Search (27 results, page 1 of 2)

  • × theme_ss:"Suchmaschinen"
  • × type_ss:"a"
  • × year_i:[2010 TO 2020}
  1. Lewandowski, D.; Spree, U.: Ranking of Wikipedia articles in search engines revisited : fair ranking for reasonable quality? (2011) 0.02 
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    Abstract
    This paper aims to review the fiercely discussed question of whether the ranking of Wikipedia articles in search engines is justified by the quality of the articles. After an overview of current research on information quality in Wikipedia, a summary of the extended discussion on the quality of encyclopedic entries in general is given. On this basis, a heuristic method for evaluating Wikipedia entries is developed and applied to Wikipedia articles that scored highly in a search engine retrieval effectiveness test and compared with the relevance judgment of jurors. In all search engines tested, Wikipedia results are unanimously judged better by the jurors than other results on the corresponding results position. Relevance judgments often roughly correspond with the results from the heuristic evaluation. Cases in which high relevance judgments are not in accordance with the comparatively low score from the heuristic evaluation are interpreted as an indicator of a high degree of trust in Wikipedia. One of the systemic shortcomings of Wikipedia lies in its necessarily incoherent user model. A further tuning of the suggested criteria catalog, for instance, the different weighing of the supplied criteria, could serve as a starting point for a user model differentiated evaluation of Wikipedia articles. Approved methods of quality evaluation of reference works are applied to Wikipedia articles and integrated with the question of search engine evaluation.
    Date
    30. 9.2012 19:27:22
  2. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.02 
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    Abstract
    Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  3. Shapira, B.; Zabar, B.: Personalized search : integrating collaboration and social networks (2011) 0.01 
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    Abstract
    Despite improvements in their capabilities, search engines still fail to provide users with only relevant results. One reason is that most search engines implement a "one size fits all" approach that ignores personal preferences when retrieving the results of a user's query. Recent studies (Smyth, 2010) have elaborated the importance of personalizing search results and have proposed integrating recommender system methods for enhancing results using contextual and extrinsic information that might indicate the user's actual needs. In this article, we review recommender system methods used for personalizing and improving search results and examine the effect of two such methods that are merged for this purpose. One method is based on collaborative users' knowledge; the second integrates information from the user's social network. We propose new methods for collaborative-and social-based search and demonstrate that each of these methods, when separately applied, produce more accurate search results than does a purely keyword-based search engine (referred to as "standard search engine"), where the social search engine is more accurate than is the collaborative one. However, separately applied, these methods do not produce a sufficient number of results (low coverage). Nevertheless, merging these methods with those implemented by standard search engines overcomes the low-coverage problem and produces personalized results for users that display significantly more accurate results while also providing sufficient coverage than do standard search engines. The improvement, however, is significant only for topics for which the diversity of terms used for queries among users is low.
  4. Souza, J.; Carvalho, A.; Cristo, M.; Moura, E.; Calado, P.; Chirita, P.-A.; Nejdl, W.: Using site-level connections to estimate link confidence (2012) 0.01 
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    Abstract
    Search engines are essential tools for web users today. They rely on a large number of features to compute the rank of search results for each given query. The estimated reputation of pages is among the effective features available for search engine designers, probably being adopted by most current commercial search engines. Page reputation is estimated by analyzing the linkage relationships between pages. This information is used by link analysis algorithms as a query-independent feature, to be taken into account when computing the rank of the results. Unfortunately, several types of links found on the web may damage the estimated page reputation and thus cause a negative effect on the quality of search results. This work studies alternatives to reduce the negative impact of such noisy links. More specifically, the authors propose and evaluate new methods that deal with noisy links, considering scenarios where the reputation of pages is computed using the PageRank algorithm. They show, through experiments with real web content, that their methods achieve significant improvements when compared to previous solutions proposed in the literature.
  5. Kruschwitz, U.; Lungley, D.; Albakour, M-D.; Song, D.: Deriving query suggestions for site search (2013) 0.01 
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    Abstract
    Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files.
  6. Lewandowski, D.: Query understanding (2011) 0.01 
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    Date
    18. 9.2018 18:22:18
  7. Bensman, S.J.: Eugene Garfield, Francis Narin, and PageRank : the theoretical bases of the Google search engine (2013) 0.01 
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    Date
    17.12.2013 11:02:22
  8. Schaat, S.: Von der automatisierten Manipulation zur Manipulation der Automatisierung (2019) 0.01 
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    Date
    19. 2.2019 17:22:00
  9. Das, A.; Jain, A.: Indexing the World Wide Web : the journey so far (2012) 0.01 
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    Abstract
    In this chapter, the authors describe the key indexing components of today's web search engines. As the World Wide Web has grown, the systems and methods for indexing have changed significantly. The authors present the data structures used, the features extracted, the infrastructure needed, and the options available for designing a brand new search engine. Techniques are highlighted that improve relevance of results, discuss trade-offs to best utilize machine resources, and cover distributed processing concepts in this context. In particular, the authors delve into the topics of indexing phrases instead of terms, storage in memory vs. on disk, and data partitioning. Some thoughts on information organization for the newly emerging data-forms conclude the chapter.
