Search (227 results, page 1 of 12)

0.3338872 = product of:
  0.58430254 = sum of:
    0.09614123 = product of:
      0.16023538 = sum of:
        0.115060724 = weight(_text_:3a in 5820) [ClassicSimilarity], result of:
          0.115060724 = score(doc=5820,freq=2.0), product of:
            0.3070917 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03622214 = queryNorm
            0.3746787 = fieldWeight in 5820, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03125 = fieldNorm(doc=5820)
        0.029295133 = weight(_text_:retrieval in 5820) [ClassicSimilarity], result of:
          0.029295133 = score(doc=5820,freq=8.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.26736724 = fieldWeight in 5820, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=5820)
        0.015879504 = weight(_text_:system in 5820) [ClassicSimilarity], result of:
          0.015879504 = score(doc=5820,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.13919188 = fieldWeight in 5820, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03125 = fieldNorm(doc=5820)
      0.6 = coord(3/5)
    0.16272044 = weight(_text_:2f in 5820) [ClassicSimilarity], result of:
      0.16272044 = score(doc=5820,freq=4.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.5298757 = fieldWeight in 5820, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03125 = fieldNorm(doc=5820)
    0.16272044 = weight(_text_:2f in 5820) [ClassicSimilarity], result of:
      0.16272044 = score(doc=5820,freq=4.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.5298757 = fieldWeight in 5820, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03125 = fieldNorm(doc=5820)
    0.16272044 = weight(_text_:2f in 5820) [ClassicSimilarity], result of:
      0.16272044 = score(doc=5820,freq=4.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.5298757 = fieldWeight in 5820, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03125 = fieldNorm(doc=5820)
  0.5714286 = coord(4/7)

Abstract

The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.
This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines word based and entity based representations together with their uncertainties considered. At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations. This dissertation overcomes the limitation of word based representations with external and carefully curated information from knowledge bases. We believe this thesis research is a solid start towards the new generation of intelligent, semantic, and structured information retrieval.

Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

0.31559512 = product of:
  0.55229145 = sum of:
    0.034518216 = product of:
      0.17259108 = sum of:
        0.17259108 = weight(_text_:3a in 400) [ClassicSimilarity], result of:
          0.17259108 = score(doc=400,freq=2.0), product of:
            0.3070917 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03622214 = queryNorm
            0.56201804 = fieldWeight in 400, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=400)
      0.2 = coord(1/5)
    0.17259108 = weight(_text_:2f in 400) [ClassicSimilarity], result of:
      0.17259108 = score(doc=400,freq=2.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.56201804 = fieldWeight in 400, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=400)
    0.17259108 = weight(_text_:2f in 400) [ClassicSimilarity], result of:
      0.17259108 = score(doc=400,freq=2.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.56201804 = fieldWeight in 400, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=400)
    0.17259108 = weight(_text_:2f in 400) [ClassicSimilarity], result of:
      0.17259108 = score(doc=400,freq=2.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.56201804 = fieldWeight in 400, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=400)
  0.5714286 = coord(4/7)

Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

0.26491702 = product of:
  0.46360478 = sum of:
    0.118422605 = product of:
      0.197371 = sum of:
        0.115060724 = weight(_text_:3a in 701) [ClassicSimilarity], result of:
          0.115060724 = score(doc=701,freq=2.0), product of:
            0.3070917 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03622214 = queryNorm
            0.3746787 = fieldWeight in 701, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03125 = fieldNorm(doc=701)
        0.054806177 = weight(_text_:retrieval in 701) [ClassicSimilarity], result of:
          0.054806177 = score(doc=701,freq=28.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.5001983 = fieldWeight in 701, product of:
              5.2915025 = tf(freq=28.0), with freq of:
                28.0 = termFreq=28.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=701)
        0.027504109 = weight(_text_:system in 701) [ClassicSimilarity], result of:
          0.027504109 = score(doc=701,freq=6.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.24108742 = fieldWeight in 701, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03125 = fieldNorm(doc=701)
      0.6 = coord(3/5)
    0.115060724 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
      0.115060724 = score(doc=701,freq=2.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.3746787 = fieldWeight in 701, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03125 = fieldNorm(doc=701)
    0.115060724 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
      0.115060724 = score(doc=701,freq=2.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.3746787 = fieldWeight in 701, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03125 = fieldNorm(doc=701)
    0.115060724 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
      0.115060724 = score(doc=701,freq=2.0), product of:
        0.3070917 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03622214 = queryNorm
        0.3746787 = fieldWeight in 701, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.03125 = fieldNorm(doc=701)
  0.5714286 = coord(4/7)

