• 지능정보연구
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• 제9권2호
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.2903-2923
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• 2023

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.3216-3230
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• 2015

A hot-issued research topic in the workflow intelligence arena is the emerging topic of "workflow-supported organizational social networks." These specialized social networks have been proposed to primarily represent the process-driven work-sharing and work-collaborating relationships among the workflow-performers fulfilling a series of workflow-related operations in a workflow-supported organization. We can discover those organizational social networks, and visualize its analysis results as organizational knowledge. In this paper, we are particularly interested in how to visualize the degrees of closeness centralities among workflow-performers by proposing a graphical representation schema based on the Graph Markup Language, which is named to ccWSSN-GraphML. Additionally, we expatiate on the functional expansion of the closeness centralization formulas so as for the visualization framework to handle a group of workflow procedures (or a workflow package) with organizational workflow-performers.

• 대한지리학회지
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• 제40권1호
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• pp.78-95
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• 2005

호남 장수지역 경제활동의 산업연계와 혁신네트워크에 대해 분석함으로써 지식정보화 시대의 이상적인 고령사회에 대비한 새로운 지역발전 방향을 모색하고자 하였다. 이를 위해 한국 장수지역 중의 하나인 전라북도의 순창군과 전라남도의 담양군, 곡성군, 구례군 등의 대표적 제조업체를 중심으로 심층 인터뷰를 실시하였다. 연구 결과, 산업 연계에 있어 원료구입과 노동력이용은 해당 군에서 많이 이루어지고 있고, 제조업과 농업 및 관광업간 연계와 인터넷을 이용한 통신판매가 활성화되고 있었다. 그리고 혁신네트워크에 있어서는 지식정보화시대에 가상혁신클러스터의 형성 가능성을 확인할 수 있었다. 따라서 농촌지역 발전을 위해서는 지식기반경제의 지역혁신체계 개념을 이용해 지역의 자원 및 전통지식과 밀접한 혁신네트워크 구축이 필요하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• 한국정보통신학회논문지
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• 제12권7호
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• pp.1308-1313
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• 2008

무선 애드혹 네트워크에서 동작하는 많은 프로토콜들은 이웃 노드에 대한 정보를 중요하게 사용한다. 이웃 노드에 대한 정보는 Hello 메시지를 주고받음으로써 알 수 있다. 자신기 존재를 알리고 싶은 노드는 주기적으로 Hello 패킷을 전송한다. 하지만, 이러한 Hello 패킷의 전송은 무선 애드혹 네트워크에 큰 제어 부하로 작용하게 된다. 이 논문에서는 Hello 패킷뿐만 아니라 방송 패킷을 활용하여 이웃 노드 정보를 주고받는 방법을 고찰한다. 분석과 컴퓨터 시뮬레이션을 통하여 방송 패킷을 활용하는 기법이 Hello 패킷만을 사용하는 방식에 비하여 상당한 효과를 보인다는 것을 분석한다. Hello 패킷의 전송 주기와 방송 패킷의 전송기가 같다면 42% 정도의 부하 절감 효과가 있다.

• International Journal of Knowledge Content Development & Technology
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• 제9권2호
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• pp.33-44
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• 2019

This paper discussed the factors that necessitated information networking, types of networks, benefits of information networking, library information networking and the University of Port 0Harcourt library network initiatives. Information networking is a process of communication, exchange of ideas, resource sharing and collaboration between individuals, organizations, institutions and libraries and it is facilitated by ICTs and the internet for improved accessibility. It has been brought about by information explosion, rapid advancement in information communication technologies, inadequate funding and increased demand for quality information. Networks can be classified into local, national, regional, and international networks and are formed to serve different categories of user communities. Benefits of information networking include resource sharing, on-line conferences and participation in programmes at distant centers, collaboration among scholars in different countries. Communication flow through the internet, social media, and electronic mail. Library information networking started with the interlibrary loan which has metamorphosed into library consortia in which groups of libraries partner to coordinate activities, share resources and combine expertise. The University of Port Harcourt Library network initiatives started with an e-granary (a CD ROM) and the establishment of a local area network. The library subscribes to more than 10 electronic databases. Information networking has greatly improved the sharing of resources in acquisition and dissemination of information resources since no single institution can acquire the overwhelming number of information resources in their various formats.

• ETRI Journal
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• 제38권6호
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• pp.1229-1239
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• 2016

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3230-3255
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• 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.

• 지식경영연구
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• 제19권3호
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• pp.47-61
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• 2018

A knowledge map, a taxonomy of knowledge repositories, must have capabilities supporting and enhancing knowledge user's activity to search and select proper knowledge for problem-solving. Conventional knowledge maps, however, have been hierarchically categorized, and could not support such activity that must coincide with the user's cognitive process for knowledge utilization. This paper, therefore, aims to verify and develop a methodology to build a networked knowledge map that can support user's activity to search and retrieve proper knowledge based on the referential navigation between content-relevant knowledge. This paper deploys keywords as the semantic information between knowledge, because they can represent the overall contents of a given document, and because they can play the role of semantic information on the link between related documents. By aggregating links between documents, a document network can be formulated: a keyword-based networked knowledge map can be finally built. Domain expert-based validation test was also conducted on a networked knowledge map of 50 research papers, which confirmed the performance of the proposed methodology to be outstanding with respect to the precision and recall.