Query suggestions (QS) tailored specifically for children are slowly gaining research attention in response to the growth in Internet use by children. Even though QS offered by popular search engines adequately meet the information needs of the general public, they do not achieve equivalent effectiveness from a child's perspective. This is because children's search behaviors, interests, cognitive levels, and ability to read and understand complex content are different from adults. Given the ubiquitous nature of the Web, its importance in today's society, and its increasing use in education, it is an urgent need to help children search the Web effectively. In this paper, we present a QS module, denoted CQS, which assists children in finding appropriate query keywords to capture their information needs by (i) analyzing content written for/by children, (ii) examining phrases and other metadata extracted from reputable (children's) websites, and (iii) using a supervised learning approach to rank suggestions that are appealing to children. CQS offers suggestions with vocabulary that can be comprehended by children and with topics of interest to them. Empirical studies conducted using keyword queries initiated by children, in addition to feedback gathered through crowdsourcing, have verified not only the effectiveness of CQS, but also the fact that children favor CQS-generated suggestions over the suggestions provided by Google, Yahoo!, and Bing.

Commercial web search engines include a querysuggestion module so that given a user's keyword query, alternativesuggestions are offered and served as a guide to assistthe user in formulating queries which capture his/her intendedinformation need in a quick and simple manner. Majorityof these modules, however, perform an in-depth analysis oflarge query logs and thus (i) their suggestions are mostlybased on queries frequently posted by users and (ii) theirdesign methodologies cannot be applied to make suggestions oncustomized search applications for enterprises for which theirrespective query logs are not large enough or non-existent. To address these design issues, we have developed PQS, aprobabilistic query suggestion module. Unlike its counterparts, PQS is not constrained by the existence of query logs, sinceit solely relies on the availability of user-generated contentfreely accessible online, such as the Wikipedia.org documentcollection, and applies simple, yet effective, probabilistic-andinformation retrieval-based models, i.e., the Multinomial, BigramLanguage, and Vector Space Models, to provide usefuland diverse query suggestions. Empirical studies conductedusing a set of test queries and the feedbacks provided byMechanical Turk appraisers have verified that PQS makesmore useful suggestions than Yahoo! and is almost as goodas Google and Bing based on the relatively small difference inperformance measures achieved by Google and Bing over PQS.

Question-Answering (QA) service is a growing area of research study, and commercial QA systems have recently been developed. We are motivated to provide complementary QA service that answers questions in advertisements (ads). These days with almost all businesses online, potential buyers who search for merchandises to purchase through the Internet are also flourishing. When a Web user looks for products online, he may have many questions on his mind for which he would be eager to receive answers prior to finalizing his purchasing decision. Although some ads Web sites are complemented with FAQs, their QA services either are non-existent or do not provide answers to inquires in real time automatically. We address these problems by answering user's questions such as "Which is the cheapest car?", "Are there any entry-level, software developer positions?", etc., spontaneously in real time. Existing general-purpose QA systems, such as Ask.com, provide answers to a user's question Q in a list format. A more sophisticated approach is to order the answers to Q according to their degrees of relevance to Q. We propose a QA system which deals with the challenge of interpreting users' questions and retrieves correct, as well as partially-matched ranked, answers. Experimental results have verified that the proposed QA system is highly accurate in answering users' questions on car ads.