Abstract

By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.

Content

Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.

0.013270704 = product of:
  0.046447463 = sum of:
    0.031724665 = product of:
      0.07931166 = sum of:
        0.0380555 = weight(_text_:retrieval in 3261) [ClassicSimilarity], result of:
          0.0380555 = score(doc=3261,freq=6.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.34732026 = fieldWeight in 3261, 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=3261)
        0.041256163 = weight(_text_:system in 3261) [ClassicSimilarity], result of:
          0.041256163 = score(doc=3261,freq=6.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.36163113 = fieldWeight in 3261, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=3261)
      0.4 = coord(2/5)
    0.0147228 = product of:
      0.0294456 = sum of:
        0.0294456 = weight(_text_:22 in 3261) [ClassicSimilarity], result of:
          0.0294456 = score(doc=3261,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.23214069 = fieldWeight in 3261, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=3261)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Ontologies improve IR systems regarding its retrieval and presentation of information, which make the task of finding information more effective, efficient, and interactive. In this paper we argue that ontologies also greatly improve the engineering of such systems. We created a framework that uses ontology to drive the process of engineering an IR system. We developed a prototype that shows how a domain specialist without knowledge in the IR field can build an IR system with interactive components. The resulting system provides support for users not only to find their information needs but also to extend their state of knowledge. This way, our approach to ontology-enabled information retrieval addresses both the engineering aspect described here and also the usability aspect described elsewhere.

Date

28.11.2016 12:43:22

0.011013014 = product of:
  0.03854555 = sum of:
    0.02136895 = product of:
      0.053422377 = sum of:
        0.025633242 = weight(_text_:retrieval in 5095) [ClassicSimilarity], result of:
          0.025633242 = score(doc=5095,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.23394634 = fieldWeight in 5095, 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=5095)
        0.027789133 = weight(_text_:system in 5095) [ClassicSimilarity], result of:
          0.027789133 = score(doc=5095,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.2435858 = fieldWeight in 5095, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5095)
      0.4 = coord(2/5)
    0.0171766 = product of:
      0.0343532 = sum of:
        0.0343532 = weight(_text_:22 in 5095) [ClassicSimilarity], result of:
          0.0343532 = score(doc=5095,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.2708308 = fieldWeight in 5095, 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=5095)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

This paper presents an abstract formalization of the notion of "facets". Facets are relational structures of units, relations and other facets selected for a certain purpose. Facets can be used to structure large knowledge representation systems into a hierarchical arrangement of consistent and independent subsystems (facets) that facilitate flexibility and combinations of different viewpoints or aspects. This paper describes the basic notions, facet characteristics and construction mechanisms. It then explicates the theory in an example of a faceted information retrieval system (FaIR)

