-
Noever, D.; Ciolino, M.: ¬The Turing deception (2022)
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- Source
- https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
-
Herb, U.; Beucke, D.: ¬Die Zukunft der Impact-Messung : Social Media, Nutzung und Zitate im World Wide Web (2013)
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- Content
- Vgl. unter: https://www.leibniz-science20.de%2Fforschung%2Fprojekte%2Faltmetrics-in-verschiedenen-wissenschaftsdisziplinen%2F&ei=2jTgVaaXGcK4Udj1qdgB&usg=AFQjCNFOPdONj4RKBDf9YDJOLuz3lkGYlg&sig2=5YI3KWIGxBmk5_kv0P_8iQ.
-
Shala, E.: ¬Die Autonomie des Menschen und der Maschine : gegenwärtige Definitionen von Autonomie zwischen philosophischem Hintergrund und technologischer Umsetzbarkeit (2014)
0.26
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- Footnote
- Vgl. unter: https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=2ahUKEwizweHljdbcAhVS16QKHXcFD9QQFjABegQICRAB&url=https%3A%2F%2Fwww.researchgate.net%2Fpublication%2F271200105_Die_Autonomie_des_Menschen_und_der_Maschine_-_gegenwartige_Definitionen_von_Autonomie_zwischen_philosophischem_Hintergrund_und_technologischer_Umsetzbarkeit_Redigierte_Version_der_Magisterarbeit_Karls&usg=AOvVaw06orrdJmFF2xbCCp_hL26q.
-
Piros, A.: Az ETO-jelzetek automatikus interpretálásának és elemzésének kérdései (2018)
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- Content
- Vgl. auch: New automatic interpreter for complex UDC numbers. Unter: <https%3A%2F%2Fudcc.org%2Ffiles%2FAttilaPiros_EC_36-37_2014-2015.pdf&usg=AOvVaw3kc9CwDDCWP7aArpfjrs5b>
-
Fan, W.; Gordon, M.D.; Pathak, P.: ¬A generic ranking function discovery framework by genetic programming for information retrieval (2004)
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- Abstract
- Ranking functions play a substantial role in the performance of information retrieval (IR) systems and search engines. Although there are many ranking functions available in the IR literature, various empirical evaluation studies show that ranking functions do not perform consistently well across different contexts (queries, collections, users). Moreover, it is often difficult and very expensive for human beings to design optimal ranking functions that work well in all these contexts. In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.
- Source
- Information processing and management. 40(2004) no.4, S.587-602
-
Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
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- Date
- 25. 8.2005 17:42:22
- Source
- Library and information research news. 24(2000) no.77, S.30-34
-
Ro, J.S.: ¬An evaluation of the applicability of ranking algorithms to improve the effectiveness of full-text retrieval : 1. On the effectiveness of full-text retrieval (1988)
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- Source
- Journal of the American Society for Information Science. 39(1988), S.73-78
-
Efthimiadis, E.N.: User choices : a new yardstick for the evaluation of ranking algorithms for interactive query expansion (1995)
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- Abstract
- The performance of 8 ranking algorithms was evaluated with respect to their effectiveness in ranking terms for query expansion. The evaluation was conducted within an investigation of interactive query expansion and relevance feedback in a real operational environment. Focuses on the identification of algorithms that most effectively take cognizance of user preferences. user choices (i.e. the terms selected by the searchers for the query expansion search) provided the yardstick for the evaluation of the 8 ranking algorithms. This methodology introduces a user oriented approach in evaluating ranking algorithms for query expansion in contrast to the standard, system oriented approaches. Similarities in the performance of the 8 algorithms and the ways these algorithms rank terms were the main focus of this evaluation. The findings demonstrate that the r-lohi, wpq, enim, and porter algorithms have similar performance in bringing good terms to the top of a ranked list of terms for query expansion. However, further evaluation of the algorithms in different (e.g. full text) environments is needed before these results can be generalized beyond the context of the present study
- Date
- 22. 2.1996 13:14:10
- Source
- Information processing and management. 31(1995) no.4, S.605-620
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Lewandowski, D.; Spree, U.: Ranking of Wikipedia articles in search engines revisited : fair ranking for reasonable quality? (2011)
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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
- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.1, S.117-132
-
Qin, T.; Zhang, X.-D.; Tsai, M.-F.; Wang, D.-S.; Liu, T.-Y.; Li, H.: Query-level loss functions for information retrieval (2008)
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- Abstract
- Many machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieval. However, since originally the methods were not developed for this task, their loss functions do not directly link to the criteria used in the evaluation of ranking. Specifically, the loss functions are defined on the level of documents or document pairs, in contrast to the fact that the evaluation criteria are defined on the level of queries. Therefore, minimizing the loss functions does not necessarily imply enhancing ranking performances. To solve this problem, we propose using query-level loss functions in learning of ranking functions. We discuss the basic properties that a query-level loss function should have and propose a query-level loss function based on the cosine similarity between a ranking list and the corresponding ground truth. We further design a coordinate descent algorithm, referred to as RankCosine, which utilizes the proposed loss function to create a generalized additive ranking model. We also discuss whether the loss functions of existing ranking algorithms can be extended to query-level. Experimental results on the datasets of TREC web track, OHSUMED, and a commercial web search engine show that with the use of the proposed query-level loss function we can significantly improve ranking accuracies. Furthermore, we found that it is difficult to extend the document-level loss functions to query-level loss functions.
