scholarly journals A Framework for Evaluating the Retrieval Effectiveness of Search Engines

2012 ◽  
pp. 456-479 ◽  
Author(s):  
Dirk Lewandowski
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Theoretical Framework ◽  
Short Description ◽  
Next Generation ◽  
Reliable Test ◽  
General Design ◽  
Additional Information ◽  
Retrieval Effectiveness

This chapter presents a theoretical framework for evaluating next generation search engines. The author focuses on search engines whose results presentation is enriched with additional information and does not merely present the usual list of “10 blue links,” that is, of ten links to results, accompanied by a short description. While Web search is used as an example here, the framework can easily be applied to search engines in any other area. The framework not only addresses the results presentation, but also takes into account an extension of the general design of retrieval effectiveness tests. The chapter examines the ways in which this design might influence the results of such studies and how a reliable test is best designed.

scholarly journals Responsible epistemic technologies: A social-epistemological analysis of autocompleted web search

2016 ◽  
Vol 19 (12) ◽  
pp. 1945-1963 ◽  
Author(s):  
Boaz Miller ◽  
Isaac Record
Keyword(s):  
Search Engines ◽  
Social Epistemology ◽  
Web Search ◽  
Theoretical Framework ◽  
Epistemic Responsibility ◽  
Search Terms ◽  
Epistemological Analysis

Information providing and gathering increasingly involve technologies like search engines, which actively shape their epistemic surroundings. Yet, a satisfying account of the epistemic responsibilities associated with them does not exist. We analyze automatically generated search suggestions from the perspective of social epistemology to illustrate how epistemic responsibilities associated with a technology can be derived and assigned. Drawing on our previously developed theoretical framework that connects responsible epistemic behavior to practicability, we address two questions: first, given the different technological possibilities available to searchers, the search technology, and search providers, who should bear which responsibilities? Second, given the technology’s epistemically relevant features and potential harms, how should search terms be autocompleted? Our analysis reveals that epistemic responsibility lies mostly with search providers, which should eliminate three categories of autosuggestions: those that result from organized attacks, those that perpetuate damaging stereotypes, and those that associate negative characteristics with specific individuals.

Searching web documents using a summarization approach

2016 ◽  
Vol 12 (1) ◽  
pp. 83-101 ◽  
Author(s):  
Rani Qumsiyeh ◽  
Yiu-Kai Ng
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Specific Information ◽  
Information Need ◽  
Search Query ◽  
Content Type ◽  
Additional Information ◽  
Search Results ◽  
User Query ◽  
Web Search Engines

Purpose 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. Design/methodology/approach The authors propose to develop a query-based summarizer, called QSum, in solving the existing problems of Web search engines which use titles and abstracts in capturing the contents of retrieved documents. QSum 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. Findings Experimental results show that QSum is effective and efficient in creating a high-quality summary for each cluster to enhance Web search. Originality/value The proposed query-based summarizer, QSum, is unique based on its searching approach. QSum 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.

Classification of means and methods of the Web semantic retrieval

2017 ◽  
pp. 030-050
Author(s):  
J.V. Rogushina ◽  
Keyword(s):  
Search Engines ◽  
Domain Knowledge ◽  
Information Needs ◽  
Web Search ◽  
User Interaction ◽  
Query Languages ◽  
Semantic Search ◽  
Semantic Retrieval ◽  
The Web

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

A Formal Model for Managing Multiple Observation Data in Agriculture

2019 ◽  
Vol 15 (3) ◽  
pp. 79-100 ◽  
Author(s):  
Watanee Jearanaiwongkul ◽  
Frederic Andres ◽  
Chutiporn Anutariya
Keyword(s):  
Search Engines ◽  
Question Answering ◽  
Formal Model ◽  
Theoretical Framework ◽  
Similarity Function ◽  
Observation Data ◽  
Contextual Data ◽  
Rule Based ◽  
Key Characteristics

Nowadays, farmers can search for treatments for their plants using search engines and applications. Most existing works are developed in the form of rule-based question answering platforms. However, an observation could be incorrectly given by the farmer. This work recommends that diseases and treatments must be considered from a set of related observations. Thus, we develop a theoretical framework for systems to manage a farmer's observation data. We investigate and formalize desirable characteristics of such systems. The observation data is attached with a geolocation in which related contextual data is found. The framework is formalized based on algebra, in which required types and functions are identified. Its key characteristics are described by: (1) the defined type called warncons for representing observation data; (2) the similarity function for warncons; and (3) the warncons composition function for composing similar warncons. Finally, we show that the framework helps observation data to become richer and improve advice-finding.

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

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.

A branding model for web search engines

2012 ◽  
Vol 7 (3) ◽  
pp. 195 ◽  
Author(s):  
Lu Zhang ◽  
Bernard J. Jansen ◽  
Anna S. Mattila
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Web Search Engines
Sign in / Sign up

Export Citation Format

Share Document