Date

22. 1.2016 17:47:06

0.011013014 = product of:
  0.03854555 = sum of:
    0.02136895 = product of:
      0.053422377 = sum of:
        0.025633242 = weight(_text_:retrieval in 2654) [ClassicSimilarity], result of:
          0.025633242 = score(doc=2654,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.23394634 = fieldWeight in 2654, 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=2654)
        0.027789133 = weight(_text_:system in 2654) [ClassicSimilarity], result of:
          0.027789133 = score(doc=2654,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.2435858 = fieldWeight in 2654, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2654)
      0.4 = coord(2/5)
    0.0171766 = product of:
      0.0343532 = sum of:
        0.0343532 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
          0.0343532 = score(doc=2654,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.2708308 = fieldWeight in 2654, 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=2654)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Faceted Knowledge Representation provides a formalism for implementing knowledge systems. The basic notions of faceted knowledge representation are "unit", "relation", "facet" and "interpretation". Units are atomic elements and can be abstract elements or refer to external objects in an application. Relations are sequences or matrices of 0 and 1's (binary matrices). Facets are relational structures that combine units and relations. Each facet represents an aspect or viewpoint of a knowledge system. Interpretations are mappings that can be used to translate between different representations. This paper introduces the basic notions of faceted knowledge representation. The formalism is applied here to an abstract modeling of a faceted thesaurus as used in information retrieval.

Date

22. 1.2016 17:30:31

0.010479822 = product of:
  0.036679376 = sum of:
    0.021956576 = product of:
      0.054891437 = sum of:
        0.03107218 = weight(_text_:retrieval in 2623) [ClassicSimilarity], result of:
          0.03107218 = score(doc=2623,freq=4.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.2835858 = fieldWeight in 2623, 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=2623)
        0.023819257 = weight(_text_:system in 2623) [ClassicSimilarity], result of:
          0.023819257 = score(doc=2623,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.20878783 = fieldWeight in 2623, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=2623)
      0.4 = coord(2/5)
    0.0147228 = product of:
      0.0294456 = sum of:
        0.0294456 = weight(_text_:22 in 2623) [ClassicSimilarity], result of:
          0.0294456 = score(doc=2623,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.23214069 = fieldWeight in 2623, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2623)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Contemporary retrieval systems, which search across collections, usually ignore collection-level metadata. Alternative approaches, exploiting collection-level information, will require an understanding of the various kinds of relationships that can obtain between collection-level and item-level metadata. This paper outlines the problem and describes a project that is developing a logic-based framework for classifying collection/item metadata relationships. This framework will support (i) metadata specification developers defining metadata elements, (ii) metadata creators describing objects, and (iii) system designers implementing systems that take advantage of collection-level metadata. We present three examples of collection/item metadata relationship categories, attribute/value-propagation, value-propagation, and value-constraint and show that even in these simple cases a precise formulation requires modal notions in addition to first-order logic. These formulations are related to recent work in information retrieval and ontology evaluation.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

0.008806083 = product of:
  0.03082129 = sum of:
    0.018552288 = product of:
      0.04638072 = sum of:
        0.01830946 = weight(_text_:retrieval in 2831) [ClassicSimilarity], result of:
          0.01830946 = score(doc=2831,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.16710453 = fieldWeight in 2831, 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=2831)
        0.028071264 = weight(_text_:system in 2831) [ClassicSimilarity], result of:
          0.028071264 = score(doc=2831,freq=4.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.24605882 = fieldWeight in 2831, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2831)
      0.4 = coord(2/5)
    0.0122690005 = product of:
      0.024538001 = sum of:
        0.024538001 = weight(_text_:22 in 2831) [ClassicSimilarity], result of:
          0.024538001 = score(doc=2831,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.19345059 = fieldWeight in 2831, 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=2831)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

The purpose of this work is to develop an ontology-based framework for developing an information retrieval system to cater to specific queries of users. For creating such an ontology, information was obtained from a wide range of information sources involved with brain tumour study and research. The information thus obtained was compiled and analysed to provide a standard, reliable and relevant information base to aid our proposed system. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as identification of the terminology, analysis, synthesis, standardization and ordering. A vast majority of the ontologies being developed nowadays lack flexibility. This becomes a formidable constraint when it comes to interoperability. We found that a facet-based method provides a distinct guideline for the development of a robust and flexible model concerning the domain of brain tumours. Our attempt has been to bridge library and information science and computer science, which itself involved an experimental approach. It was discovered that a faceted approach is really enduring, as it helps in the achievement of properties like navigation, exploration and faceted browsing. Computer-based brain tumour ontology supports the work of researchers towards gathering information on brain tumour research and allows users across the world to intelligently access new scientific information quickly and efficiently.

