Image searching on the Excite Web search engine

A growing body of research is beginning to explore the information-seeking behavior of Web users. The vast majority of these studies have concentrated on the area of textual information retrieval (IR). Little research has examined how people search for non-textual information on the Internet, and few large-scale studies has investigated visual information-seeking behavior with general-purpose Web search engines. This study examined visual information needs as expressed in users’ Web image queries. The data set examined consisted of 1,025,908 sequential queries from 211,058 users of Excite, a major Internet search service. Twenty-eight terms were used to identify queries for both still and moving images, resulting in a subset of 33,149 image queries by 9855 users. We provide data on: (1) image queries – the number of queries and the number of search terms per user, (2) image search sessions – the number of queries per user, modifications made to subsequent queries in a session, and (3) image terms – their rank/frequency distribution and the most highly used search terms. On average, there were 3.36 image queries per user containing an average of 3.74 terms per query. Image queries contained a large number of unique terms. The most frequently occurring image related terms appeared less than 10% of the time, with most terms occurring only once. We contrast this to earlier work by P.G.B. Enser, Journal of Documentation 51 (2) (1995) 126–170, who examined written queries for pictorial information in a non-digital environment. Implications for the development of models for visual information retrieval, and for the design of Web search engines are discussed.     

Syntactic complexity of Web search queries through the lenses of language models,networks and users

Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication.     

A study of results overlap and uniqueness among major Web search engines

The performance and capabilities of Web search engines is an important and significant area of research. Millions of people world wide use Web search engines very day. This paper reports the results of a major study examining the overlap among results retrieved by multiple Web search engines for a large set of more than 10,000 queries. Previous smaller studies have discussed a lack of overlap in results returned by Web search engines for the same queries. The goal of the current study was to conduct a large-scale study to measure the overlap of search results on the first result page (both non-sponsored and sponsored) across the four most popular Web search engines, at specific points in time using a large number of queries. The Web search engines included in the study were MSN Search, Google, Yahoo! and Ask Jeeves. Our study then compares these results with the first page results retrieved for the same queries by the metasearch engine Dogpile.com. Two sets of randomly selected user-entered queries, one set was 10,316 queries and the other 12,570 queries, from Infospace’s Dogpile.com search engine (the first set was from Dogpile, the second was from across the Infospace Network of search properties were submitted to the four single Web search engines). Findings show that the percent of total results unique to only one of the four Web search engines was 84.9%, shared by two of the three Web search engines was 11.4%, shared by three of the Web search engines was 2.6%, and shared by all four Web search engines was 1.1%. This small degree of overlap shows the significant difference in the way major Web search engines retrieve and rank results in response to given queries. Results point to the value of metasearch engines in Web retrieval to overcome the biases of individual search engines.     

Determining the informational,navigational, and transactional intent of Web queries

In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.     

How are we searching the World Wide Web? A comparison of nine search engine transaction logs

The Web and especially major Web search engines are essential tools in the quest to locate online information for many people. This paper reports results from research that examines characteristics and changes in Web searching from nine studies of five Web search engines based in the US and Europe. We compare interactions occurring between users and Web search engines from the perspectives of session length, query length, query complexity, and content viewed among the Web search engines. The results of our research shows (1) users are viewing fewer result pages, (2) searchers on US-based Web search engines use more query operators than searchers on European-based search engines, (3) there are statistically significant differences in the use of Boolean operators and result pages viewed, and (4) one cannot necessary apply results from studies of one particular Web search engine to another Web search engine. The wide spread use of Web search engines, employment of simple queries, and decreased viewing of result pages may have resulted from algorithmic enhancements by Web search engine companies. We discuss the implications of the findings for the development of Web search engines and design of online content.     

Tagging and searching: Search retrieval effectiveness of folksonomies on the World Wide Web

Many Web sites have begun allowing users to submit items to a collection and tag them with keywords. The folksonomies built from these tags are an interesting topic that has seen little empirical research. This study compared the search information retrieval (IR) performance of folksonomies from social bookmarking Web sites against search engines and subject directories. Thirty-four participants created 103 queries for various information needs. Results from each IR system were collected and participants judged relevance. Folksonomy search results overlapped with those from the other systems, and documents found by both search engines and folksonomies were significantly more likely to be judged relevant than those returned by any single IR system type. The search engines in the study had the highest precision and recall, but the folksonomies fared surprisingly well. Del.icio.us was statistically indistinguishable from the directories in many cases. Overall the directories were more precise than the folksonomies but they had similar recall scores. Better query handling may enhance folksonomy IR performance further. The folksonomies studied were promising, and may be able to improve Web search performance.     

