• Journal of Intelligence and Information Systems
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• v.9 no.2
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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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• v.17 no.11
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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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• v.9 no.8
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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.

• Journal of the Korean Geographical Society
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• v.40 no.1 s.106
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• pp.78-95
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• 2005

The purpose of this paper is to analyze industrial and innovation networks of long-live area of Honam Region and to suggest a policy direction for regional development of rural areas where have been neglected in the knowledge-based information society. Four counties (Sunchang, Damyang, Gokseong, and Gurye) in the Southwestern region of Korea are regarded as long-live belt of Korea. Production and innovation networks :Ire analyzed based on intensive surveys of firms in the belt. Major findings from the surveys are as follows. First, there are considerably strong local networks of production firms in terms of supply of input materials and labor. There are strong backward industrial linkages of the production firms with agricultural activities and considerable forward linkages with tourism industry. In addition, Internet is becoming a useful tool for sales of the new products. Second, the analysis of the innovation networks in the long-live area suggests the development of 'virtual innovation cluster' in the era of knowledge-based information society. The results imply that this innovation networks can be developed as a virtual innovation cluster in the rural areas, which can be the basis for the development of rural innovation systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1308-1313
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• 2008

Neighbor knowledge in wireless ad hoc networks provides important functionality for a number of protocols. The neighbor knowledge is acquired via Hello packets. Hello packets are periodically broadcasted by the nodes which want to advertise their existence. Usage of periodic Hello packet, however, is a big burden on the wireless ad hoc networks. This paper deals with an approach where each node acquires neighbor knowledge by observing not only Hello packets but also broadcasting packets. Analysis and computer simulation results show that the method using broadcast packets offers significant improvement over the method using Hello packet only. When the required hello packet transmission interval and the average broadcasting interval are equal, the overhead reduction is about 42%.

• International Journal of Knowledge Content Development & Technology
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• v.9 no.2
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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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• v.38 no.6
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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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• v.16 no.10
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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.

• Knowledge Management Research
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• v.19 no.3
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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.