A Study on Web Searching

Web search engines are one of the most popular services to help users find useful information on the Web. Although many studies have been carried out to estimate the size and overlap of the general web search engines, it may not benefit the ordinary web searching users, since they care more about the overlap of the top N (N=10, 20 or 50) search results on concrete queries, but not the overlap of the total index database. In this study, we present experimental results on the comparison of the overlap of the top N (N=10, 20 or 50) search results from AlltheWeb, Google, AltaVista and WiseNut for the 58 most popular queries, as well as for the distance of the overlapped results. These 58 queries are chosen from WordTracker service, which records the most popular queries submitted to some famous metasearch engines, such as MetaCrawler and Dogpile. We divide these 58 queries into three categories for further investigation. Through in-depth study, we observe a number of interesting results: the overlap of the top N results retrieved by different search engines is very small; the search results of the queries in different categories behave in dramatically different ways; Google, on average, has the highest overlap among these four search engines; each search engine tends to adopt a different rank algorithm independently.

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TemporalClassifier

Information Retrieval and Management ◽

10.4018/978-1-5225-5191-1.ch049 ◽

2018 ◽

pp. 1143-1165

Author(s):

Rahul Pradhan ◽

Dilip Kumar Sharma

Keyword(s):

Search Engine ◽

Search Engines ◽

Web Search ◽

Web Page ◽

Temporal Expression ◽

Course Of Action ◽

Temporal Boundary ◽

Web Search Engines ◽

The Web

Users issuing query on search engine, expect results to more relevant to query topic rather than just the textual match with text in query. Studies conducted by few researchers shows that user want the search engine to understand the implicit intent of query rather than looking the textual match in hypertext structure of document or web page. In this paper the authors will be addressing queries that have any temporal intent and help the web search engines to classify them in certain categories. These classes or categories will help search engine to understand and cater the need of query. The authors will consider temporal expression (e.g. 1943) in document and categories them on the basis of temporal boundary of that query. Their experiment classifies the query and tries to suggest further course of action for search engines. Results shows that classifying the query to these classes will help user to reach his/her seeking information faster.

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Users’ Click and Bookmark Based Personalization Using Modified Agglomerative Clustering for Web Search Engine

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213017300022 ◽

2017 ◽

Vol 26 (06) ◽

pp. 1730002 ◽

Cited By ~ 3

Author(s):

T. Dhiliphan Rajkumar ◽

S. P. Raja ◽

A. Suruliandi

Keyword(s):

Information Retrieval ◽

Search Engine ◽

Search Engines ◽

Web Search ◽

Irrelevant Information ◽

Experimental Results ◽

Agglomerative Clustering ◽

Semantic Level ◽

Web Search Engine ◽

The Web

Short and ambiguous queries are the major problems in search engines which lead to irrelevant information retrieval for the users’ input. The increasing nature of the information on the web also makes various difficulties for the search engine to provide the users needed results. The web search engine experience the ill effects of ambiguity, since the queries are looked at on a rational level rather than the semantic level. In this paper, for improving the performance of search engine as of the users’ interest, personalization is based on the users’ clicks and bookmarking is proposed. Modified agglomerative clustering is used in this work for clustering the results. The experimental results prove that the proposed work scores better precision, recall and F-score.

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TemporalClassifier

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2015100103 ◽

2015 ◽

Vol 10 (4) ◽

pp. 44-66 ◽

Cited By ~ 3

Author(s):

Rahul Pradhan ◽

Dilip Kumar Sharma

Keyword(s):

Search Engine ◽

Search Engines ◽

Web Search ◽

Web Page ◽

Temporal Expression ◽

Course Of Action ◽

Temporal Boundary ◽

Web Search Engines ◽

The Web

Users issuing query on search engine, expect results to more relevant to query topic rather than just the textual match with text in query. Studies conducted by few researchers shows that user want the search engine to understand the implicit intent of query rather than looking the textual match in hypertext structure of document or web page. In this paper the authors will be addressing queries that have any temporal intent and help the web search engines to classify them in certain categories. These classes or categories will help search engine to understand and cater the need of query. The authors will consider temporal expression (e.g. 1943) in document and categories them on the basis of temporal boundary of that query. Their experiment classifies the query and tries to suggest further course of action for search engines. Results shows that classifying the query to these classes will help user to reach his/her seeking information faster.