- Source
- Information processing and management. 44(2008) no.2, S.838-855
-
Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015)
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- Abstract
- Purpose In a system-based approach, replicating the web would require large test collections, and judging the relevancy of all documents per topic in creating relevance judgment through human assessors is infeasible. Due to the large amount of documents that requires judgment, there are possible errors introduced by human assessors because of disagreements. The paper aims to discuss these issues. Design/methodology/approach This study explores exponential variation and document ranking methods that generate a reliable set of relevance judgments (pseudo relevance judgments) to reduce human efforts. These methods overcome problems with large amounts of documents for judgment while avoiding human disagreement errors during the judgment process. This study utilizes two key factors: number of occurrences of each document per topic from all the system runs; and document rankings to generate the alternate methods. Findings The effectiveness of the proposed method is evaluated using the correlation coefficient of ranked systems using mean average precision scores between the original Text REtrieval Conference (TREC) relevance judgments and pseudo relevance judgments. The results suggest that the proposed document ranking method with a pool depth of 100 could be a reliable alternative to reduce human effort and disagreement errors involved in generating TREC-like relevance judgments. Originality/value Simple methods proposed in this study show improvement in the correlation coefficient in generating alternate relevance judgment without human assessors while contributing to information retrieval evaluation.
- Date
- 20. 1.2015 18:30:22
18. 9.2018 18:22:56
- Source
- Aslib journal of information management. 67(2015) no.6, S.700-714
-
Fricke, M.: Measuring recall (1998)
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- Abstract
- Recall, the proortion of the relevant documents retrieved, is a key indicator of the performance of an information retrieval system. With large information systems, like the WWW, recal is almost impossible to measure or estimate by all standard techniques. Proposes an 'needle hiding' technique for measuring recall under these circumstances. Shows that ranking by relative recall does not have to be isomorphic to ranking by recall and hence the use of relative recall for comparative evaluation might not be entirely sound
- Source
- Journal of information science. 24(1998) no.6, S.409-417
-
Neunzert, H.: Mathematische Modellierung : ein "curriculum vitae" (2012)
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- Content
- Vortrag auf der Tagung "Geschichte und Modellierung", Jena, 3. Februar 2012. Vgl. unter: http://www.fmi.uni-jena.de/Fakult%C3%A4t/Institute+und+Abteilungen/Abteilung+f%C3%BCr+Didaktik/Kolloquien.html?highlight=neunzert.
-
Mayr, P.: Information Retrieval-Mehrwertdienste für Digitale Bibliotheken: : Crosskonkordanzen und Bradfordizing (2010)
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- Abstract
- In dieser Arbeit werden zwei Mehrwertdienste für Suchsysteme vorgestellt, die typische Probleme bei der Recherche nach wissenschaftlicher Literatur behandeln können. Die beiden Mehrwertdienste semantische Heterogenitätsbehandlung am Beispiel Crosskonkordanzen und Re-Ranking auf Basis von Bradfordizing, die in unterschiedlichen Phasen der Suche zum Einsatz kommen, werden in diesem Buch ausführlich beschrieben und evaluiert. Für die Tests wurden Fragestellungen und Daten aus zwei Evaluationsprojekten (CLEF und KoMoHe) verwendet. Die intellektuell bewerteten Dokumente stammen aus insgesamt sieben Fachdatenbanken der Fächer Sozialwissenschaften, Politikwissenschaft, Wirtschaftswissenschaften, Psychologie und Medizin. Die Ergebnisse dieser Arbeit sind in das GESIS-Projekt IRM eingeflossen.
