A knowledge-based question answering system for B2C eCommerce

Abstract

The evolution of Business-to-Consumer (B2C) eCommerce has been formed through various generations. Last models of B2C eCommerce are comparative shopping systems that connect to multiple vendors’ databases and collect the information requested by the user. The comparative result obtained is then displayed in a tabular format in the user’s browser. Although this scenario is much better than the multiple manual site comparisons, user still needs to face inconsistent user interfaces when he is linked from the comparison site to the actual purchasing site for shopping. Therefore, user has to learn logics of each site’s user interface. In this paper, we propose a question answering system based on natural language processing techniques for retail (B2C) in eCommerce. This system gets a question in natural language formats, decomposes it to keywords, and extracts constraints automatically. Corresponding answers are then retrieved from the vendors’ Web sites by exploiting the question constraints.

Introduction

The eCommerce began with the introduction of EDI between companies, and ATMs for banking [1], [2]. Introduction of the Web Browsers opened up a new age by combining open internet and easy user interface approaches [1], [2].

B2C ordinarily refer to online trading and auctions, for example, online stock trading markets, online auction for computers and other goods. B2C eCommerce refers to the emerging commerce model where businesses/companies and consumers interact electronically or digitally in some way. One of the best examples of B2C eCommerce is Amazon.com, an online bookstore that launched its site in 1995. In a B2C eCommerce the focus is more about enticing prospects and converting them into customers, retaining them and share value created during the process. The ultimate goal is the conversion of shoppers into buyers as aggressively and consistently as possible.

In a typical B2C flow of information between business and consumer typically is through the medium of Internet. This flow includes product orders/service requests from customers and product information, specifications, providing of services by Business.

B2C eCommerce is the predominant commercial experience of Web users. A typical scenario involves a user’s visiting one or several online shops, browsing their offers, selecting and ordering products. Ideally, a user would collect information about price, terms, and conditions (such as availability) of all or at least all major, online shops and then proceed to select the best offer. But manual browsing is too time-consuming to be conducted on this scale. Typically a user will visit one or a very few online stores before making a decision.

However, the evolution of B2C eCommerce has been formed through various generations. Last models of B2C eCommerce are comparative shopping catalogs. Models such as pricescan.com [3] that visit several shops, extract product and price information, and compile a market overview. The comparative result obtained is then displayed in a tabular format in the user’s browser. This approach suffers from several drawbacks. First, it’s necessary for these models to get access grant from vendors before to access their databases for retrieving any information. Since some vendors may not give access grant to their databases, their product information will not appear in the information provided by these models. We have proposed a knowledge-based approach to resolve this problem in [15]. In this approach, products and price information are understood and extracted from Web pages of vendors’ sites to build virtual catalog directly. Second, user still needs to face inconsistent user interfaces when he is linked from the comparison site to the actual purchasing site for shopping. Therefore, user has to learn logics of each site’s user interface. For example, user has to analyze his question into some keywords based on logics of user interface and give them to the system. It means that there is not such a possibility that user can ask his question in form of natural language (such as English) and get his answer. We can say that using keywords based on logics of third generation system’s user interfaces is not a good way to establish relationship between user and system [4]. Because at first a user is not interested to extract keywords of his question or maybe he is unable to do so. On the other hand, usually a few keywords cannot cover the complete meaning of user’s question. In most cases, users are searching clear responses for their questions, while the outputs of third generation systems are collection of answers related to user’s question that probably they contain the correct answer.

In recent years, Question Answering (QA) systems have evolved out of the field of Information Retrieval to meet better the needs of information seekers. Unlike simple keyword-based information retrieval systems, they aim to communicate directly with users through a natural language. They accept natural language questions and return exact answers eliminating the burden of query formulation and reading lots of irrelevant documents to attain the answer. Open-domain QA systems deal with unrestricted questions upon large-scale text corpora typically by means of statistical approaches whereas restricted-domain systems endeavor to concentrate on a controlled domain of interest (e.g. weather forecast or UNIX technical manuals). MELISA [5] is a good example for restricted-domain QA systems.

In this paper, we propose a QA system for B2C eCommerce. Now, this system can answer all questions in domain of digital camera while it can be developed for any retail domains. This system exploits an initial knowledge base which makes some advantages in contrast with Open-domain QA systems (i.e. systems do not have any specific domain knowledge [6], [7], [8]).

We present the details of our approach in the remainder of the paper as follows. After a short overview of the related work in Section 2, Section 3 describes the system architecture. Section 4 explains how we define an initial knowledge’s concepts, relations, and instances. In Section 5, we describe our approach to analyze the NL questions. In Section 6, we report the experiments we conducted involving digital camera advertisements on the Web. Finally, Section 7 presents the conclusion of this work.