-
Jindal, V.; Bawa, S.; Batra, S.: ¬A review of ranking approaches for semantic search on Web (2014)
0.09
0.088055015 = product of:
0.14675835 = sum of:
0.048630223 = weight(_text_:retrieval in 2799) [ClassicSimilarity], result of:
0.048630223 = score(doc=2799,freq=6.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.34732026 = fieldWeight in 2799, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=2799)
0.075019486 = weight(_text_:semantic in 2799) [ClassicSimilarity], result of:
0.075019486 = score(doc=2799,freq=4.0), product of:
0.19245663 = queryWeight, product of:
4.1578603 = idf(docFreq=1879, maxDocs=44218)
0.04628742 = queryNorm
0.38979942 = fieldWeight in 2799, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.1578603 = idf(docFreq=1879, maxDocs=44218)
0.046875 = fieldNorm(doc=2799)
0.023108633 = product of:
0.046217266 = sum of:
0.046217266 = weight(_text_:web in 2799) [ClassicSimilarity], result of:
0.046217266 = score(doc=2799,freq=4.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.3059541 = fieldWeight in 2799, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.046875 = fieldNorm(doc=2799)
0.5 = coord(1/2)
0.6 = coord(3/5)
- 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.
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Bhansali, D.; Desai, H.; Deulkar, K.: ¬A study of different ranking approaches for semantic search (2015)
0.08
0.08123621 = product of:
0.13539368 = sum of:
0.023397226 = weight(_text_:retrieval in 2696) [ClassicSimilarity], result of:
0.023397226 = score(doc=2696,freq=2.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.16710453 = fieldWeight in 2696, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=2696)
0.0884113 = weight(_text_:semantic in 2696) [ClassicSimilarity], result of:
0.0884113 = score(doc=2696,freq=8.0), product of:
0.19245663 = queryWeight, product of:
4.1578603 = idf(docFreq=1879, maxDocs=44218)
0.04628742 = queryNorm
0.45938298 = fieldWeight in 2696, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
4.1578603 = idf(docFreq=1879, maxDocs=44218)
0.0390625 = fieldNorm(doc=2696)
0.02358515 = product of:
0.0471703 = sum of:
0.0471703 = weight(_text_:web in 2696) [ClassicSimilarity], result of:
0.0471703 = score(doc=2696,freq=6.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.3122631 = fieldWeight in 2696, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.0390625 = fieldNorm(doc=2696)
0.5 = coord(1/2)
0.6 = coord(3/5)
- 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.
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Dominich, S.; Skrop, A.: PageRank and interaction information retrieval (2005)
0.04
0.035533555 = product of:
0.08883388 = sum of:
0.056153342 = weight(_text_:retrieval in 3268) [ClassicSimilarity], result of:
0.056153342 = score(doc=3268,freq=8.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.40105087 = fieldWeight in 3268, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=3268)
0.03268054 = product of:
0.06536108 = sum of:
0.06536108 = weight(_text_:web in 3268) [ClassicSimilarity], result of:
0.06536108 = score(doc=3268,freq=8.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.43268442 = fieldWeight in 3268, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.046875 = fieldNorm(doc=3268)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- The PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the Interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady-state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (1**2 R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based an dynamic systems. In the present paper, a different Interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.
