Donghee Yoo

A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis

Many online shopping malls in which explicit rating information is not available still have difficulty in providing recommendation services using collaborative filtering (CF) techniques for their users. Applying temporal purchase patterns derived from sequential pattern analysis (SPA) for recommendation services also often makes users unhappy with the inaccurate and biased results obtained by not considering individual preferences. The objective of this research is twofold. One is to derive implicit ratings so that CF can be applied to online transaction data even when no explicit rating information is available, and the other is to integrate CF and SPA for improving recommendation quality. Based on the results of several experiments that we conducted to compare the performance between ours and others, we contend that implicit rating can successfully replace explicit rating in CF and that the hybrid approach of CF and SPA is better than the individual ones.

Hybrid query processing for personalized information retrieval on the Semantic Web

This paper suggests a hybrid query processing method for the effective retrieval of personalized information on the Semantic Web. When individual requirements change, the current method of query processing requires additional reasoning for knowledge to support personalization. To minimize this problem, the hybrid query processing method uses both the query rewriting method and the reasoning method. This paper distinguishes knowledge that is frequently changed from knowledge that is not. The query rewriting method is used for frequently changed knowledge; otherwise the reasoning approach is used. The query rewriting method refers to individual requirements to extend user queries instead of conducting inference. To illustrate the advantage of this method, a Personalized Hotel Search System (PerHSS) was implemented, consisting of hotel domain ontology, question-based and answer-based requirements collector, and a personalized hotel search interface using available Semantic Web technologies. This paper reports the results of the performance of a set of query tests and compares the results to those of similar works. The results show that the suggested method is suitable for the efficient retrieval of personal information.

Ontology-based economics knowledge sharing system

The objective of this paper is to argue the need for economics knowledge sharing and to demonstrate that it can be achieved with Semantic Web technologies. To this end, we first designed an economics knowledge sharing ontology (EKSO) to describe economic domain knowledge. We then implemented an ontology-based economics knowledge sharing system (OEKSS) based on the EKSO and Semantic Web technologies. The OEKSS included three search functions - basic search, knowledge navigation, and instrumental variable recommendation - to demonstrate how we can use shared economics knowledge in future research. In particular, an instrumental variable recommendation is made based on an instrumental variable recommendation algorithm (IVRA), which is a systematic and efficient way to find instrumental variables through EKSO in limited experimental environments. Finally, the paper presents a case study for IVRA that illustrates the usefulness and significance of the algorithm.

Building and evaluating a collaboratively built structured folksonomy

Flat folksonomy uses simple tags and has emerged as a powerful instrument for classifying and sharing a huge amount of knowledge on Web 2.0. However, it has semantic problems, such as ambiguous and misunderstood tags. To alleviate such problems, researchers have built structured folksonomies with a hierarchical structure or relationships among tags. Structured folksonomies, however, also have some fundamental problems, such as limited tagging of pre-defined vocabulary and time-consuming manual effort required to select tags. To resolve these problems, we suggested a new method of attaching a tag with its category, which we call a categorized tag (CT), to web content. CTs entered by users are automatically and immediately integrated into a collaboratively built structured folksonomy (CSF), reflecting the tag-and-category relationships supported by the majority of users. Then, we developed a CT-based knowledge organization system (CTKOS), which builds upon the CSF to classify organizational knowledge and enables us to locate appropriate knowledge. In addition, the results of the evaluation, which we conducted to compare our proposed system with the flat folksonomy system, indicate that users perceive CTKOS to be more useful than the flat folksonomy system in terms of knowledge sharing (i.e. the tagging mechanism) and retrieval (i.e. the searching mechanism).

User-Categorized Tags to Build a Structured Folksonomy

With the coming of Web 2.0, folksonomy has emerged to help users share web-based information created by users. The basic components of folksonomy are user-inputted tags, but a major problem is that the semantics of tags are not obvious because there is no hierarchy and no relationships among the tags. To minimize these problems, this paper suggests a user-categorized tag that freely defines the category of the tag when the user inputs it. Based on the user-categorized tags, a structured folksonomy is automatically created. This paper develops a prototype as web-based document management system to describe how a structured folksonomy can be useful.

