scholarly journals Identifying Aspects for Web-Search Queries

2011 ◽  
Vol 40 ◽  
pp. 677-700 ◽  
Author(s):  
F. Wu ◽  
J. Madhavan ◽  
A. Halevy
Keyword(s):  
Search Engine ◽  
Web Search ◽  
User Study ◽  
Effective Means ◽  
Relevant Information ◽  
Sources Of Information ◽  
Information Need ◽  
Search Queries ◽  
Mass Collaboration ◽  
Query Logs

Many web-search queries serve as the beginning of an exploration of an unknown space of information, rather than looking for a specific web page. To answer such queries effec- tively, the search engine should attempt to organize the space of relevant information in a way that facilitates exploration. We describe the Aspector system that computes aspects for a given query. Each aspect is a set of search queries that together represent a distinct information need relevant to the original search query. To serve as an effective means to explore the space, Aspector computes aspects that are orthogonal to each other and to have high combined coverage. Aspector combines two sources of information to compute aspects. We discover candidate aspects by analyzing query logs, and cluster them to eliminate redundancies. We then use a mass-collaboration knowledge base (e.g., Wikipedia) to compute candidate aspects for queries that occur less frequently and to group together aspects that are likely to be “semantically” related. We present a user study that indicates that the aspects we compute are rated favorably against three competing alternatives – related searches proposed by Google, cluster labels assigned by the Clusty search engine, and navigational searches proposed by Bing.

Associating Searching on Search Engines to Subsequent Searching on Sites

2016 ◽  
Vol 8 (2) ◽  
pp. 30-43
Author(s):  
Adan Ortiz-Cordova ◽  
Bernard J. Jansen
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Research Study ◽  
Search Queries ◽  
Web Search Engine ◽  
Search Patterns ◽  
Search Information

In this research study, the authors investigate the association between external searching, which is searching on a web search engine, and internal searching, which is searching on a website. They classify 295,571 external – internal searches where each search is composed of a search engine query that is submitted to a web search engine and then one or more subsequent queries submitted to a commercial website by the same user. The authors examine 891,453 queries from all searches, of which 295,571 were external search queries and 595,882 were internal search queries. They algorithmically classify all queries into states, and then clustered the searching episodes into major searching configurations and identify the most commonly occurring search patterns for both external, internal, and external-to-internal searching episodes. The research implications of this study are that external sessions and internal sessions must be considered as part of a continuous search episode and that online businesses can leverage external search information to more effectively target potential consumers.

Requirements to a Search Engine for Semantic Multimedia Content

2014 ◽  
Vol 5 (4) ◽  
pp. 53-65
Author(s):  
Lydia Weiland ◽  
Felix Hanser ◽  
Ansgar Scherp
Keyword(s):  
Search Engine ◽  
Web Search ◽  
User Study ◽  
Quantitative Information ◽  
Multimedia Content ◽  
Structured Interviews ◽  
Web Search Engine ◽  
Multimedia Search ◽  
Multimedia Search And Retrieval ◽  
Search And Retrieval

The authors investigate user requirements regarding the interface design for semantic multimedia search and retrieval based on a prototypical implementation of a search engine for multimedia content on the web. Thus, unlike existing image or video search engines, they are interested in true multimedia content combining different media assets into multimedia documents like PowerPoint presentations and Flash files. In a user study with 20 participants, the authors conducted a formative evaluation based on the think-aloud method and semi-structured interviews in order to obtain requirements to a future web search engine for multimedia content. The interviews are complemented by a paper-and-pencil questionnaire to obtain quantitative information. As a result, the authors elicit requirements to a web search engine for multimedia content. Among them, scalability and personalization of the presented information are identified as the main goals. Based on the requirements, they present mockups demonstrating the user interface of a future multimedia search and retrieval engine.

Enhancing Web Search through Query Log Mining

2011 ◽  
pp. 438-442
Author(s):  
Ji-Rong Wen
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
Web Mining ◽  
Web Search ◽  
Information Source ◽  
Query Log ◽  
Additional Information ◽  
Query Logs ◽  
Query Log Mining ◽  
The Web

Web query log is a type of file keeping track of the activities of the users who are utilizing a search engine. Compared to traditional information retrieval setting in which documents are the only information source available, query logs are an additional information source in the Web search setting. Based on query logs, a set of Web mining techniques, such as log-based query clustering, log-based query expansion, collaborative filtering and personalized search, could be employed to improve the performance of Web search.

scholarly journals The Semanference System: Better Search Results through Better Queries

10.28945/2570 ◽  
2002 ◽  
Author(s):  
Anthony Scime ◽  
Colleen Powderly
Keyword(s):  
Web Search ◽  
Information Need ◽  
Semantic Approach ◽  
Search Queries ◽  
Search Results ◽  
Frequency Of Use ◽  
Key Phrases

A method to create more effective Web search queries is to combine elements of a semantic approach with a template that requests specific details about the searcher’s information need. Fundamental to this process is the use of semantics. Nouns, key phrases, and verbs are scored according to their frequency of use, then ranked as keywords and used to create the query. Key phrases and words in the query accurately represent the concepts of the text, generating search results that are significantly more accurate than those available using current methods.