-
Meghabghab, G.: Google's Web page ranking applied to different topological Web graph structures (2001)
0.04
0.03548062 = product of:
0.088701546 = sum of:
0.023397226 = weight(_text_:retrieval in 6028) [ClassicSimilarity], result of:
0.023397226 = score(doc=6028,freq=2.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.16710453 = fieldWeight in 6028, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=6028)
0.065304324 = product of:
0.13060865 = sum of:
0.13060865 = weight(_text_:web in 6028) [ClassicSimilarity], result of:
0.13060865 = score(doc=6028,freq=46.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.86461735 = fieldWeight in 6028, product of:
6.78233 = tf(freq=46.0), with freq of:
46.0 = termFreq=46.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.0390625 = fieldNorm(doc=6028)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- This research is part of the ongoing study to better understand web page ranking on the web. It looks at a web page as a graph structure or a web graph, and tries to classify different web graphs in the new coordinate space: (out-degree, in-degree). The out-degree coordinate od is defined as the number of outgoing web pages from a given web page. The in-degree id coordinate is the number of web pages that point to a given web page. In this new coordinate space a metric is built to classify how close or far different web graphs are. Google's web ranking algorithm (Brin & Page, 1998) on ranking web pages is applied in this new coordinate space. The results of the algorithm has been modified to fit different topological web graph structures. Also the algorithm was not successful in the case of general web graphs and new ranking web algorithms have to be considered. This study does not look at enhancing web ranking by adding any contextual information. It only considers web links as a source to web page ranking. The author believes that understanding the underlying web page as a graph will help design better ranking web algorithms, enhance retrieval and web performance, and recommends using graphs as a part of visual aid for browsing engine designers
-
Thelwall, M.; Vaughan, L.: New versions of PageRank employing alternative Web document models (2004)
0.03
0.029717578 = product of:
0.07429394 = sum of:
0.028076671 = weight(_text_:retrieval in 674) [ClassicSimilarity], result of:
0.028076671 = score(doc=674,freq=2.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.20052543 = fieldWeight in 674, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=674)
0.046217266 = product of:
0.09243453 = sum of:
0.09243453 = weight(_text_:web in 674) [ClassicSimilarity], result of:
0.09243453 = score(doc=674,freq=16.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.6119082 = fieldWeight in 674, product of:
4.0 = tf(freq=16.0), with freq of:
16.0 = termFreq=16.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.046875 = fieldNorm(doc=674)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- Introduces several new versions of PageRank (the link based Web page ranking algorithm), based on an information science perspective on the concept of the Web document. Although the Web page is the typical indivisible unit of information in search engine results and most Web information retrieval algorithms, other research has suggested that aggregating pages based on directories and domains gives promising alternatives, particularly when Web links are the object of study. The new algorithms introduced based on these alternatives were used to rank four sets of Web pages. The ranking results were compared with human subjects' rankings. The results of the tests were somewhat inconclusive: the new approach worked well for the set that includes pages from different Web sites; however, it does not work well in ranking pages that are from the same site. It seems that the new algorithms may be effective for some tasks but not for others, especially when only low numbers of links are involved or the pages to be ranked are from the same site or directory.
-
Ding, Y.; Chowdhury, G.; Foo, S.: Organsising keywords in a Web search environment : a methodology based on co-word analysis (2000)
0.03
0.028695542 = product of:
0.071738854 = sum of:
0.048630223 = weight(_text_:retrieval in 105) [ClassicSimilarity], result of:
0.048630223 = score(doc=105,freq=6.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.34732026 = fieldWeight in 105, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=105)
0.023108633 = product of:
0.046217266 = sum of:
0.046217266 = weight(_text_:web in 105) [ClassicSimilarity], result of:
0.046217266 = score(doc=105,freq=4.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.3059541 = fieldWeight in 105, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.046875 = fieldNorm(doc=105)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- The rapid development of the Internet and World Wide Web has caused some critical problem for information retrieval. Researchers have made several attempts to solve these problems. Thesauri and subject heading lists as traditional information retrieval tools have been criticised for their efficiency to tackle these newly emerging problems. This paper proposes an information retrieval tool generated by cocitation analysis, comprising keyword clusters with relationships based on the co-occurrences of keywords in the literature. Such a tool can play the role of an associative thesaurus that can provide information about the keywords in a domain that might be useful for information searching and query expansion
-
Thelwall, M.: Can Google's PageRank be used to find the most important academic Web pages? (2003)
0.03
0.025845854 = product of:
0.06461463 = sum of:
0.028076671 = weight(_text_:retrieval in 4457) [ClassicSimilarity], result of:
0.028076671 = score(doc=4457,freq=2.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.20052543 = fieldWeight in 4457, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=4457)
0.03653796 = product of:
0.07307592 = sum of:
0.07307592 = weight(_text_:web in 4457) [ClassicSimilarity], result of:
0.07307592 = score(doc=4457,freq=10.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.48375595 = fieldWeight in 4457, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.046875 = fieldNorm(doc=4457)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- Google's PageRank is an influential algorithm that uses a model of Web use that is dominated by its link structure in order to rank pages by their estimated value to the Web community. This paper reports on the outcome of applying the algorithm to the Web sites of three national university systems in order to test whether it is capable of identifying the most important Web pages. The results are also compared with simple inlink counts. It was discovered that the highest inlinked pages do not always have the highest PageRank, indicating that the two metrics are genuinely different, even for the top pages. More significantly, however, internal links dominated external links for the high ranks in either method and superficial reasons accounted for high scores in both cases. It is concluded that PageRank is not useful for identifying the top pages in a site and that it must be combined with a powerful text matching techniques in order to get the quality of information retrieval results provided by Google.