Date

12. 3.2016 13:21:22

0.008182896 = product of:
  0.028640134 = sum of:
    0.011463534 = product of:
      0.05731767 = sum of:
        0.05731767 = weight(_text_:retrieval in 4330) [ClassicSimilarity], result of:
          0.05731767 = score(doc=4330,freq=10.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.5231199 = fieldWeight in 4330, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4330)
      0.2 = coord(1/5)
    0.0171766 = product of:
      0.0343532 = sum of:
        0.0343532 = weight(_text_:22 in 4330) [ClassicSimilarity], result of:
          0.0343532 = score(doc=4330,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.2708308 = fieldWeight in 4330, 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=4330)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

One vision of the Semantic Web is that it will be much like the Web we know today, except that documents will be enriched by annotations in machine understandable markup. These annotations will provide metadata about the documents as well as machine interpretable statements capturing some of the meaning of document content. We discuss how the information retrieval paradigm might be recast in such an environment. We suggest that retrieval can be tightly bound to inference. Doing so makes today's Web search engines useful to Semantic Web inference engines, and causes improvements in either retrieval or inference to lead directly to improvements in the other.

Date

12. 2.2011 17:35:22

0.007866439 = product of:
  0.027532537 = sum of:
    0.015263537 = product of:
      0.03815884 = sum of:
        0.01830946 = weight(_text_:retrieval in 1434) [ClassicSimilarity], result of:
          0.01830946 = score(doc=1434,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.16710453 = fieldWeight in 1434, 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=1434)
        0.01984938 = weight(_text_:system in 1434) [ClassicSimilarity], result of:
          0.01984938 = score(doc=1434,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.17398985 = fieldWeight in 1434, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1434)
      0.4 = coord(2/5)
    0.0122690005 = product of:
      0.024538001 = sum of:
        0.024538001 = weight(_text_:22 in 1434) [ClassicSimilarity], result of:
          0.024538001 = score(doc=1434,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.19345059 = fieldWeight in 1434, 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=1434)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Knowledge organization of large-scale content on the Web requires substantial amounts of semantic metadata that is expensive to generate manually. Recent developments in Web technologies have enabled any user to tag documents and other forms of content thereby generating metadata that could help organize knowledge. However, merely adding one or more tags to a document is highly inadequate to capture the aboutness of the document and thereby to support powerful semantic functions such as automatic classification, question answering or true semantic search and retrieval. This is true even when the tags used are labels from a well-designed classification system such as a thesaurus or taxonomy. There is a strong need to develop a semantic tagging mechanism with sufficient expressive power to capture the aboutness of each part of a document or dataset or multimedia content in order to enable applications that can benefit from knowledge organization on the Web. This article proposes a highly expressive mechanism of using ontology snippets as semantic tags that map portions of a document or a part of a dataset or a segment of a multimedia content to concepts and relations in an ontology of the domain(s) of interest.

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

0.00752827 = product of:
  0.026348945 = sum of:
    0.011626146 = product of:
      0.058130726 = sum of:
        0.058130726 = weight(_text_:retrieval in 987) [ClassicSimilarity], result of:
          0.058130726 = score(doc=987,freq=14.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.5305404 = fieldWeight in 987, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=987)
      0.2 = coord(1/5)
    0.0147228 = product of:
      0.0294456 = sum of:
        0.0294456 = weight(_text_:22 in 987) [ClassicSimilarity], result of:
          0.0294456 = score(doc=987,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.23214069 = fieldWeight in 987, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=987)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Content

Introduction: envisioning semantic information spacesIndexing and knowledge organization -- Semantic technologies for knowledge representation -- Information retrieval and knowledge exploration -- Approaches to handle heterogeneity -- Problems with establishing semantic interoperability -- Formalization in indexing languages -- Typification of semantic relations -- Inferences in retrieval processes -- Semantic interoperability and inferences -- Remaining research questions.

