BuyerRelations

Buyer-Centric Product Filtering with GoodRelations/eClass and RuleML

Abstract

In this work, we instantiate Rule Responder to a buyer-centric virtual organization, BuyerRelations, which responds to offers from sellers. BuyerRelations makes use of a 'Computer Games' subset of the GoodRelations/eClass vocabulary,which is a generic Semantic Web ontology about products and services on the Web.

Keywords: GoodRelations vocabulary/eClass, RuleML, BuyerRelations, Product filtering


Introduction 

In today’s hyper communicative and technology dominated world it does not matter what business people are doing, although every kind of business needs some creative ways to spread the word about their business. So we are making this project to overcome this scenario by using product filter to advertise their products and services to the people who really need it (buyers).

BuyerRelations screens the offers from sellers via Rule Responder's Organizational Agent, which dispatches legitimate offer queries to the Personal Agent that assists the group of buyers with the highest potential interest. The group division of buyers follows the top-level of the 'Computer Games' taxonomy. The Personal Agents use myOffer rules -- referring to the remaining levels of the 'Computer Games' taxonomy -- to filter offers through buyers' requirements based on game quantities and attributes such as prices and discounts.

Objective

In our daily life, people often search for a particular type of product or service on the Web. Using standard search engines like Google or others can be a frustrating experience. There are a number of reasons.

First of all, if you want to search for a product, you have to know the exact meanings and very same words. For example, if you search for "CAD", you won't get the same page that use "computer aided design". Second, you limit your searching domain if you offer a certain search. Third, sometimes you want to search for a special requirement that most of the offers do not meet, or your search is based on a less common meaning of a very popular term , the current search is not very satisfied for these requirements.

To overcome the current web technology's defects, our project is for making up the current searching methods by integrating Rule Responder with GoodRelations. The project will be useful for sellers and manufacturers, because it makes sure the particular features and strengths of their products or services are considered by Semantic Web search engines, and it is good for buyers, because it allows them to find offers that exactly fit their requirements.


Methodology

 

Various steps in building BuyerRelations are as follows:

  1. BuyerRelations shall be an instantiation of Rule Responder for GoodRelations offer filtering that is built, tested, and illustrated in Office products and services.

  1. Offers and products shall be described, respectively, with the GoodRelations vocabulary[5] and the eClassOWLontology[6].

  1. Buyers in the domain shall be organized as a Rule Responder virtual organization(RVO).

  1. Offers from sellers shall be pre-filtered by BuyerRelations Organizational Agent (OA), which shall dispatch good candidate offers to the potentially most interested BuyerRelations Personal Agent (PA).

  1. The PA shall use its local RuleML rule base to decide whether the offer may be interesting for its human owner, perhaps with modifications, sending its decision back to OA and hence to seller.

Conclusion

In conclusion, BuyerRelations will act as a virtual organization to help sellers find buyers interested in their offers. The integration of rule responder with GoodRelations vocabulary/eClass makes the search of seller much more convenient. With this work, the features and strengths of  products are filtered from buyers perspective and enables sellers to search for appropriate buyers .

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