-
Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005)
0.02
0.022551542 = product of:
0.056378856 = sum of:
0.028076671 = weight(_text_:retrieval in 3455) [ClassicSimilarity], result of:
0.028076671 = score(doc=3455,freq=2.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.20052543 = fieldWeight in 3455, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=3455)
0.028302183 = product of:
0.056604367 = sum of:
0.056604367 = weight(_text_:web in 3455) [ClassicSimilarity], result of:
0.056604367 = score(doc=3455,freq=6.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.37471575 = fieldWeight in 3455, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.046875 = fieldNorm(doc=3455)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- Web-based search engines such as Google and NorthernLight return documents that are relevant to a user query, not answers to user questions. We have developed an architecture that augments existing search engines so that they support natural language question answering. The process entails five steps: query modulation, document retrieval, passage extraction, phrase extraction, and answer ranking. In this article, we describe some probabilistic approaches to the last three of these stages. We show how our techniques apply to a number of existing search engines, and we also present results contrasting three different methods for question answering. Our algorithm, probabilistic phrase reranking (PPR), uses proximity and question type features and achieves a total reciprocal document rank of .20 an the TREC8 corpus. Our techniques have been implemented as a Web-accessible system, called NSIR.
-
Kanaeva, Z.: Ranking: Google und CiteSeer (2005)
0.02
0.021882275 = product of:
0.054705687 = sum of:
0.032756116 = weight(_text_:retrieval in 3276) [ClassicSimilarity], result of:
0.032756116 = score(doc=3276,freq=2.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.23394634 = fieldWeight in 3276, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0546875 = fieldNorm(doc=3276)
0.021949572 = product of:
0.043899145 = sum of:
0.043899145 = weight(_text_:22 in 3276) [ClassicSimilarity], result of:
0.043899145 = score(doc=3276,freq=2.0), product of:
0.16209066 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04628742 = queryNorm
0.2708308 = fieldWeight in 3276, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0546875 = fieldNorm(doc=3276)
0.5 = coord(1/2)
0.4 = coord(2/5)
- 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
-
Agosti, M.; Pretto, L.: ¬A theoretical study of a generalized version of kleinberg's HITS algorithm (2005)
0.02
0.020252405 = product of:
0.050631013 = sum of:
0.023397226 = weight(_text_:retrieval in 4) [ClassicSimilarity], result of:
0.023397226 = score(doc=4,freq=2.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.16710453 = fieldWeight in 4, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=4)
0.027233787 = product of:
0.054467574 = sum of:
0.054467574 = weight(_text_:web in 4) [ClassicSimilarity], result of:
0.054467574 = score(doc=4,freq=8.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.36057037 = fieldWeight in 4, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.0390625 = fieldNorm(doc=4)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- Kleinberg's HITS (Hyperlink-Induced Topic Search) algorithm (Kleinberg 1999), which was originally developed in a Web context, tries to infer the authoritativeness of a Web page in relation to a specific query using the structure of a subgraph of the Web graph, which is obtained considering this specific query. Recent applications of this algorithm in contexts far removed from that of Web searching (Bacchin, Ferro and Melucci 2002, Ng et al. 2001) inspired us to study the algorithm in the abstract, independently of its particular applications, trying to mathematically illuminate its behaviour. In the present paper we detail this theoretical analysis. The original work starts from the definition of a revised and more general version of the algorithm, which includes the classic one as a particular case. We perform an analysis of the structure of two particular matrices, essential to studying the behaviour of the algorithm, and we prove the convergence of the algorithm in the most general case, finding the analytic expression of the vectors to which it converges. Then we study the symmetry of the algorithm and prove the equivalence between the existence of symmetry and the independence from the order of execution of some basic operations on initial vectors. Finally, we expound some interesting consequences of our theoretical results.
