Search (47 results, page 1 of 3)

0.11097769 = product of:
  0.18496281 = sum of:
    0.033088673 = weight(_text_:retrieval in 2670) [ClassicSimilarity], result of:
      0.033088673 = score(doc=2670,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.23632148 = fieldWeight in 2670, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2670)
    0.13261695 = weight(_text_:semantic in 2670) [ClassicSimilarity], result of:
      0.13261695 = score(doc=2670,freq=18.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.68907446 = fieldWeight in 2670, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2670)
    0.019257195 = product of:
      0.03851439 = sum of:
        0.03851439 = weight(_text_:web in 2670) [ClassicSimilarity], result of:
          0.03851439 = score(doc=2670,freq=4.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.25496176 = fieldWeight in 2670, 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=2670)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods.

0.10134516 = product of:
  0.1689086 = sum of:
    0.03970641 = weight(_text_:retrieval in 1280) [ClassicSimilarity], result of:
      0.03970641 = score(doc=1280,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.2835858 = fieldWeight in 1280, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1280)
    0.10609356 = weight(_text_:semantic in 1280) [ClassicSimilarity], result of:
      0.10609356 = score(doc=1280,freq=8.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.5512596 = fieldWeight in 1280, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=1280)
    0.023108633 = product of:
      0.046217266 = sum of:
        0.046217266 = weight(_text_:web in 1280) [ClassicSimilarity], result of:
          0.046217266 = score(doc=1280,freq=4.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.3059541 = fieldWeight in 1280, 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=1280)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

This article provides a technical review of semantic search methods used to support text-based search over formal Semantic Web knowledge bases. Our focus is on ranking methods and auxiliary processes explored by existing semantic search systems, outlined within broad areas of classification. We present reflective examples from the literature in some detail, which should appeal to readers interested in a deeper perspective on the various methods and systems implemented in the outlined literature. The presentation covers graph exploration and propagation methods, adaptations of classic probabilistic retrieval models, and query-independent link analysis via flexible extensions to the PageRank algorithm. Future research directions are discussed, including development of more cohesive retrieval models to unlock further potentials and uses, data indexing schemes, integration with user interfaces, and building community consensus for more systematic evaluation and gradual development.

0.088055015 = product of:
  0.14675835 = sum of:
    0.048630223 = weight(_text_:retrieval in 101) [ClassicSimilarity], result of:
      0.048630223 = score(doc=101,freq=6.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.34732026 = fieldWeight in 101, 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=101)
    0.075019486 = weight(_text_:semantic in 101) [ClassicSimilarity], result of:
      0.075019486 = score(doc=101,freq=4.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.38979942 = fieldWeight in 101, 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=101)
    0.023108633 = product of:
      0.046217266 = sum of:
        0.046217266 = weight(_text_:web in 101) [ClassicSimilarity], result of:
          0.046217266 = score(doc=101,freq=4.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.3059541 = fieldWeight in 101, 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=101)
      0.5 = coord(1/2)
  0.6 = coord(3/5)

Abstract

Question Answering is an area of information retrieval with the added challenge of applying sophisticated techniques to identify the complex syntactic and semantic relationships present in text in order to provide a more sophisticated and satisfactory response to the user's information needs. For this reason, the authors see question answering as the next step beyond standard information retrieval. In this chapter state of the art question answering is covered focusing on providing an overview of systems, techniques and approaches that are likely to be employed in the next generations of search engines. Special attention is paid to question answering using the World Wide Web as the data source and to question answering exploiting the possibilities of Semantic Web. Considerations about the current issues and prospects for promising future research are also provided.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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

