system based on Okapi's BM25 for developing an efficient QA system and evaluates performances of the passage retrieval system. The test collection of TREC Q&A track which is composed of about one million documents was indexed and a hundred queries of TREC Q&A track are used as testing queries. The experimental results shows that the proposed passage retrieval system can reach to 100% recall rate by searching in only 1700 sentences while the conventional document retrieval system have to search about 120 thousands sentences which are about 70 times more than the proposed passage retrieval system.

• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.41-51
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• 2002

In this paper, I describe a knowledge-based question-answering system and significance of the system with a view to constructing a fact database. The knowledge-based system takes advantage of existing NLP-resources such as conceptual structures of ontologies along with morphotogical, syntactic and semantic analysis. The use of conceptual structures allows us to select right answers through inferences basically made by expansions of concepts. However, the work of constructing factual knowledge requires a great amount of acquisition time in large-scale applications because of the nature of human interference. This is why the procedure of acquiring factual knowledge cannot be fully automated. Apart from efficiency considerations. the knowledge-based system deserves serious consideration, I point out benefits of the system and describe the whole procedure of building the system in terms of a fact database.