Search results

Recent studies show that many Web users only submit short queries and conduct short search sessions. This paper examines aspects of users’ attempting longer more complex queries. Web search services such as Ask Jeeves – publicly accessible question and answer (Q&A) search engines – encourage queries in question or request format. In light of this trend, this study examines whether general Web queries are shifting towards a more question/request format. Previous studies show that some users were submitting question or request format queries to general non‐Q&A Web search engines. This paper re‐examines this issue by analysing large‐scale Web query data from two different (US and European) Web query data sets, including 1.2 million Excite queries (www.excite.com) and 1.2 million AlltheWeb.com (http://AlltheWeb.com) queries from 2001.

This study is an attempt to develop an automatic identification method for Arabic web queries and divide them into several query types using data mining. In addition, it seeks to evaluate the impact of the academic environment on using the internet.

The web log files were collected from one of the higher institute's servers over a one‐month period. A special program was designed and implemented to extract web search queries from these files and also to automatically classify Arabic queries into three query types (i.e. Navigational, Transactional, and Informational queries) based on predefined specifications for each type.

The results indicate that students are slowly and gradually using the internet for more relevant academic purposes. Tests showed that it is possible to automatically classify Arabic queries based on query terms, with 80.6 per cent to 80.2 per cent accuracy for the two phases of the test respectively. In their future strategies, Jordanian universities should apply methods to encourage university students to use the internet for academic purposes. Web search engines in general and Arabic search engines in particular may benefit from the proposed classification method in order to improve the effectiveness and relevancy of their results in accordance with users' needs.

Studying internet web logs has been the subject of many papers. However, the particular domain, and the specific focuses on this research are what can distinguish it from the others.

This study aims to examine the differences between web image and textual queries.

A large number of web queries from image and textual search engines were analysed and compared based on their factual characteristics, query types, and search interests.

Useful results include the findings that web users tend to input short queries when searching for visual or textual information; that image requests have more zero hits and higher specificity, and contain more refined queries; that web image requests are more focused than textual requests on some popular search interests, and that the variety of textual queries is greater than that of image requests.

This study provides results that may enhance one's understanding of web‐searching behaviour and the inherent implications for the improvement of current web image retrieval systems.

The purpose of this study is to investigate whether question and keyword‐format queries are more successfully processed by search engines encouraging answers to searching and keyword‐format querying, respectively. This study aims to investigate whether web user characteristics and choice of search engine affects the relevancy scores and precision of the results.

The results of two search engines, Google and AskJeeves, were compared for question and keyword‐format queries. It was observed that AskJeeves was slightly more successful in processing question‐format queries, but this finding was not statistically supported. However, Google provided results on keyword‐format queries and the entire set of queries, which were statistically superior to those of AskJeeves.

Analysis of variance (ANOVA) showed that the age of web user is not as affective on the relevancy score and precision of results as other factors. Interactions of the main factors were also affective on the relevancy scores and precision, meaning that the different combinations of various factors cause a synergy in terms of relevancy scores and precision.

This was a preliminary work on the effect of user characteristics on comprehension and evaluation of search query results. Future work includes expanding this study to include more web user characteristics, more levels of the web user characteristics, and inclusion of more search engines.

The findings of this study provide statistical proof for the relationship between the characteristics of web users, choice of search engine and the relevancy scores and precision of search results.

The Web is a communication and information technology that is often used for the distribution and retrieval of personal information. Many people and organizations mount Web sites containing large amounts of information on individuals, particularly about celebrities. However, limited studies have examined how people search for information on other people, using personal names, via Web search engines. Explores the nature of personal name searching on Web search engines. The specific research questions addressed in the study are: “Do personal names form a major part of queries to Web search engines?”; “What are the characteristics of personal name Web searching?”; and “How effective is personal name Web searching?”. Random samples of queries from two Web search engines were analyzed. The findings show that: personal name searching is a common but not a major part of Web searching with few people seeking information on celebrities via Web search engines; few personal name queries include double quotations or additional identifying terms; and name searches on Alta Vista included more advanced search features relative to those on AlltheWeb.com. Discusses the implications of the findings for Web searching and search engines, and further research.

This paper aims to introduce a prototype system called SAFQuery (Simple And Flexible Query interface). In many existing Web search interfaces, simple and advanced query processes are treated separately that cannot be issued interchangeably. In addition, after several rounds of queries for specific information need(s), it is possible that users might wish to re-examine the retrieval results corresponding to some previous queries or to slightly modify some of the specific queries issued before. However, it is often hard to remember what queries have been issued. These factors make the current Web search process not very simple or flexible.

In SAFQuery, the simple and advanced query strategies are integrated into a single interface, which can easily formulate query specifications when needed in the same interface. Moreover, query history information is provided that displays the past query specifications, which can help with the memory load.

The authors' experiments by user evaluation show that most users had a positive experience when using SAFQuery. Specifically, it is easy to use and can simplify the Web search task.

The proposed prototype system provides simple and flexible Web search strategies. Particularly, it allows users to easily issue simple and advanced queries based on one single query interface, interchangeably. In addition, users can easily input previously issued queries without spending time to recall what the queries are and/or to re-type previous queries.