Finding information on the World Wide Web: the retrieval effectiveness of search engines

Search engines are essential for finding information on the World Wide Web. We conducted a study to see how effective eight search engines are. Expert searchers sought information on the Web for users who had legitimate needs for information, and these users assessed the relevance of the information retrieved. We calculated traditional information retrieval measures of recall and precision at varying numbers of retrieved documents and used these as the bases for statistical comparisons of retrieval effectiveness among the eight search engines. We also calculated the likelihood that a document retrieved by one search engine was retrieved by other search engines as well.     

An analysis of Web searching by European AlltheWeb.com users

The Web has become a worldwide source of information and a mainstream business tool. It is changing the way people conduct the daily business of their lives. As these changes are occurring, we need to understand what Web searching trends are emerging within the various global regions. What are the regional differences and trends in Web searching, if any? What is the effectiveness of Web search engines as providers of information? As part of a body of research studying these questions, we have analyzed two data sets collected from queries by mainly European users submitted to AlltheWeb.com on 6 February 2001 and 28 May 2002. AlltheWeb.com is a major and highly rated European search engine. Each data set contains approximately a million queries submitted by over 200,000 users and spans a 24-h period. This longitudinal benchmark study shows that European Web searching is evolving in certain directions. There was some decline in query length, with extremely simple queries. European search topics are broadening, with a notable percentage decline in sexual and pornographic searching. The majority of Web searchers view fewer than five Web documents, spending only seconds on a Web document. Approximately 50% of the Web documents viewed by these European users were topically relevant. We discuss the implications for Web information systems and information content providers.     

Application of automatic topic identification on Excite Web search engine data logs

The analysis of contextual information in search engine query logs enhances the understanding of Web users’ search patterns. Obtaining contextual information on Web search engine logs is a difficult task, since users submit few number of queries, and search multiple topics. Identification of topic changes within a search session is an important branch of search engine user behavior analysis. The purpose of this study is to investigate the properties of a specific topic identification methodology in detail, and to test its validity. The topic identification algorithm’s performance becomes doubtful in various cases. These cases are explored and the reasons underlying the inconsistent performance of automatic topic identification are investigated with statistical analysis and experimental design techniques.     

Characteristics of character usage in Chinese Web searching

The use of non-English Web search engines has been prevalent. Given the popularity of Chinese Web searching and the unique characteristics of Chinese language, it is imperative to conduct studies with focuses on the analysis of Chinese Web search queries. In this paper, we report our research on the character usage of Chinese search logs from a Web search engine in Hong Kong. By examining the distribution of search query terms, we found that users tended to use more diversified terms and that the usage of characters in search queries was quite different from the character usage of general online information in Chinese. After studying the Zipf distribution of n-grams with different values of n, we found that the curve of unigram is the most curved one of all while the bigram curve follows the Zipf distribution best, and that the curves of n-grams with larger n (n = 3–6) had similar structures with β-values in the range of 0.66–0.86. The distribution of combined n-grams was also studied. All the analyses are performed on the data both before and after the removal of function terms and incomplete terms and similar findings are revealed. We believe the findings from this study have provided some insights into further research in non-English Web searching and will assist in the design of more effective Chinese Web search engines.     

Analysis of large data logs: an application of Poisson sampling on excite web queries

Search engines are the gateway for users to retrieve information from the Web. There is a crucial need for tools that allow effective analysis of search engine queries to provide a greater understanding of Web users' information seeking behavior. The objective of the study is to develop an effective strategy for the selection of samples from large-scale data sets. Millions of queries are submitted to Web search engines daily and new sampling techniques are required to bring these databases to a manageable size, while preserving the statistically representative characteristics of the entire data set. This paper reports results from a study using data logs from the Excite Web search engine. We use Poisson sampling to develop a sampling strategy, and show how sample sets selected by Poisson sampling statistically effectively represent the characteristics of the entire dataset. In addition, this paper discusses the use of Poisson sampling in continuous monitoring of stochastic processes, such as Web site dynamics.     