- Source
- Advances in mathematical/formal methods in information retrieval. 8(2005) no.2 , S.219-243
-
Chen, Z.; Meng, X.; Fowler, R.H.; Zhu, B.: Real-time adaptive feature and document learning for Web search (2001)
0.02
0.017061768 = product of:
0.04265442 = sum of:
0.023397226 = weight(_text_:retrieval in 5209) [ClassicSimilarity], result of:
0.023397226 = score(doc=5209,freq=2.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.16710453 = fieldWeight in 5209, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=5209)
0.019257195 = product of:
0.03851439 = sum of:
0.03851439 = weight(_text_:web in 5209) [ClassicSimilarity], result of:
0.03851439 = score(doc=5209,freq=4.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.25496176 = fieldWeight in 5209, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.0390625 = fieldNorm(doc=5209)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- Chen et alia report on the design of FEATURES, a web search engine with adaptive features based on minimal relevance feedback. Rather than developing user profiles from previous searcher activity either at the server or client location, or updating indexes after search completion, FEATURES allows for index and user characterization files to be updated during query modification on retrieval from a general purpose search engine. Indexing terms relevant to a query are defined as the union of all terms assigned to documents retrieved by the initial search run and are used to build a vector space model on this retrieved set. The top ten weighted terms are presented to the user for a relevant non-relevant choice which is used to modify the term weights. Documents are chosen if their summed term weights are greater than some threshold. A user evaluation of the top ten ranked documents as non-relevant will decrease these term weights and a positive judgement will increase them. A new ordering of the retrieved set will generate new display lists of terms and documents. Precision is improved in a test on Alta Vista searches.
-
White, R.W.; Jose, J.M.; Ruthven, I.: Using top-ranking sentences to facilitate effective information access (2005)
0.01
0.014805648 = product of:
0.03701412 = sum of:
0.023397226 = weight(_text_:retrieval in 3881) [ClassicSimilarity], result of:
0.023397226 = score(doc=3881,freq=2.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.16710453 = fieldWeight in 3881, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=3881)
0.013616893 = product of:
0.027233787 = sum of:
0.027233787 = weight(_text_:web in 3881) [ClassicSimilarity], result of:
0.027233787 = score(doc=3881,freq=2.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.18028519 = fieldWeight in 3881, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.0390625 = fieldNorm(doc=3881)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- Web searchers typically fall to view search results beyond the first page nor fully examine those results presented to them. In this article we describe an approach that encourages a deeper examination of the contents of the document set retrieved in response to a searcher's query. The approach shifts the focus of perusal and interaction away from potentially uninformative document surrogates (such as titles, sentence fragments, and URLs) to actual document content, and uses this content to drive the information seeking process. Current search interfaces assume searchers examine results document-by-document. In contrast our approach extracts, ranks, and presents the contents of the top-ranked document set. We use query-relevant topranking sentences extracted from the top documents at retrieval time as fine-grained representations of topranked document content and, when combined in a ranked list, an overview of these documents. The interaction of the searcher provides implicit evidence that is used to reorder the sentences where appropriate. We evaluate our approach in three separate user studies, each applying these sentences in a different way. The findings of these studies show that top-ranking sentences can facilitate effective information access.
-
Bauckhage, C.: Marginalizing over the PageRank damping factor (2014)
0.01
0.00935889 = product of:
0.04679445 = sum of:
0.04679445 = weight(_text_:retrieval in 928) [ClassicSimilarity], result of:
0.04679445 = score(doc=928,freq=2.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.33420905 = fieldWeight in 928, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.078125 = fieldNorm(doc=928)
0.2 = coord(1/5)
- Abstract
- In this note, we show how to marginalize over the damping parameter of the PageRank equation so as to obtain a parameter-free version known as TotalRank. Our discussion is meant as a reference and intended to provide a guided tour towards an interesting result that has applications in information retrieval and classification.