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

0.044841524 = product of:
  0.112103805 = sum of:
    0.040525187 = weight(_text_:retrieval in 2591) [ClassicSimilarity], result of:
      0.040525187 = score(doc=2591,freq=6.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.28943354 = fieldWeight in 2591, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2591)
    0.07157862 = sum of:
      0.027233787 = weight(_text_:web in 2591) [ClassicSimilarity], result of:
        0.027233787 = score(doc=2591,freq=2.0), product of:
          0.15105948 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.04628742 = queryNorm
          0.18028519 = fieldWeight in 2591, 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=2591)
      0.04434483 = weight(_text_:22 in 2591) [ClassicSimilarity], result of:
        0.04434483 = score(doc=2591,freq=4.0), product of:
          0.16209066 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04628742 = queryNorm
          0.27358043 = fieldWeight in 2591, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2591)
  0.4 = coord(2/5)

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

0.041238464 = product of:
  0.10309616 = sum of:
    0.028076671 = weight(_text_:retrieval in 1165) [ClassicSimilarity], result of:
      0.028076671 = score(doc=1165,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.20052543 = fieldWeight in 1165, 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=1165)
    0.075019486 = weight(_text_:semantic in 1165) [ClassicSimilarity], result of:
      0.075019486 = score(doc=1165,freq=4.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.38979942 = fieldWeight in 1165, 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=1165)
  0.4 = coord(2/5)

Abstract

One of the main themes in text mining is text representation, which is fundamental and indispensable for text-based intellegent information processing. Generally, text representation inludes two tasks: indexing and weighting. This paper has comparatively studied TF*IDF, LSI and multi-word for text representation. We used a Chinese and an English document collection to respectively evaluate the three methods in information retreival and text categorization. Experimental results have demonstrated that in text categorization, LSI has better performance than other methods in both document collections. Also, LSI has produced the best performance in retrieving English documents. This outcome has shown that LSI has both favorable semantic and statistical quality and is different with the claim that LSI can not produce discriminative power for indexing.

Object

Latent Semantic Indexing

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.029611295 = product of:
  0.07402824 = sum of:
    0.04679445 = weight(_text_:retrieval in 1694) [ClassicSimilarity], result of:
      0.04679445 = score(doc=1694,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.33420905 = fieldWeight in 1694, 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=1694)
    0.027233787 = product of:
      0.054467574 = sum of:
        0.054467574 = weight(_text_:web in 1694) [ClassicSimilarity], result of:
          0.054467574 = score(doc=1694,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.36057037 = fieldWeight in 1694, 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=1694)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Footnote

Beitrag in einem Special Issue "Combining bibliometrics and information retrieval"

0.028371263 = product of:
  0.07092816 = sum of:
    0.057311267 = weight(_text_:retrieval in 1338) [ClassicSimilarity], result of:
      0.057311267 = score(doc=1338,freq=12.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.40932083 = fieldWeight in 1338, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1338)
    0.013616893 = product of:
      0.027233787 = sum of:
        0.027233787 = weight(_text_:web in 1338) [ClassicSimilarity], result of:
          0.027233787 = score(doc=1338,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.18028519 = fieldWeight in 1338, 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=1338)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

A user's query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques model syntagmatic associations that infer two terms co-occur more often than by chance in natural language. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency-based approaches to query expansion and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process improves retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus-based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large-scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.018682227 = product of:
  0.046705566 = sum of:
    0.033088673 = weight(_text_:retrieval in 5087) [ClassicSimilarity], result of:
      0.033088673 = score(doc=5087,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.23632148 = fieldWeight in 5087, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5087)
    0.013616893 = product of:
      0.027233787 = sum of:
        0.027233787 = weight(_text_:web in 5087) [ClassicSimilarity], result of:
          0.027233787 = score(doc=5087,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.18028519 = fieldWeight in 5087, 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=5087)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Numerous feature-based models have been recently proposed by the information retrieval community. The capability of features to express different relevance facets (query- or document-dependent) can explain such a success story. Such models are most of the time supervised, thus requiring a learning phase. To leverage the advantages of feature-based representations of documents, we propose TournaRank, an unsupervised approach inspired by real-life game and sport competition principles. Documents compete against each other in tournaments using features as evidences of relevance. Tournaments are modeled as a sequence of matches, which involve pairs of documents playing in turn their features. Once a tournament is ended, documents are ranked according to their number of won matches during the tournament. This principle is generic since it can be applied to any collection type. It also provides great flexibility since different alternatives can be considered by changing the tournament type, the match rules, the feature set, or the strategies adopted by documents during matches. TournaRank was experimented on several collections to evaluate our model in different contexts and to compare it with related approaches such as Learning To Rank and fusion ones: the TREC Robust2004 collection for homogeneous documents, the TREC Web2014 (ClueWeb12) collection for heterogeneous web documents, and the LETOR3.0 collection for comparison with supervised feature-based models.