Characteristics of question format web queries: an exploratory study

Web queries in question format are becoming a common element of a user's interaction with Web search engines. Web search services such as Ask Jeeves – a publicly accessible question and answer (Q&A) search engine – request users to enter question format queries. This paper provides results from a study examining queries in question format submitted to two different Web search engines – Ask Jeeves that explicitly encourages queries in question format and the Excite search service that does not explicitly encourage queries in question format. We identify the characteristics of queries in question format in two different data sets: (1) 30,000 Ask Jeeves queries and 15,575 Excite queries, including the nature, length, and structure of queries in question format. Findings include: (1) 50% of Ask Jeeves queries and less than 1% of Excite were in question format, (2) most users entered only one query in question format with little query reformulation, (3) limited range of formats for queries in question format – mainly “where”, “what”, or “how” questions, (4) most common question query format was “Where can I find………” for general information on a topic, and (5) non-question queries may be in request format. Overall, four types of user Web queries were identified: keyword, Boolean, question, and request. These findings provide an initial mapping of the structure and content of queries in question and request format. Implications for Web search services are discussed.     

Computing controversy: Formal model and algorithms for detecting controversy on Wikipedia and in search queries

Controversy is a complex concept that has been attracting attention of scholars from diverse fields. In the era of Internet and social media, detecting controversy and controversial concepts by the means of automatic methods is especially important. Web searchers could be alerted when the contents they consume are controversial or when they attempt to acquire information on disputed topics. Presenting users with the indications and explanations of the controversy should offer them chance to see the “wider picture” rather than letting them obtain one-sided views. In this work we first introduce a formal model of controversy as the basis of computational approaches to detecting controversial concepts. Then we propose a classification based method for automatic detection of controversial articles and categories in Wikipedia. Next, we demonstrate how to use the obtained results for the estimation of the controversy level of search queries. The proposed method can be incorporated into search engines as a component responsible for detection of queries related to controversial topics. The method is independent of the search engine’s retrieval and search results recommendation algorithms, and is therefore unaffected by a possible filter bubble.Our approach can be also applied in Wikipedia or other knowledge bases for supporting the detection of controversy and content maintenance. Finally, we believe that our results could be useful for social science researchers for understanding the complex nature of controversy and in fostering their studies.     

The effectiveness of Web search engines for retrieving relevant ecommerce links

Ecommerce is developing into a fast-growing channel for new business, so a strong presence in this domain could prove essential to the success of numerous commercial organizations. However, there is little research examining ecommerce at the individual customer level, particularly on the success of everyday ecommerce searches. This is critical for the continued success of online commerce. The purpose of this research is to evaluate the effectiveness of search engines in the retrieval of relevant ecommerce links. The study examines the effectiveness of five different types of search engines in response to ecommerce queries by comparing the engines’ quality of ecommerce links using topical relevancy ratings. This research employs 100 ecommerce queries, five major search engines, and more than 3540 Web links. The findings indicate that links retrieved using an ecommerce search engine are significantly better than those obtained from most other engines types but do not significantly differ from links obtained from a Web directory service. We discuss the implications for Web system design and ecommerce marketing campaigns.     

Evaluating WordBars in exploratory Web search scenarios

Web searchers commonly have difficulties crafting queries to fulfill their information needs; even after they are able to craft a query, they often find it challenging to evaluate the results of their Web searches. Sources of these problems include the lack of support for constructing and refining queries, and the static nature of the list-based representations of Web search results. WordBars has been developed to assist users in their Web search and exploration tasks. This system provides a visual representation of the frequencies of the terms found in the first 100 document surrogates returned from an initial query, in the form of a histogram. Exploration of the search results is supported through term selection in the histogram, resulting in a re-sorting of the search results based on the use of the selected terms in the document surrogates. Terms from the histogram can be easily added or removed from the query, generating a new set of search results. Examples illustrate how WordBars can provide valuable support for query refinement and search results exploration, both when vague and specific initial queries are provided. User evaluations with both expert and intermediate Web searchers illustrate the benefits of the interactive exploration features of WordBars in terms of effectiveness as well as subjective measures. Although differences were found in the demographics of these two user groups, both were able to benefit from the features of WordBars.     

Comparing rankings of search results on the Web

The Web has become an information source for professional data gathering. Because of the vast amounts of information on almost all topics, one cannot systematically go over the whole set of results, and therefore must rely on the ordering of the results by the search engine. It is well known that search engines on the Web have low overlap in terms of coverage. In this study we measure how similar are the rankings of search engines on the overlapping results.We compare rankings of results for identical queries retrieved from several search engines. The method is based only on the set of URLs that appear in the answer sets of the engines being compared. For comparing the similarity of rankings of two search engines, the Spearman correlation coefficient is computed. When comparing more than two sets Kendall’s W is used. These are well-known measures and the statistical significance of the results can be computed. The methods are demonstrated on a set of 15 queries that were submitted to four large Web search engines. The findings indicate that the large public search engines on the Web employ considerably different ranking algorithms.     