Enhancing Web Search through Query Log Mining

2011 ◽  
pp. 758-763 ◽  
Author(s):  
Ji-Rong Wen
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
Web Mining ◽  
Web Search ◽  
Information Source ◽  
Query Log ◽  
Additional Information ◽  
Query Logs ◽  
Query Log Mining ◽  
The Web

Web query log is a type of file keeping track of the activities of the users who are utilizing a search engine. Compared to traditional information retrieval setting in which documents are the only information source available, query logs are an additional information source in the Web search setting. Based on query logs, a set of Web mining techniques, such as log-based query clustering, log-based query expansion, collaborative filtering and personalized search, could be employed to improve the performance of Web search.

Processing and Analysis of Search Query Logs in Chinese

2011 ◽  
pp. 378-388 ◽  
Author(s):  
Michael Chau ◽  
Yan Lu ◽  
Xiao Fang ◽  
Christopher C. Yang
Keyword(s):  
World Wide ◽  
Web Search ◽  
Searching Behavior ◽  
Web Searching ◽  
Search Queries ◽  
Web Search Engine ◽  
The World ◽  
Query Logs ◽  
Search Logs ◽  
The Web

More non-English contents are now available on the World Wide Web and the number of non-English users on the Web is increasing. While it is important to understand the Web searching behavior of these non-English users, many previous studies on Web query logs have focused on analyzing English search logs and their results may not be directly applied to other languages. In this Chapter we discuss some methods and techniques that can be used to analyze search queries in Chinese. We also show an example of applying our methods on a Chinese Web search engine. Some interesting findings are reported.

A Semantic-Based Dynamic Search Engine Design and Implementation for Electronic Medical Records

2012 ◽  
pp. 285-297
Author(s):  
Weider D. Yu ◽  
Seshadri K. Yilayavilli
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
First Generation ◽  
Web Search ◽  
Relevant Information ◽  
Engine Design ◽  
Search Service ◽  
Web Search Engines ◽  
World Information ◽  
The Right

In the current technology driven world, information retrieval activities are in almost every aspect of daily, as society uses popular web search engines like Google, Yahoo!, Live Search, Ask, and so forth to obtain helpful information. Often, these popular search engines look for and obtain key information; however, not all of the retrieved items are relevant in context to the search target a. Thus, it is left for the user to filter out unwanted information, using only a few information items left from the search results. These popular web search engines use a first generation search service based on “static keywords”, which require the users to know exactly what they want to search and enter the right keywords. This approach puts the user at a disadvantage. In this paper, the authors investigate and design a dynamic, question-answer search engine that enables searching by attributes for more precise and relevant information in Electronic Medical Record (EMR) field.

scholarly journals Semantic Web Search Engines : A Comparative Survey

2019 ◽  
pp. 107-115
Author(s):  
Anita Kumari ◽  
Jawahar Thakur
Keyword(s):  
Semantic Web ◽  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Retrieval System ◽  
Relevant Information ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Web Search Engine ◽  
Comparative Survey

Search engines play important role in the success of the Web. Search engine helps the users to find the relevant information on the internet. Due to many problems in traditional search engines has led to the development of semantic web. Semantic web technologies are playing a crucial role in enhancing traditional search, as it work to create machines readable data and focus on metadata. However, it will not replace traditional search engines. In the environment of semantic web, search engine should be more useful and efficient for searching the relevant web information. It is a way to increase the accuracy of information retrieval system. This is possible because semantic web uses software agents; these agents collect the information, perform relevant transactions and interact with physical devices. This paper includes the survey on the prevalent Semantic Search Engines based on their advantages, working and disadvantages and presents a comparative study based on techniques, type of results, crawling, and indexing.

Web 2.0

2010 ◽  
pp. 1176-1182
Author(s):  
Jos van Iwaarden ◽  
Ton van der Wiele ◽  
Roger Williams ◽  
Steve Eldridge
Keyword(s):  
Search Engine ◽  
Web Sites ◽  
Relevant Information ◽  
The Internet ◽  
Web Pages ◽  
Sources Of Information ◽  
Social Bookmarking ◽  
Video Sharing ◽  
Internet Search Engine ◽  
A Company

The Internet has come of age as a global source of information about every topic imaginable. A company like Google has become a household name in Western countries and making use of its internet search engine is so popular that “Googling” has even become a verb in many Western languages. Whether it is for business or private purposes, people worldwide rely on Google to present them relevant information. Even the scientific community is increasingly employing Google’s search engine to find academic articles and other sources of information about the topics they are studying. Yet, the vast amount of information that is available on the internet is gradually changing in nature. Initially, information would be uploaded by the administrators of the web site and would then be visible to all visitors of the site. This approach meant that web sites tended to be limited in the amount of content they provided, and that such content was strictly controlled by the administrators. Over time, web sites have granted their users the authority to add information to web pages, and sometimes even to alter existing information. Current examples of such web sites are eBay (auction), Wikipedia (encyclopedia), YouTube (video sharing), LinkedIn (social networking), Blogger (weblogs) and Delicious (social bookmarking).

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