0.018375946 = product of:
  0.091879725 = sum of:
    0.091879725 = weight(_text_:semantic in 4577) [ClassicSimilarity], result of:
      0.091879725 = score(doc=4577,freq=6.0), product of:
        0.19245663 = queryWeight, product of:
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.04628742 = queryNorm
        0.47740483 = fieldWeight in 4577, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.1578603 = idf(docFreq=1879, maxDocs=44218)
          0.046875 = fieldNorm(doc=4577)
  0.2 = coord(1/5)

Abstract

Image Ranking is one of the key problems in information science research area. However, most current methods focus on increasing the performance, leaving the semantic gap problem, which refers to the learned ranking models are hard to be understood, remaining intact. Therefore, in this article, we aim at learning an interpretable ranking model to tackle the semantic gap in fine-grained image ranking. We propose to combine attribute-based representation and online passive-aggressive (PA) learning based ranking models to achieve this goal. Besides, considering the highly localized instances in fine-grained image ranking, we introduce a supervised constrained clustering method to gather class-balanced training instances for local PA-based models, and incorporate the learned local models into a unified probabilistic framework. Extensive experiments on the benchmark demonstrate that the proposed framework outperforms state-of-the-art methods in terms of accuracy and speed.

0.017766777 = product of:
  0.04441694 = sum of:
    0.028076671 = weight(_text_:retrieval in 1035) [ClassicSimilarity], result of:
      0.028076671 = score(doc=1035,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.20052543 = fieldWeight in 1035, 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=1035)
    0.01634027 = product of:
      0.03268054 = sum of:
        0.03268054 = weight(_text_:web in 1035) [ClassicSimilarity], result of:
          0.03268054 = score(doc=1035,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.21634221 = fieldWeight in 1035, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=1035)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

User interest as a very dynamic information need is often ignored in most existing information retrieval systems. In this research, we present the results of experiments designed to evaluate the performance of a real-time interest model (RIM) that attempts to identify the dynamic and changing query level interests regarding social media outputs. Unlike most existing ranking methods, our ranking approach targets calculation of the probability that user interest in the content of the document is subject to very dynamic user interest change. We describe 2 formulations of the model (real-time interest vector space and real-time interest language model) stemming from classical relevance ranking methods and develop a novel methodology for evaluating the performance of RIM using Amazon Mechanical Turk to collect (interest-based) relevance judgments on a daily basis. Our results show that the model usually, although not always, performs better than baseline results obtained from commercial web search engines. We identify factors that affect RIM performance and outline plans for future research.

0.015882565 = product of:
  0.07941282 = sum of:
    0.07941282 = weight(_text_:retrieval in 1687) [ClassicSimilarity], result of:
      0.07941282 = score(doc=1687,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.5671716 = fieldWeight in 1687, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.09375 = fieldNorm(doc=1687)
  0.2 = coord(1/5)

Footnote

Beitrag in einem Special Issue "Combining bibliometrics and information retrieval"

0.015630199 = product of:
  0.039075494 = sum of:
    0.023397226 = weight(_text_:retrieval in 664) [ClassicSimilarity], result of:
      0.023397226 = score(doc=664,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.16710453 = fieldWeight in 664, 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=664)
    0.015678266 = product of:
      0.031356532 = sum of:
        0.031356532 = weight(_text_:22 in 664) [ClassicSimilarity], result of:
          0.031356532 = score(doc=664,freq=2.0), product of:
            0.16209066 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04628742 = queryNorm
            0.19345059 = fieldWeight in 664, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=664)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