Date

23. 7.2017 13:49:22

LCSH

Information retrieval

RSWK

Information Retrieval

Subject

Information retrieval
Information Retrieval

0.0072365343 = product of:
  0.025327869 = sum of:
    0.016739568 = product of:
      0.04184892 = sum of:
        0.022199038 = weight(_text_:retrieval in 1633) [ClassicSimilarity], result of:
          0.022199038 = score(doc=1633,freq=6.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.20260347 = fieldWeight in 1633, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1633)
        0.019649884 = weight(_text_:system in 1633) [ClassicSimilarity], result of:
          0.019649884 = score(doc=1633,freq=4.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.17224117 = fieldWeight in 1633, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1633)
      0.4 = coord(2/5)
    0.0085883 = product of:
      0.0171766 = sum of:
        0.0171766 = weight(_text_:22 in 1633) [ClassicSimilarity], result of:
          0.0171766 = score(doc=1633,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.1354154 = fieldWeight in 1633, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1633)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Purpose - The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts. Design/methodology/approach - In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query. Findings - The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology "with and without query expansion" is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42. Practical implications - When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved. Originality/value - In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.

Date

20. 1.2015 18:30:22

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.005982068 = product of:
  0.020937236 = sum of:
    0.006214436 = product of:
      0.03107218 = sum of:
        0.03107218 = weight(_text_:retrieval in 2230) [ClassicSimilarity], result of:
          0.03107218 = score(doc=2230,freq=4.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.2835858 = fieldWeight in 2230, 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=2230)
      0.2 = coord(1/5)
    0.0147228 = product of:
      0.0294456 = sum of:
        0.0294456 = weight(_text_:22 in 2230) [ClassicSimilarity], result of:
          0.0294456 = score(doc=2230,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.23214069 = fieldWeight in 2230, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2230)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Source

Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0055579958 = product of:
  0.038905967 = sum of:
    0.038905967 = product of:
      0.097264916 = sum of:
        0.06550591 = weight(_text_:retrieval in 3979) [ClassicSimilarity], result of:
          0.06550591 = score(doc=3979,freq=10.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.59785134 = fieldWeight in 3979, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0625 = fieldNorm(doc=3979)
        0.03175901 = weight(_text_:system in 3979) [ClassicSimilarity], result of:
          0.03175901 = score(doc=3979,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.27838376 = fieldWeight in 3979, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0625 = fieldNorm(doc=3979)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

The retrieval problem is one of the main reasoning tasks for knowledge base systems. Given a knowledge base K and a concept C, the retrieval problem consists of finding all individuals a for which K logically entails C(a). We present an approach to answer retrieval queries over (a restriction of) OWL ontologies. Our solution is based on reducing the retrieval problem to a problem of evaluating an SQL query over a database constructed from the original knowledge base. We provide complete answers to retrieval problems. Still, our system performs very well as is shown by a standard benchmark.

0.005520997 = product of:
  0.038646977 = sum of:
    0.038646977 = product of:
      0.09661744 = sum of:
        0.05731767 = weight(_text_:retrieval in 1153) [ClassicSimilarity], result of:
          0.05731767 = score(doc=1153,freq=10.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.5231199 = fieldWeight in 1153, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1153)
        0.039299767 = weight(_text_:system in 1153) [ClassicSimilarity], result of:
          0.039299767 = score(doc=1153,freq=4.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.34448233 = fieldWeight in 1153, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1153)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

In the proposed article a new, ontology-based approach to information retrieval (IR) is presented. The system is based on a domain knowledge representation schema in form of ontology. New resources registered within the system are linked to concepts from this ontology. In such a way resources may be retrieved based on the associations and not only based on partial or exact term matching as the use of vector model presumes In order to evaluate the quality of this retrieval mechanism, experiments to measure retrieval efficiency have been performed with well-known Cystic Fibrosis collection of medical scientific papers. The ontology-based retrieval mechanism has been compared with traditional full text search based on vector IR model as well as with the Latent Semantic Indexing method.

