9,637 research outputs found
Evaluating the retrieval effectiveness of Web search engines using a representative query sample
Search engine retrieval effectiveness studies are usually small-scale, using
only limited query samples. Furthermore, queries are selected by the
researchers. We address these issues by taking a random representative sample
of 1,000 informational and 1,000 navigational queries from a major German
search engine and comparing Google's and Bing's results based on this sample.
Jurors were found through crowdsourcing, data was collected using specialised
software, the Relevance Assessment Tool (RAT). We found that while Google
outperforms Bing in both query types, the difference in the performance for
informational queries was rather low. However, for navigational queries, Google
found the correct answer in 95.3 per cent of cases whereas Bing only found the
correct answer 76.6 per cent of the time. We conclude that search engine
performance on navigational queries is of great importance, as users in this
case can clearly identify queries that have returned correct results. So,
performance on this query type may contribute to explaining user satisfaction
with search engines
Folksonomy: the New Way to Serendipity
Folksonomy expands the collaborative process by allowing contributors to index content. It rests on three powerful properties: the absence of a prior taxonomy, multi-indexation and the absence of thesaurus. It concerns a more exploratory search than an entry in a search engine. Its original relationship-based structure (the three-way relationship between users, content and tags) means that folksonomy allows various modalities of curious explorations: a cultural exploration and a social exploration. The paper has two goals. Firstly, it tries to draw a general picture of the various folksonomy websites. Secundly, since labelling lacks any standardisation, folksonomies are often under threat of invasion by noise. This paper consequently tries to explore the different possible ways of regulating the self-generated indexation process.taxonomy; indexation; innovation and user-created content
A Comparison of Source Distribution and Result Overlap in Web Search Engines
When it comes to search engines, users generally prefer Google. Our study
aims to find the differences between the results found in Google compared to
other search engines. We compared the top 10 results from Google, Bing,
DuckDuckGo, and Metager, using 3,537 queries generated from Google Trends from
Germany and the US. Google displays more unique domains in the top results than
its competitors. Wikipedia and news websites are the most popular sources
overall. With some top sources dominating search results, the distribution of
domains is also consistent across all search engines. The overlap between
Google and Bing is always under 32%, while Metager has a higher overlap with
Bing than DuckDuckGo, going up to 78%. This study shows that the use of another
search engine, especially in addition to Google, provides a wider variety in
sources and might lead the user to find new perspectives.Comment: Submitted to the 85th Annual Meeting of the Association for
Information Science & Technology and will be published in the conference
proceeding
Search Engine Similarity Analysis: A Combined Content and Rankings Approach
How different are search engines? The search engine wars are a favorite topic
of on-line analysts, as two of the biggest companies in the world, Google and
Microsoft, battle for prevalence of the web search space. Differences in search
engine popularity can be explained by their effectiveness or other factors,
such as familiarity with the most popular first engine, peer imitation, or
force of habit. In this work we present a thorough analysis of the affinity of
the two major search engines, Google and Bing, along with DuckDuckGo, which
goes to great lengths to emphasize its privacy-friendly credentials. To do so,
we collected search results using a comprehensive set of 300 unique queries for
two time periods in 2016 and 2019, and developed a new similarity metric that
leverages both the content and the ranking of search responses. We evaluated
the characteristics of the metric against other metrics and approaches that
have been proposed in the literature, and used it to (1) investigate the
similarities of search engine results, (2) the evolution of their affinity over
time, (3) what aspects of the results influence similarity, and (4) how the
metric differs over different kinds of search services. We found that Google
stands apart, but Bing and DuckDuckGo are largely indistinguishable from each
other.Comment: Shorter version of this paper was accepted in the 21st International
Conference on Web Information Systems Engineering (WISE 2020). The final
authenticated version is available online at
https://doi.org/10.1007/978-3-030-62008-0_
Grids and the Virtual Observatory
We consider several projects from astronomy that benefit from the Grid paradigm and
associated technology, many of which involve either massive datasets or the federation
of multiple datasets. We cover image computation (mosaicking, multi-wavelength
images, and synoptic surveys); database computation (representation through XML,
data mining, and visualization); and semantic interoperability (publishing, ontologies,
directories, and service descriptions)
A Method for the Automated, Reliable Retrieval of Publication-Citation Records
BACKGROUND: Publication records and citation indices often are used to evaluate academic performance. For this reason, obtaining or computing them accurately is important. This can be difficult, largely due to a lack of complete knowledge of an individual's publication list and/or lack of time available to manually obtain or construct the publication-citation record. While online publication search engines have somewhat addressed these problems, using raw search results can yield inaccurate estimates of publication-citation records and citation indices. METHODOLOGY: In this paper, we present a new, automated method that produces estimates of an individual's publication-citation record from an individual's name and a set of domain-specific vocabulary that may occur in the individual's publication titles. Because this vocabulary can be harvested directly from a research web page or online (partial) publication list, our method delivers an easy way to obtain estimates of a publication-citation record and the relevant citation indices. Our method works by applying a series of stringent name and content filters to the raw publication search results returned by an online publication search engine. In this paper, our method is run using Google Scholar, but the underlying filters can be easily applied to any existing publication search engine. When compared against a manually constructed data set of individuals and their publication-citation records, our method provides significant improvements over raw search results. The estimated publication-citation records returned by our method have an average sensitivity of 98% and specificity of 72% (in contrast to raw search result specificity of less than 10%). When citation indices are computed using these records, the estimated indices are within of the true value 10%, compared to raw search results which have overestimates of, on average, 75%. CONCLUSIONS: These results confirm that our method provides significantly improved estimates over raw search results, and these can either be used directly for large-scale (departmental or university) analysis or further refined manually to quickly give accurate publication-citation records
Towards web supported identification of top affiliations from scholarly papers
Frequent successful publications by specific institutions are indicators for identifying outstanding centres of research. This institution data are present in scholarly papers as the authors‟ affilations – often in very heterogeneous variants for the same institution across publications. Thus, matching is needed to identify the denoted real world institutions and locations. We introduce an approximate string metric that handles acronyms and abbreviations. Our URL overlap similarity measure is based on comparing the result sets of web searches. Evaluations on affiliation strings of a conference prove better results than soft tf/idf, trigram, and levenshtein. Incorporating the aligned affiliations we present top institutions and countries for the last 10 years of SIGMOD
- …