- RSWK
- Dokumentationssprache / Heterogenität / Information Retrieval / Ranking / Evaluation
- Subject
- Dokumentationssprache / Heterogenität / Information Retrieval / Ranking / Evaluation
-
Carpineto, C.; Romano, G.: Order-theoretical ranking (2000)
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- Abstract
- Current best-match ranking (BMR) systems perform well but cannot handle word mismatch between a query and a document. The best known alternative ranking method, hierarchical clustering-based ranking (HCR), seems to be more robust than BMR with respect to this problem, but it is hampered by theoretical and practical limitations. We present an approach to document ranking that explicitly addresses the word mismatch problem by exploiting interdocument similarity information in a novel way. Document ranking is seen as a query-document transformation driven by a conceptual representation of the whole document collection, into which the query is merged. Our approach is nased on the theory of concept (or Galois) lattices, which, er argue, provides a powerful, well-founded, and conputationally-tractable framework to model the space in which documents and query are represented and to compute such a transformation. We compared information retrieval using concept lattice-based ranking (CLR) to BMR and HCR. The results showed that HCR was outperformed by CLR as well as BMR, and suggested that, of the two best methods, BMR achieved better performance than CLR on the whole document set, whereas CLR compared more favorably when only the first retrieved documents were used for evaluation. We also evaluated the three methods' specific ability to rank documents that did not match the query, in which case the speriority of CLR over BMR and HCR was apparent
- Source
- Journal of the American Society for Information Science. 51(2000) no.7, S.587-601
-
Stets, P.: Ranking-Algorithmen im Information Retrieval (1994)
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-
Wechsler, M.; Schäuble, P.: ¬The probability ranking principle revisited (2000)
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- Source
- Information retrieval. 3(2000), S.217-227
-
Wiggers, G.; Verberne, S.; Loon, W. van; Zwenne, G.-J.: Bibliometric-enhanced legal information retrieval : combining usage and citations as flavors of impact relevance (2023)
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- Abstract
- Bibliometric-enhanced information retrieval uses bibliometrics (e.g., citations) to improve ranking algorithms. Using a data-driven approach, this article describes the development of a bibliometric-enhanced ranking algorithm for legal information retrieval, and the evaluation thereof. We statistically analyze the correlation between usage of documents and citations over time, using data from a commercial legal search engine. We then propose a bibliometric boost function that combines usage of documents with citation counts. The core of this function is an impact variable based on usage and citations that increases in influence as citations and usage counts become more reliable over time. We evaluate our ranking function by comparing search sessions before and after the introduction of the new ranking in the search engine. Using a cost model applied to 129,571 sessions before and 143,864 sessions after the intervention, we show that our bibliometric-enhanced ranking algorithm reduces the time of a search session of legal professionals by 2 to 3% on average for use cases other than known-item retrieval or updating behavior. Given the high hourly tariff of legal professionals and the limited time they can spend on research, this is expected to lead to increased efficiency, especially for users with extremely long search sessions.
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.8, S.1010-1025
-
Kanaeva, Z.: Ranking: Google und CiteSeer (2005)
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- Abstract
- Im Rahmen des klassischen Information Retrieval wurden verschiedene Verfahren für das Ranking sowie die Suche in einer homogenen strukturlosen Dokumentenmenge entwickelt. Die Erfolge der Suchmaschine Google haben gezeigt dass die Suche in einer zwar inhomogenen aber zusammenhängenden Dokumentenmenge wie dem Internet unter Berücksichtigung der Dokumentenverbindungen (Links) sehr effektiv sein kann. Unter den von der Suchmaschine Google realisierten Konzepten ist ein Verfahren zum Ranking von Suchergebnissen (PageRank), das in diesem Artikel kurz erklärt wird. Darüber hinaus wird auf die Konzepte eines Systems namens CiteSeer eingegangen, welches automatisch bibliographische Angaben indexiert (engl. Autonomous Citation Indexing, ACI). Letzteres erzeugt aus einer Menge von nicht vernetzten wissenschaftlichen Dokumenten eine zusammenhängende Dokumentenmenge und ermöglicht den Einsatz von Banking-Verfahren, die auf den von Google genutzten Verfahren basieren.
- Date
- 20. 3.2005 16:23:22
- Source
- Information - Wissenschaft und Praxis. 56(2005) H.2, S.87-92
-
Harman, D.: Ranking algorithms (1992)
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- Abstract
- Presents both a summary of past research done in the development of ranking algorithms and detailed instructions on implementing a ranking type of retrieval system. This type of retrieval system takes as input a natural language query without Boolean syntax and produces a list of records that 'answer' the query, with the records ranked in order of likely relevance. Ranking retrieval systems are particularly appropriate for end-users
- Source
- Information retrieval: data structures and algorithms. Ed.: W.B. Frakes u. R. Baeza-Yates