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Cluster Search Engine Results with Crowd Intelligence

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.3375 ◽

2013 ◽

Vol 284-287 ◽

pp. 3375-3379

Author(s):

Chun Hsiung Tseng ◽

Fu Cheng Yang ◽

Yu Ping Tseng ◽

Yi Yun Chang

Keyword(s):

Search Engine ◽

Search Engines ◽

Web Search ◽

Web Pages ◽

Huge Amount ◽

Search Results ◽

Web Search Engines ◽

Crowd Intelligence ◽

User Queries

Most Web users today rely heavily on search engines to gather information. To achieve better search results, some algorithms such as PageRank have been developed. However, most Web search engines employ keyword-based search and thus have some natural weaknesses. Among these problems, a well-known one is that it is very difficult for search engines to infer semantics from user queries and returned results. Hence, despite of efforts of ranking search results, users may still have to navigate through a huge amount of Web pages to locate the desired resources. In this research, the researchers developed a clustering-based methodology to improve the performance of search engines. Instead of extracting features used for clustering from the returned documents, the proposed method extracts features from the delicious service, which is actually a tag provider service. By utilizing such information, the resulting system can benefit from crowd intelligence. The obtained information is then used for enhancing the performance of the ordinary k-means algorithm to achieve better clustering results.

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Retriever

Managing Business with Electronic Commerce ◽

10.4018/978-1-930708-12-9.ch004 ◽

2002 ◽

pp. 59-81 ◽

Cited By ~ 7

Author(s):

Anupam Joshi ◽

Zhihua Jiang

Keyword(s):

Vector Space ◽

Search Engine ◽

Search Engines ◽

Web Search ◽

State Of The Art ◽

Dissimilarity Matrix ◽

Fuzzy Algorithm ◽

Web Search Engines ◽

N Gram ◽

The Web

Web search engines have become increasingly ineffective as the number of documents on the Web have proliferated. Typical queries retrieve hundreds of documents, most of which have no relation with what the user was looking for. The chapter describes a system named Retriever that uses a recently proposed robust fuzzy algorithm RFCMdd to cluster the results of a query from a search engine into groups. These groups and their associated keywords are presented to the user, who can then look into the URLs for the group(s) that s/he finds interesting. This application requires clustering in the presence of a significant amount of noise, which our system can handle efficiently. N-Gram and Vector Space methods are used to create the dissimilarity matrix for clustering. We discuss the performance of our system by comparing it with other state-of-the-art peers, such as Husky search, and present the results from analyzing the effectiveness of the N-Gram and Vector Space methods during the generation of dissimilarity matrices.

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The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines

Social Science Computer Review ◽

10.1177/08944393211006863 ◽

2021 ◽

pp. 089443932110068

Author(s):

Aleksandra Urman ◽

Mykola Makhortykh ◽

Roberto Ulloa

Keyword(s):

Search Engine ◽

Search Engines ◽

Large Scale ◽

Web Search ◽

Primary Elections ◽

Virtual Agents ◽

Search Results ◽

Presidential Primary ◽

Large Scale Analysis ◽

Algorithmic Information

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.

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A Roadmap to Integrate Document Clustering in Information Retrieval

Information Retrieval Methods for Multidisciplinary Applications ◽

10.4018/978-1-4666-3898-3.ch003 ◽

2013 ◽

pp. 31-45

Author(s):

R. Subhashini ◽

V.Jawahar Senthil Kumar

Keyword(s):

Information Retrieval ◽

Search Engines ◽

World Wide ◽

Clustering Algorithm ◽

Web Search ◽

Full Potential ◽

Digital Information ◽

Search Results ◽

The World ◽

The Web

The World Wide Web is a large distributed digital information space. The ability to search and retrieve information from the Web efficiently and effectively is an enabling technology for realizing its full potential. Information Retrieval (IR) plays an important role in search engines. Today’s most advanced engines use the keyword-based (“bag of words”) paradigm, which has inherent disadvantages. Organizing web search results into clusters facilitates the user’s quick browsing of search results. Traditional clustering techniques are inadequate because they do not generate clusters with highly readable names. This paper proposes an approach for web search results in clustering based on a phrase based clustering algorithm. It is an alternative to a single ordered result of search engines. This approach presents a list of clusters to the user. Experimental results verify the method’s feasibility and effectiveness.