0.00546202 = product of:
  0.01911707 = sum of:
    0.00439427 = product of:
      0.02197135 = sum of:
        0.02197135 = weight(_text_:retrieval in 2655) [ClassicSimilarity], result of:
          0.02197135 = score(doc=2655,freq=2.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.20052543 = fieldWeight in 2655, 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=2655)
      0.2 = coord(1/5)
    0.0147228 = product of:
      0.0294456 = sum of:
        0.0294456 = weight(_text_:22 in 2655) [ClassicSimilarity], result of:
          0.0294456 = score(doc=2655,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.23214069 = fieldWeight in 2655, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2655)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

The term "facet" was introduced into the field of library classification systems by Ranganathan in the 1930's [Ranganathan, 1962]. A facet is a viewpoint or aspect. In contrast to traditional classification systems, faceted systems are modular in that a domain is analyzed in terms of baseline facets which are then synthesized. In this paper, the term "facet" is used in a broader meaning. Facets can describe different aspects on the same level of abstraction or the same aspect on different levels of abstraction. The notion of facets is related to database views, multicontexts and conceptual scaling in formal concept analysis [Ganter and Wille, 1999], polymorphism in object-oriented design, aspect-oriented programming, views and contexts in description logic and semantic networks. This paper presents a definition of facets in terms of faceted knowledge representation that incorporates the traditional narrower notion of facets and potentially facilitates translation between different knowledge representation formalisms. A goal of this approach is a modular, machine-aided knowledge base design mechanism. A possible application is faceted thesaurus construction for information retrieval and data mining. Reasoning complexity depends on the size of the modules (facets). A more general analysis of complexity will be left for future research.

Date

22. 1.2016 17:30:31

0.0052332124 = product of:
  0.036632486 = sum of:
    0.036632486 = product of:
      0.09158121 = sum of:
        0.0439427 = weight(_text_:retrieval in 3829) [ClassicSimilarity], result of:
          0.0439427 = score(doc=3829,freq=8.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.40105087 = fieldWeight in 3829, 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=3829)
        0.047638513 = weight(_text_:system in 3829) [ClassicSimilarity], result of:
          0.047638513 = score(doc=3829,freq=8.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.41757566 = fieldWeight in 3829, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.046875 = fieldNorm(doc=3829)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

In this thesis, we present an ontology-based information extraction and retrieval system and its application to soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of the system is improved considerably using domain-specific information extraction, inference and rules. Scalability is achieved by adapting a semantic indexing approach. The system is implemented using the state-of-the-art technologies in SemanticWeb and its performance is evaluated against traditional systems as well as the query expansion methods. Furthermore, a detailed evaluation is provided to observe the performance gain due to domain-specific information extraction and inference. Finally, we show how we use semantic indexing to solve simple structural ambiguities.

0.004782734 = product of:
  0.033479135 = sum of:
    0.033479135 = product of:
      0.08369784 = sum of:
        0.044398077 = weight(_text_:retrieval in 4471) [ClassicSimilarity], result of:
          0.044398077 = score(doc=4471,freq=6.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.40520695 = fieldWeight in 4471, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4471)
        0.039299767 = weight(_text_:system in 4471) [ClassicSimilarity], result of:
          0.039299767 = score(doc=4471,freq=4.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.34448233 = fieldWeight in 4471, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4471)
      0.4 = coord(2/5)
  0.14285715 = coord(1/7)