Automatic suggestion of phrasal-concept queries for literature search

Both general and domain-specific search engines have adopted query suggestion techniques to help users formulate effective queries. In the specific domain of literature search (e.g., finding academic papers), the initial queries are usually based on a draft paper or abstract, rather than short lists of keywords. In this paper, we investigate phrasal-concept query suggestions for literature search. These suggestions explicitly specify important phrasal concepts related to an initial detailed query. The merits of phrasal-concept query suggestions for this domain are their readability and retrieval effectiveness: (1) phrasal concepts are natural for academic authors because of their frequent use of terminology and subject-specific phrases and (2) academic papers describe their key ideas via these subject-specific phrases, and thus phrasal concepts can be used effectively to find those papers. We propose a novel phrasal-concept query suggestion technique that generates queries by identifying key phrasal-concepts from pseudo-labeled documents and combines them with related phrases. Our proposed technique is evaluated in terms of both user preference and retrieval effectiveness. We conduct user experiments to verify a preference for our approach, in comparison to baseline query suggestion methods, and demonstrate the effectiveness of the technique with retrieval experiments.     

Querying XML documents using Prolog engines: When is this a good idea?

XML has become a universal standard for information exchange over the Web due to features such as simple syntax and extensibility. Processing queries over these documents has been the focus of several research groups. In fact, there is broad literature in efficient XML query processing which explore indexes, fragmentation techniques, etc. However, for answering complex queries, existing approaches mainly analyze information that is explicitly defined in the XML document. A few work investigate the use of Prolog to increase the query possibilities, allowing inference over the data content. This can cause a significant increase in the query possibilities and expressive power, allowing access to non-obvious information. However, this requires translating the XML documents into Prolog facts. But for regular queries (which do not require inference), is this a good alternative? What kind of queries could benefit from the Prolog translation? Can we always use Prolog engines to execute XML queries in an efficient way? There are many questions involved in adopting an alternative approach to run XML queries. In this work, we investigate this matter by translating XML queries into Prolog queries and comparing the query processing times using Prolog and native XML engines. Our work contributes by providing a set of heuristics that helps users to decide when to use Prolog engines to process a given XML query. In summary, our results show that queries that search elements by a key value or by its position (simple search) are more efficient when run in Prolog than in native XML engines. Also, queries over large datasets, or that searches for substrings perform better when run by native XML engines.     

Iterative radial basis functions neural networks as metamodels of stochastic simulations of the quality of search engines in the World Wide Web

Stochastic simulation has been very effective in many domains but never applied to the WWW. This study is a premiere in using neural networks in stochastic simulation of the number of rejected Web pages per search query. The evaluation of the quality of search engines should involve not only the resulting set of Web pages but also an estimate of the rejected set of Web pages. The iterative radial basis functions (RBF) neural network developed by Meghabghab and Nasr [Iterative RBF neural networks as meta-models for stochastic simulations, in: Second International Conference on Intelligent Processing and Manufacturing of Materials, IPMM’99, Honolulu, Hawaii, 1999, pp. 729–734] was adapted to the actual evaluation of the number of rejected Web pages on four search engines, i.e., Yahoo, Alta Vista, Google, and Northern Light. Nine input variables were selected for the simulation: (1) precision, (2) overlap, (3) response time, (4) coverage, (5) update frequency, (6) boolean logic, (7) truncation, (8) word and multi-word searching, (9) portion of the Web pages indexed. Typical stochastic simulation meta-modeling uses regression models in response surface methods. RBF becomes a natural target for such an attempt because they use a family of surfaces each of which naturally divides an input space into two regions X+ and X− and the n patterns for testing will be assigned either class X+ or X−. This technique divides the resulting set of responses to a query into accepted and rejected Web pages. To test the hypothesis that the evaluation of any search engine query should involve an estimate of the number of rejected Web pages as part of the evaluation, RBF meta-model was trained on 937 examples from a set of 9000 different simulation runs on the nine different input variables. Results show that two of the variables can be eliminated which include: response time and portion of the Web indexed without affecting evaluation results. Results show that the number of rejected Web pages for a specific set of search queries on these four engines very high. Also a goodness measure of a search engine for a given set of queries can be designed which is a function of the coverage of the search engine and the normalized age of a new document in result set for the query. This study concludes that unless search engine designers address the issue of rejected Web pages, indexing, and crawling, the usage of the Web as a research tool for academic and educational purposes will stay hindered.