Electronic catalogs (or e-catalogs) hold information about the goods and services offered or requested by the participants, and consequently, form the basis of an e-commerce transaction. Catalog management is complicated by a number of factors and product classification is at the core of these issues. Classification hierarchy is used for spend analysis, custom3 regulation, and product identification. Classification is the foundation on which product databases are designed, and plays a central role in almost all aspects of management and use of product information. However, product classification has received little formal treatment in terms of underlying model, operations, and semantics. We believe that the lack of a logical model for classification Introduces a number of problems not only for the classification itself but also for the product database in general. It needs to meet diverse user views to support efficient and convenient use of product information. It needs to be changed and evolved very often without breaking consistency in the cases of introduction of new products, extinction of existing products, class reorganization, and class specialization. It also needs to be merged and mapped with other classification schemes without information loss when B2B transactions occur. For these requirements, a classification scheme should be so dynamic that it takes in them within right time and cost. The existing classification schemes widely used today such as UNSPSC and eClass, however, have a lot of limitations to meet these requirements for dynamic features of classification. In this paper, we try to understand what it means to classify products and present how best to represent classification schemes so as to capture the semantics behind the classifications and facilitate mappings between them. Product information implies a plenty of semantics such as class attributes like material, time, place, etc., and integrity constraints. In this paper, we analyze the dynamic features of product databases and the limitation of existing code based classification schemes. And describe the semantic classification model, which satisfies the requirements for dynamic features oi product databases. It provides a means to explicitly and formally express more semantics for product classes and organizes class relationships into a graph. We believe the model proposed in this paper satisfies the requirements and challenges that have been raised by previous works.
To measure variations in some of the important agronomic characteristics of rice varieties under shifting of seedling dates, this study has been carried out at the Paddy Crop Division of Crop Experiment Station(then Agricultural Experiment Station) in Suwon for the period of three years 1958 to 1960. The varieties used in this study were Kwansan, Suwon #82, Mojo, Paltal and Chokwang, which have the different agronomic characteristics such as earliness and plant type. Seeds of each variety were sown at 14 different dates in 10-day interval starting on March 2. The seedlings were grown on seed bed for 30, 40, 50, 60, 70 and 80 days, respectively. The results of this study are as follows: A. Heading dates. 1. As the seeding date was delayed, the heading dates was almost proportionally delayed. The degree of delay was higher in early varieties and lower in late varieties and the longer the seedling stage, the more delayed the heading date. 2. Number of days to heading was proportionally lessened as seeding was delayed in all the varieties but the magnitude varied depending upon variety. In other words, the required period for heading in case of late planting was much shortened in late variety compared with early one. Within a variety, the number of days to heading was less shortened as the seedling stage was prolonged. Early variety reached earlier than late variety to the marginal date for the maximum shortening of days to heading and the longer the seeding stage, the limitted date came earlier. There was a certain limit in seeding date for shortening of days to heading as seeding was delayed, and days to heading were rather prolonged due to cold weather when seeded later than that date. 3. In linear regression equation, Y=a+bx obtained from the seeding dates and the number of days to heading, the coefficient b(shortening rate of days to heading) was closely correlated with the average number of days to heading. That is, the period from seeding to heading was more shortened in late variety than early one as seeding was delayed. 4. To the extent that the seedling stage is not so long and there is a linear relationship between delay of seeding and shortening of days to heading, it might be possible to predict heading date of a rice variety to be sown any date by using the linear regression obtained from variation of heading dates under the various seeding dates of the same variety. 5. It was found out that there was a close correlation between the numbers of days to heading in ordinary culture and the other ones. When a rice variety was planted during the period from the late part of March to the middle of June and the seedling ages were within 30 to 50 days, it could be possible to estimate heading date of the variety under late or early culture with the related data of ordinary culture. B. Maturing date. 6. Within (he marginal date for maturation of rice variety, maturing date was proportionally delayed as heading was delayed. Of course, the degree of delay depended upon varieties and seedling ages. The average air temperature (Y) during the ripening period of rice variety was getting lower as the heading date. (X) was delayed. Though there was a difference among varieties, in general, a linear regression equation(y=25.53-0.182X) could be obtained as far as heading date were within August 1 to September 13. 7. Depending upon earliness of a rice variety, the average air temperature during the ripening period were greatly different. Early variety underwent under 28