  10. Jindal, V.; Bawa, S.; Batra, S.: ¬A review of ranking approaches for semantic search on Web (2014) 0.01 
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    Abstract
    With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.
  11. Wichor, M.B.: Variation in number of hits for complex searches in Google Scholar (2016) 0.01 
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    Abstract
    Google Scholar is often used to search for medical literature. Numbers of results reported by Google Scholar outperform the numbers reported by traditional databases. How reliable are these numbers? Why are often not all available 1,000 references shown? Methods: For several complex search strategies used in systematic review projects, the number of citations and the total number of versions were calculated. Several search strategies were followed over a two-year period, registering fluctuations in reported search results. Results: Changes in numbers of reported search results varied enormously between search strategies and dates. Theories for calculations of the reported and shown number of hits were not proved. Conclusions: The number of hits reported in Google Scholar is an unreliable measure. Therefore, its repeatability is problematic, at least when equal results are needed.
  12. Kucukyilmaz, T.; Cambazoglu, B.B.; Aykanat, C.; Baeza-Yates, R.: ¬A machine learning approach for result caching in web search engines (2017) 0.01 
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    Abstract
    A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache can be directly served by the result cache, eliminating the need to process the query using costly computing resources. Although other performance metrics are possible, the main performance metric for evaluating the success of a result cache is hit rate. In this work, we present a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning approach to static, dynamic, and static-dynamic caching. Compared to the previous methods in the literature, the proposed approach improves the hit rate of the result cache up to 0.66%, which corresponds to 9.60% of the potential room for improvement.
  13. Bhansali, D.; Desai, H.; Deulkar, K.: ¬A study of different ranking approaches for semantic search (2015) 0.01 
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    Abstract
    Search Engines have become an integral part of our day to day life. Our reliance on search engines increases with every passing day. With the amount of data available on Internet increasing exponentially, it becomes important to develop new methods and tools that help to return results relevant to the queries and reduce the time spent on searching. The results should be diverse but at the same time should return results focused on the queries asked. Relation Based Page Rank [4] algorithms are considered to be the next frontier in improvement of Semantic Web Search. The probability of finding relevance in the search results as posited by the user while entering the query is used to measure the relevance. However, its application is limited by the complexity of determining relation between the terms and assigning explicit meaning to each term. Trust Rank is one of the most widely used ranking algorithms for semantic web search. Few other ranking algorithms like HITS algorithm, PageRank algorithm are also used for Semantic Web Searching. In this paper, we will provide a comparison of few ranking approaches.
  14. Rieh, S.Y.; Kim, Y.-M.; Markey, K.: Amount of invested mental effort (AIME) in online searching (2012) 0.01 
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    Abstract
    This research investigates how people's perceptions of information retrieval (IR) systems, their perceptions of search tasks, and their perceptions of self-efficacy influence the amount of invested mental effort (AIME) they put into using two different IR systems: a Web search engine and a library system. It also explores the impact of mental effort on an end user's search experience. To assess AIME in online searching, two experiments were conducted using these methods: Experiment 1 relied on self-reports and Experiment 2 employed the dual-task technique. In both experiments, data were collected through search transaction logs, a pre-search background questionnaire, a post-search questionnaire and an interview. Important findings are these: (1) subjects invested greater mental effort searching a library system than searching the Web; (2) subjects put little effort into Web searching because of their high sense of self-efficacy in their searching ability and their perception of the easiness of the Web; (3) subjects did not recognize that putting mental effort into searching was something needed to improve the search results; and (4) data collected from multiple sources proved to be effective for assessing mental effort in online searching.
  15. Fluhr, C.: Crosslingual access to photo databases (2012) 0.00 
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    Date
    17. 4.2012 14:25:22
  16. Chen, L.-C.: Next generation search engine for the result clustering technology (2012) 0.00 
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    Date
    17. 4.2012 15:22:11
  17. Bouidghaghen, O.; Tamine, L.: Spatio-temporal based personalization for mobile search (2012) 0.00 
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  18. Lewandowski, D.: ¬Die Macht der Suchmaschinen und ihr Einfluss auf unsere Entscheidungen (2014) 0.00 
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  19. Huvila, I.: Affective capitalism of knowing and the society of search engine (2016) 0.00 
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  20. Chaudiron, S.; Ihadjadene, M.: Studying Web search engines from a user perspective : key concepts and main approaches (2012) 0.00 
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