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How People Search for Governmental Information on the Web

Encyclopedia of Digital Government ◽

10.4018/978-1-59140-789-8.ch140 ◽

2011 ◽

pp. 933-939

Author(s):

B. J. Jansen ◽

A. Spink

Keyword(s):

Web Sites ◽

Search Engines ◽

Web Search ◽

Daily Lives ◽

Governmental Organizations ◽

Web Search Engines ◽

People Search ◽

E Mail ◽

The U.S ◽

The Web

People are now confronted with the task of locating electronic information needed to address the issues of their daily lives. The Web is presently the major information source for many people in the U.S. (Cole, Suman, Schramm, Lunn, & Aquino, 2003), used more than newspapers, magazines, and television as a source of information. Americans are expanding their use of the Web for all sorts of information and commercial purposes (Horrigan, 2004; Horrigan & Rainie, 2002; National Telecommunications and Information Administration, 2002). Searching for information is one of the most popular Web activities, second only to the use of e-mail (Nielsen Media, 1997). However, successfully locating needed information remains a difficult and challenging task (Eastman & Jansen, 2003). Locating relevant information not only affects individuals but also commercial, educational, and governmental organizations. This is especially true in regards to people interacting with their governmental agencies. Executive Order 13011 (Clinton, 1996) directed the U.S. federal government to move aggressively with strategies to utilize the Internet. Birdsell and Muzzio (1999) present the growing presence of governmental Web sites, classifying them into three general categories, (1) provision of information, (2) delivery of forms, and (3) transactions. In 2004, 29% of American said they visited a government Web site to contact some governmental entity, 18% sent an e-mail and 22% use multiple means (Horrigan, 2004). It seems clear that the Web is a major conduit for accessing governmental information and maybe services. Search engines are the primary means for people to locate Web sites (Nielsen Media, 1997). Given the Web’s importance, we need to understand how Web search engines perform (Lawrence & Giles, 1998) and how people use and interact with Web search engines to locate governmental information. Examining Web searching for governmental information is an important area of research with the potential to increase our understanding of users of Web-based governmental information, advance our knowledge of Web searchers’ governmental information needs, and positively impact the design of Web search engines and sites that specialize in governmental information.

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WEBCONTENT VISUALIZER: A VISUALIZATION SYSTEM FOR SEARCH ENGINES IN SEMATIC WEB

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622011004646 ◽

2011 ◽

Vol 10 (05) ◽

pp. 913-931 ◽

Cited By ~ 2

Author(s):

XIANYONG FANG ◽

CHRISTIAN JACQUEMIN ◽

FRÉDÉRIC VERNIER

Keyword(s):

Semantic Web ◽

Search Engine ◽

Search Engines ◽

Web Search ◽

Search Query ◽

Visualization System ◽

Xml Documents ◽

Web Search Engines ◽

New Generation

Since the results from Semantic Web search engines are highly structured XML documents, they cannot be efficiently visualized with traditional explorers. Therefore, the Semantic Web calls for a new generation of search query visualizers that can rely on document metadata. This paper introduces such a visualization system called WebContent Visualizer that is used to display and browse search engine results. The visualization is organized into three levels: (1) Carousels contain documents with the same ranking, (2) carousels are piled into stacks, one for each date, and (3) these stacks are organized along a meta-carousel to display the results for several dates. Carousel stacks are piles of local carousels with increasing radii to visualize the ranks of classes. For document comparison, colored links connect documents between neighboring classes on the basis of shared entities. Based on these techniques, the interface is made of three collaborative components: an inspector window, a visualization panel, and a detailed dialog component. With this architecture, the system is intended to offer an efficient way to explore the results returned by Semantic Web search engines.

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