A new challenge, accessing multiple relevant entities, arises from the availability of linked heterogeneous data. In this article, we address more specifically the problem of accessing relevant entities, such as publications and authors within a bibliographic network, given an information need. We propose a novel algorithm, called BibRank, that estimates a joint relevance of documents and authors within a bibliographic network. This model ranks each type of entity using a score propagation algorithm with respect to the query topic and the structure of the underlying bi-type information entity network. Evidence sources, namely content-based and network-based scores, are both used to estimate the topical similarity between connected entities. For this purpose, authorship relationships are analyzed through a language model-based score on the one hand and on the other hand, non topically related entities of the same type are detected through marginal citations. The article reports the results of experiments using the Bibrank algorithm for an information retrieval task. The CiteSeerX bibliographic data set forms the basis for the topical query automatic generation and evaluation. We show that a statistically significant improvement over closely related ranking models is achieved.

Date

22. 3.2013 19:34:49

0.014805648 = product of:
  0.03701412 = sum of:
    0.023397226 = weight(_text_:retrieval in 5247) [ClassicSimilarity], result of:
      0.023397226 = score(doc=5247,freq=2.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.16710453 = fieldWeight in 5247, 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=5247)
    0.013616893 = product of:
      0.027233787 = sum of:
        0.027233787 = weight(_text_:web in 5247) [ClassicSimilarity], result of:
          0.027233787 = score(doc=5247,freq=2.0), product of:
            0.15105948 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.04628742 = queryNorm
            0.18028519 = fieldWeight in 5247, 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=5247)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Conventional approaches to relevance ranking typically optimize ranking models by each query separately. The traditional cluster hypothesis also does not consider the dependency between related queries. The goal of this paper is to leverage similar search intents to perform ranking consistency so that the search performance can be improved accordingly. Different from the previous supervised approach, which learns relevance by click-through data, we propose a novel cocluster hypothesis to bridge the gap between relevance ranking and ranking consistency. A nearest-neighbors test is also designed to measure the extent to which the cocluster hypothesis holds. Based on the hypothesis, we further propose a two-stage unsupervised approach, in which two ranking heuristics and a cost function are developed to optimize the combination of consistency and uniqueness (or inconsistency). Extensive experiments have been conducted on a real and large-scale search engine log. The experimental results not only verify the applicability of the proposed cocluster hypothesis but also show that our approach is effective in boosting the retrieval performance of the commercial search engine and reaches a comparable performance to the supervised approach.

0.013235469 = product of:
  0.066177346 = sum of:
    0.066177346 = weight(_text_:retrieval in 1696) [ClassicSimilarity], result of:
      0.066177346 = score(doc=1696,freq=4.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.47264296 = fieldWeight in 1696, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.078125 = fieldNorm(doc=1696)
  0.2 = coord(1/5)

Footnote

Beitrag in einem Special Issue "Combining bibliometrics and information retrieval"

0.012415972 = product of:
  0.062079858 = sum of:
    0.062079858 = weight(_text_:retrieval in 4459) [ClassicSimilarity], result of:
      0.062079858 = score(doc=4459,freq=22.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.44337842 = fieldWeight in 4459, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=4459)
  0.2 = coord(1/5)

Abstract

One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text-based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag-of-words approaches, in which the image retrieval problem turns into one of standard text-based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical-image-dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical-image-dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image-relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image-relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state-of-the-art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.

