• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.317-326
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• 2010

To improve the diagnosis and prescription for military personnel, it is required to adopt Clinical Decision Support System (CDSS) in armed forces hospitals. The objective of this paper is to suggest a CDSS for armed forces hospitals using semantic web technologies. To this end, we designed military medical ontologies and military medical rules which consist of the various concepts and rules for supporting medical activities. We developed a semantic web-based CDSS to demonstrate the use of the ontologies and rules for treating military patients. We also showed the process of semantic search for the medical records which are created from the semantic web-based CDSS.

• Journal of Information Science Theory and Practice
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• v.7 no.4
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• pp.56-64
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• 2019

Planting intercropping in rubber plantations is another alternative for generating more income for farmers. However, farmers still lack the knowledge of choosing plants. In addition, information for decision making comes from many sources and is knowledge accumulated by the expert. Therefore, this research aims to create a decision support system for growing rubber trees for individual farmers. It aims to get the highest income and the lowest cost by using semantic web technology so that farmers can access knowledge at all times and reduce the risk of growing crops, and also support the decision supporting system (DSS) to be more intelligent. The integrated intercropping ontology and rule are a part of the decision-making process for selecting plants that is suitable for individual rubber plots. A list of suitable plants is important for decision variables in the allocation of planting areas for each type of plant for multiple purposes. This article presents designing and developing the intercropping ontology for DSS which defines a class based on the principle of intercropping in rubber plantations. It is grouped according to the characteristics and condition of the area of the farmer as a concept of the rubber plantation. It consists of the age of rubber tree, spacing between rows of rubber trees, and water sources for use in agriculture and soil group, including slope, drainage, depth of soil, etc. The use of ontology for recommended plants suitable for individual farmers makes a contribution to the knowledge management field. Besides being useful in DSS by offering options with accuracy, it also reduces the complexity of the problem by reducing decision variables and condition variables in the multi-objective optimization model of DSS.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.73-90
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• 2006

When a consumer intends to purchase products through Internet stores, many difficulties are met because of limitations of the current search engines and the current web structure, and lack of tools supporting decision-makings. This paper raises an Internet shopping problem and proposes a framework of decision making process to settle it with an intelligent agent based on Semantic Web and Finite Domain Constraint. The agent uses finite domain constraint programming as modeling and solution methods for the decision problem under the Semantic Web environment.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.97-117
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• 2012

Nowadays, Internet users tend to do a variety of actions at the same time such as web browsing, social networking and multimedia consumption. While watching a video, once a user is interested in any product, the user has to do information searches to get to know more about the product. With a conventional approach, user has to search it separately with search engines like Bing or Google, which might be inconvenient and time-consuming. For this reason, a video annotation platform has been developed in order to provide users more convenient and more interactive ways with video content. In the future of smart TV environment, users can follow annotated information, for example, a link to a vendor to buy the product of interest. It is even better to enable users to search for information by directly discussing with friends. Users can effectively get useful and relevant information about the product from friends who share common interests or might have experienced it before, which is more reliable than the results from search engines. Social networking services provide an appropriate environment for people to share products so that they can show new things to their friends and to share their personal experiences on any specific product. Meanwhile, they can also absorb the most relevant information about the product that they are interested in by either comments or discussion amongst friends. However, within a very huge graph of friends, determining the most appropriate persons to ask for information about a specific product has still a limitation within the existing conventional approach. Once users want to share or discuss a product, they simply share it to all friends as new feeds. This means a newly posted article is blindly spread to all friends without considering their background interests or knowledge. In this way, the number of responses back will be huge. Users cannot easily absorb the relevant and useful responses from friends, since they are from various fields of interest and knowledge. In order to overcome this limitation, we propose a method to filter a user's friends for information search, which leverages semantic video annotation and social networking services. Our method filters and brings out who can give user useful information about a specific product. By examining the existing Facebook information regarding users and their social graph, we construct a user profile of product interest. With user's permission and authentication, user's particular activities are enriched with the domain-specific ontology such as GoodRelations and BestBuy Data sources. Besides, we assume that the object in the video is already annotated using Linked Data. Thus, the detail information of the product that user would like to ask for more information is retrieved via product URI. Our system calculates the similarities among them in order to identify the most suitable friends for seeking information about the mentioned product. The system filters a user's friends according to their score which tells the order of whom can highly likely give the user useful information about a specific product of interest. We have conducted an experiment with a group of respondents in order to verify and evaluate our system. First, the user profile accuracy evaluation is conducted to demonstrate how much our system constructed user profile of product interest represents user's interest correctly. Then, the evaluation on filtering method is made by inspecting the ranked results with human judgment. The results show that our method works effectively and efficiently in filtering. Our system fulfills user needs by supporting user to select appropriate friends for seeking useful information about a specific product that user is curious about. As a result, it helps to influence and convince user in purchase decisions.