Development of Construction Material Naming Ontology for Automated Building Energy Analysis

BIM Data exchange using standard format can provide a user friendly and practical way of integrating the BIM tools in the life cycle of a building on the currently construction industry which is participated various stakeholder. It used IFC format to exchange the BIM data from Design software to energy analysis software. However, since we can not use the material name data in the library of an energy analysis directly, it is necessary to input the material property data for building energy analysis. In this paper, to matching the material named of name of DOE-2 default library, rhe extracted material names from BIM file are inferred by the ontology With this we can make the reliable input data of the engine by development a standard data and also increase the efficient of building energy analysis process. The methodology can enable to provide a direction of BIM-based information management system as a conceptual study of using ontology in the construction industry.

Recommending valuable ideas in an open innovation community: A text mining approach to information overload problem

Purpose Open innovation communities are a growing trend across diverse industries because they provide opportunities of collaborating with customers and exploiting their knowledge effectively. Although open innovation communities can be strategic assets that can help firms innovate, firms nonetheless face the challenge of information overload incurred due to the characteristic of the community. The purpose of this paper is to mitigate the problem of information overload in an open innovation environment. Design/methodology/approach This study chose MyStarbucksIdea.com (MSI) as a target open innovation community in which customers share their ideas. The authors analyzed a large data set collected from MSI utilizing text mining techniques including TF-IDF and sentiment analysis, while considering both term and non-term features of the data set. Those features were used to develop classification models to calculate the adoption probability of each idea. Findings The results showed that term and non-term features play important roles in predicting the adoptability of ideas and the best classification accuracy was achieved by the hybrid classification models. In most cases, the precisions of classification models decreased as the number of recommendations increased, while the models’ recalls and F1s increased. Originality/value This research dealt with the problem of information overload in an open innovation context. A large amount of customer opinions from an innovation community were examined and a recommendation system to mitigate the problem was proposed. Using the proposed system, the firm can get recommendations for ideas that could be valuable for its business innovation in the idea generation phase, thereby resolving the information overload and enhancing the effectiveness of open innovation.

Construction and applicability of military ontology for semantic data processing

Recently, ontology has been utilized as a crucial element in knowledge management and knowledge representation. In the military area, the importance of ontology is also increasing. If military ontology is provided, machines will be able to understand and automatically manage information from various military information systems. Hence, how to construct and apply military ontology has become a significant research challenge. In this paper, we present the construction of the military ontology based on the Mixed Ontology Building Methodology (MOBM) and describe four application services using the military ontology for semantic data processing in the Army Tactical Command Information System (ATCIS). From these services, we show that military ontology can be used for constructing intelligent military information systems.

Assignment of collaborators to multiple business problems using genetic algorithm

As firms encounter new problems in the fast-changing business environment, they have to find collaborators with problem-solving expertise. Since this optimization problem takes place in a firm as the business environment changes, genetic algorithm (GA), which has shown outstanding performance in obtaining a sub-optimal solution relatively quickly, seems to be the right solution, one that is superior to goal-programming, multi-attribute decision making, and branch and bound. We therefore propose a GA-based approach to solving the problem of assigning collaborators to multiple business problems. Our solution worked well in several experiments.

A Mobile Semantic Integrated Search System of National Defense Research Information

To effectively manage research information in the field of national defense, metadata about the information should be managed systematically, and an integrated system to help convergence and management of the information should be implemented based on the metadata. In addition, the system should provide the users with effective integrated search services in a mobile environment, because searching via the use of mobile devices is increasing. The objective of this paper is to suggest a MSISS (Mobile Semantic Integrated Search System), which satisfies the requirements for effective management of the national defense research information. Specifically, we defined national defense research ontologies and national defense research rules after analyzing the Dublin Core metadata and database information of the major military research institutions. We implemented a prototype system for MSISS to demonstrate the use of the ontologies and rules for semantic integrated searching of the military research information. We also presented a triple-based search service to support semantic integrated search in a mobile environment and suggested future mobile semantic integrated search services.