The purpose of this paper is to introduce a summarization method to enhance the current web-search approaches by offering a summary of each clustered set of web-search results with contents addressing the same topic, which should allow the user to quickly identify the information covered in the clustered search results. Web search engines, such as Google, Bing and Yahoo!, rank the set of documents S retrieved in response to a user query and represent each document D in S using a title and a snippet, which serves as an abstract of D. Snippets, however, are not as useful as they are designed for, i.e. assisting its users to quickly identify results of interest. These snippets are inadequate in providing distinct information and capture the main contents of the corresponding documents. Moreover, when the intended information need specified in a search query is ambiguous, it is very difficult, if not impossible, for a search engine to identify precisely the set of documents that satisfy the user’s intended request without requiring additional information. Furthermore, a document title is not always a good indicator of the content of the corresponding document either.

The authors propose to develop a query-based summarizer, called Q_Sum, in solving the existing problems of Web search engines which use titles and abstracts in capturing the contents of retrieved documents. Q_Sum generates a concise/comprehensive summary for each cluster of documents retrieved in response to a user query, which saves the user’s time and effort in searching for specific information of interest by skipping the step to browse through the retrieved documents one by one.

Experimental results show that Q_Sum is effective and efficient in creating a high-quality summary for each cluster to enhance Web search.

The proposed query-based summarizer, Q_Sum, is unique based on its searching approach. Q_Sum is also a significant contribution to the Web search community, as it handles the ambiguous problem of a search query by creating summaries in response to different interpretations of the search which offer a “road map” to assist users to quickly identify information of interest.

Web search engines are frequently used by people to locate information on the Internet. However, not all queries have an informational goal. Instead of information, some people may be looking for specific web sites or may wish to conduct transactions with web services. This paper aims to focus on automatically classifying the different user intents behind web queries.

For the research reported in this paper, 130,000 web search engine queries are categorized as informational, navigational, or transactional using a k‐means clustering approach based on a variety of query traits.

The research findings show that more than 75 percent of web queries (clustered into eight classifications) are informational in nature, with about 12 percent each for navigational and transactional. Results also show that web queries fall into eight clusters, six primarily informational, and one each of primarily transactional and navigational.

This study provides an important contribution to web search literature because it provides information about the goals of searchers and a method for automatically classifying the intents of the user queries. Automatic classification of user intent can lead to improved web search engines by tailoring results to specific user needs.

The paper discusses how web search engines can use automatically classified user queries to provide more targeted and relevant results in web searching by implementing a real time classification method as presented in this research.

This research investigates a new application of a method for automatically classifying the intent of user queries. There has been limited research to date on automatically classifying the user intent of web queries, even though the pay‐off for web search engines can be quite beneficial.

The web is now a significant component of the recruitment and job search process. However, very little is known about how companies and job seekers use the web, and the ultimate effectiveness of this process. The specific research questions guiding this study are: how do people search for job‐related information on the web? How effective are these searches? And how likely are job seekers to find an appropriate job posting or application?

The data used to examine these questions come from job seekers submitting job‐related queries to a major web search engine at three points in time over a five‐year period.

Results indicate that individuals seeking job information generally submit only one query with several terms and over 45 percent of job‐seeking queries contain a specific location reference. Of the documents retrieved, findings suggest that only 52 percent are relevant and only 40 percent of job‐specific searches retrieve job postings.

This study provides an important contribution to web research and online recruiting literature. The data come from actual web searches, providing a realistic glimpse into how job seekers are actually using the web.

The results of this research can assist organizations in seeking to use the web as part of their recruiting efforts, in designing corporate recruiting web sites, and in developing web systems to support job seeking and recruiting.

This research is one of the first studies to investigate job searching on the web using longitudinal real world data.

This paper reports the findings of a major study examining the overlap among results retrieved by three major web search engines. The goal of the research was to: measure the overlap across three major web search engines on the first results page overlap (i.e. share the same results) and the differences across a wide range of user defined search terms; determine the differences in the first page of search results and their rankings (each web search engine's view of the most relevant content) across single‐source web search engines, including both sponsored and non‐sponsored results; and measure the degree to which a meta‐search web engine, such as Dogpile.com, provides searchers with the most highly ranked search results from three major single source web search engines.

The authors collected 10,316 random Dogpile.com queries and ran an overlap algorithm using the URL for each result by query. The overlap of first result page search for each query was then summarized across all 10,316 to determine the overall overlap metrics. For a given query, the URL of each result for each engine was retrieved from the database.

The percent of total results unique retrieved by only one of the three major web search engines was 85 percent, retrieved by two web search engines was 12 percent, and retrieved by all three web search engines was 3 percent. This small level of overlap reflects major differences in web search engines retrieval and ranking results.

This study provides an important contribution to the web research literature. The findings point to the value of meta‐search engines in web retrieval to overcome the biases of single search engines.

The results of this research can inform people and organizations that seek to use the web as part of their information seeking efforts, and the design of web search engines.

This research is a large investigation into web search engine overlap using real data from a major web meta‐search engine and single web search engines that sheds light on the uniqueness of top results retrieved by web search engines.