0.0114622535 = product of:
  0.057311267 = sum of:
    0.057311267 = weight(_text_:retrieval in 4746) [ClassicSimilarity], result of:
      0.057311267 = score(doc=4746,freq=12.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.40932083 = fieldWeight in 4746, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4746)
  0.2 = coord(1/5)

Abstract

Traditional ranking models for information retrieval lack the ability to make a clear distinction between relevant and nonrelevant documents at top ranks if both have similar bag-of-words representations with regard to a user query. We aim to go beyond the bag-of-words approach to document ranking in a new perspective, by representing each document as a sequence of sentences. We begin with an assumption that relevant documents are distinguishable from nonrelevant ones by sequential patterns of relevance degrees of sentences to a query. We introduce the notion of relevance flow, which refers to a stream of sentence-query relevance within a document. We then present a framework to learn a function for ranking documents effectively based on various features extracted from their relevance flows and leverage the output to enhance existing retrieval models. We validate the effectiveness of our approach by performing a number of retrieval experiments on three standard test collections, each comprising a different type of document: news articles, medical references, and blog posts. Experimental results demonstrate that the proposed approach can improve the retrieval performance at the top ranks significantly as compared with the state-of-the-art retrieval models regardless of document type.

0.010463557 = product of:
  0.052317787 = sum of:
    0.052317787 = weight(_text_:retrieval in 1283) [ClassicSimilarity], result of:
      0.052317787 = score(doc=1283,freq=10.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.37365708 = fieldWeight in 1283, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1283)
  0.2 = coord(1/5)

Abstract

While term independence is a widely held assumption in most of the established information retrieval approaches, it is clearly not true and various works in the past have investigated a relaxation of the assumption. One approach is to use n-grams in document representation instead of unigrams. However, the majority of early works on n-grams obtained only modest performance improvement. On the other hand, the use of information based on supporting terms or "contexts" of queries has been found to be promising. In particular, recent studies showed that using new context-dependent term weights improved the performance of relevance feedback (RF) retrieval compared with using traditional bag-of-words BM25 term weights. Calculation of the new term weights requires an estimation of the local probability of relevance of each query term occurrence. In previous studies, the estimation of this probability was based on unigrams that occur in the neighborhood of a query term. We explore an integration of the n-gram and context approaches by computing context-dependent term weights based on a mixture of unigrams and bigrams. Extensive experiments are performed using the title queries of the Text Retrieval Conference (TREC)-6, TREC-7, TREC-8, and TREC-2005 collections, for RF with relevance judgment of either the top 10 or top 20 documents of an initial retrieval. We identify some crucial elements needed in the use of bigrams in our methods, such as proper inverse document frequency (IDF) weighting of the bigrams and noise reduction by pruning bigrams with large document frequency values. We show that enhancing context-dependent term weights with bigrams is effective in further improving retrieval performance.

0.010463557 = product of:
  0.052317787 = sum of:
    0.052317787 = weight(_text_:retrieval in 2929) [ClassicSimilarity], result of:
      0.052317787 = score(doc=2929,freq=10.0), product of:
        0.14001551 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.04628742 = queryNorm
        0.37365708 = fieldWeight in 2929, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2929)
  0.2 = coord(1/5)

Abstract

Pseudo relevance feedback, as an effective query expansion method, can significantly improve information retrieval performance. However, the method may negatively impact the retrieval performance when some irrelevant terms are used in the expanded query. Therefore, it is necessary to refine the expansion terms. Learning to rank methods have proven effective in information retrieval to solve ranking problems by ranking the most relevant documents at the top of the returned list, but few attempts have been made to employ learning to rank methods for term refinement in pseudo relevance feedback. This article proposes a novel framework to explore the feasibility of using learning to rank to optimize pseudo relevance feedback by means of reranking the candidate expansion terms. We investigate some learning approaches to choose the candidate terms and introduce some state-of-the-art learning to rank methods to refine the expansion terms. In addition, we propose two term labeling strategies and examine the usefulness of various term features to optimize the framework. Experimental results with three TREC collections show that our framework can effectively improve retrieval performance.

Theme

Semantisches Umfeld in Indexierung u. Retrieval