-
Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
0.01
0.008779829 = product of:
0.043899145 = sum of:
0.043899145 = product of:
0.08779829 = sum of:
0.08779829 = weight(_text_:22 in 3445) [ClassicSimilarity], result of:
0.08779829 = score(doc=3445,freq=2.0), product of:
0.16209066 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.04628742 = queryNorm
0.5416616 = fieldWeight in 3445, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.109375 = fieldNorm(doc=3445)
0.5 = coord(1/2)
0.2 = coord(1/5)
- Date
- 25. 8.2005 17:42:22
-
Zhang, D.; Dong, Y.: ¬An effective algorithm to rank Web resources (2000)
0.01
0.0076254606 = product of:
0.038127303 = sum of:
0.038127303 = product of:
0.076254606 = sum of:
0.076254606 = weight(_text_:web in 3662) [ClassicSimilarity], result of:
0.076254606 = score(doc=3662,freq=2.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.50479853 = fieldWeight in 3662, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.109375 = fieldNorm(doc=3662)
0.5 = coord(1/2)
0.2 = coord(1/5)
-
Finding anything in the billion page Web : are algorithms the key? (1999)
0.01
0.0076254606 = product of:
0.038127303 = sum of:
0.038127303 = product of:
0.076254606 = sum of:
0.076254606 = weight(_text_:web in 6248) [ClassicSimilarity], result of:
0.076254606 = score(doc=6248,freq=2.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.50479853 = fieldWeight in 6248, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.109375 = fieldNorm(doc=6248)
0.5 = coord(1/2)
0.2 = coord(1/5)
-
Stock, M.; Stock, W.G.: Internet-Suchwerkzeuge im Vergleich (IV) : Relevance Ranking nach "Popularität" von Webseiten: Google (2001)
0.01
0.0056604366 = product of:
0.028302183 = sum of:
0.028302183 = product of:
0.056604367 = sum of:
0.056604367 = weight(_text_:web in 5771) [ClassicSimilarity], result of:
0.056604367 = score(doc=5771,freq=6.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.37471575 = fieldWeight in 5771, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.046875 = fieldNorm(doc=5771)
0.5 = coord(1/2)
0.2 = coord(1/5)
- Abstract
- In unserem Retrievaltest von Suchwerkzeugen im World Wide Web (Password 11/2000) schnitt die Suchmaschine Google am besten ab. Im Vergleich zu anderen Search Engines setzt Google kaum auf Informationslinguistik, sondern auf Algorithmen, die sich aus den Besonderheiten der Web-Dokumente ableiten lassen. Kernstück der informationsstatistischen Technik ist das "PageRank"- Verfahren (benannt nach dem Entwickler Larry Page), das aus der Hypertextstruktur des Web die "Popularität" von Seiten anhand ihrer ein- und ausgehenden Links berechnet. Google besticht durch das Angebot intuitiv verstehbarer Suchbildschirme sowie durch einige sehr nützliche "Kleinigkeiten" wie die Angabe des Rangs einer Seite, Highlighting, Suchen in der Seite, Suchen innerhalb eines Suchergebnisses usw., alles verstaut in einer eigenen Befehlsleiste innerhalb des Browsers. Ähnlich wie RealNames bietet Google mit dem Produkt "AdWords" den Aufkauf von Suchtermen an. Nach einer Reihe von nunmehr vier Password-Artikeln über InternetSuchwerkzeugen im Vergleich wollen wir abschließend zu einer Bewertung kommen. Wie ist der Stand der Technik bei Directories und Search Engines aus informationswissenschaftlicher Sicht einzuschätzen? Werden die "typischen" Internetnutzer, die ja in der Regel keine Information Professionals sind, adäquat bedient? Und können auch Informationsfachleute von den Suchwerkzeugen profitieren?
-
Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009)
0.01
0.0056153345 = product of:
0.028076671 = sum of:
0.028076671 = weight(_text_:retrieval in 3161) [ClassicSimilarity], result of:
0.028076671 = score(doc=3161,freq=2.0), product of:
0.14001551 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.04628742 = queryNorm
0.20052543 = fieldWeight in 3161, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=3161)
0.2 = coord(1/5)
- Abstract
- This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors.
-
Courtois, M.P.; Berry, M.W.: Results ranking in Web search engines (1999)
0.01
0.0054467577 = product of:
0.027233787 = sum of:
0.027233787 = product of:
0.054467574 = sum of:
0.054467574 = weight(_text_:web in 3726) [ClassicSimilarity], result of:
0.054467574 = score(doc=3726,freq=2.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.36057037 = fieldWeight in 3726, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.078125 = fieldNorm(doc=3726)
0.5 = coord(1/2)
0.2 = coord(1/5)
-
Chakrabarti, S.; Dom, B.; Kumar, S.R.; Raghavan, P.; Rajagopalan, S.; Tomkins, A.; Kleinberg, J.M.; Gibson, D.: Neue Pfade durch den Internet-Dschungel : Die zweite Generation von Web-Suchmaschinen (1999)
0.00
0.004357406 = product of:
0.021787029 = sum of:
0.021787029 = product of:
0.043574058 = sum of:
0.043574058 = weight(_text_:web in 3) [ClassicSimilarity], result of:
0.043574058 = score(doc=3,freq=2.0), product of:
0.15105948 = queryWeight, product of:
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.04628742 = queryNorm
0.2884563 = fieldWeight in 3, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.2635105 = idf(docFreq=4597, maxDocs=44218)
0.0625 = fieldNorm(doc=3)
0.5 = coord(1/2)
0.2 = coord(1/5)