Abstract

We present an Information Retrieval System for scientific publications that provides the possibility to filter results according to semantic facets. We use sentence-level semantic annotations that identify specific semantic relations in texts, such as methods, definitions, hypotheses, that correspond to common information needs related to scientific literature. The semantic annotations are obtained using a rule-based method that identifies linguistic clues organized into a linguistic ontology. The system is implemented using Solr Search Server and offers efficient search and navigation in scientific papers.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0046759406 = product of:
  0.01636579 = sum of:
    0.006550591 = product of:
      0.032752953 = sum of:
        0.032752953 = weight(_text_:retrieval in 4399) [ClassicSimilarity], result of:
          0.032752953 = score(doc=4399,freq=10.0), product of:
            0.109568894 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03622214 = queryNorm
            0.29892567 = fieldWeight in 4399, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=4399)
      0.2 = coord(1/5)
    0.0098152 = product of:
      0.0196304 = sum of:
        0.0196304 = weight(_text_:22 in 4399) [ClassicSimilarity], result of:
          0.0196304 = score(doc=4399,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.15476047 = fieldWeight in 4399, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=4399)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Indexing plays a vital role in Information Retrieval. With the availability of huge volume of information, it has become necessary to index the information in such a way to make easier for the end users to find the information they want efficiently and accurately. Keyword-based indexing uses words as indexing terms. It is not capable of capturing the implicit relation among terms or the semantics of the words in the document. To eliminate this limitation, ontology-based indexing came into existence, which allows semantic based indexing to solve complex and indirect user queries. Ontologies are used for document indexing which allows semantic based information retrieval. Existing ontologies or the ones constructed from scratch are used presently for indexing. Constructing ontologies from scratch is a labor-intensive task and requires extensive domain knowledge whereas use of an existing ontology may leave some important concepts in documents un-annotated. Using multiple ontologies can overcome the problem of missing out concepts to a great extent, but it is difficult to manage (changes in ontologies over time by their developers) multiple ontologies and ontology heterogeneity also arises due to ontologies constructed by different ontology developers. One possible solution to managing multiple ontologies and build from scratch is to use modular ontologies for indexing.
Modular ontologies are built in modular manner by combining modules from multiple relevant ontologies. Ontology heterogeneity also arises during modular ontology construction because multiple ontologies are being dealt with, during this process. Ontologies need to be aligned before using them for modular ontology construction. The existing approaches for ontology alignment compare all the concepts of each ontology to be aligned, hence not optimized in terms of time and search space utilization. A new indexing technique is proposed based on modular ontology. An efficient ontology alignment technique is proposed to solve the heterogeneity problem during the construction of modular ontology. Results are satisfactory as Precision and Recall are improved by (8%) and (10%) respectively. The value of Pearsons Correlation Coefficient for degree of similarity, time, search space requirement, precision and recall are close to 1 which shows that the results are significant. Further research can be carried out for using modular ontology based indexing technique for Multimedia Information Retrieval and Bio-Medical information retrieval.

Date

20. 1.2015 18:30:22

0.004639679 = product of:
  0.016238876 = sum of:
    0.003969876 = product of:
      0.01984938 = sum of:
        0.01984938 = weight(_text_:system in 4607) [ClassicSimilarity], result of:
          0.01984938 = score(doc=4607,freq=2.0), product of:
            0.11408355 = queryWeight, product of:
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.03622214 = queryNorm
            0.17398985 = fieldWeight in 4607, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1495528 = idf(docFreq=5152, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4607)
      0.2 = coord(1/5)
    0.0122690005 = product of:
      0.024538001 = sum of:
        0.024538001 = weight(_text_:22 in 4607) [ClassicSimilarity], result of:
          0.024538001 = score(doc=4607,freq=2.0), product of:
            0.12684377 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03622214 = queryNorm
            0.19345059 = fieldWeight in 4607, 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=4607)
      0.5 = coord(1/2)
  0.2857143 = coord(2/7)

Abstract

Smart applications behave intelligently because they understand at least partially the context where they operate. To do this, they need not only a formal domain model but also formal descriptions of the data they process and their own operational behaviour. Interoperability of smart applications is based on formalised definitions of all their data and processes. This paper studies the semantic interoperability of data in the case of eLearning and describes an experiment and its assessment. New content is imported into a knowledge-based learning environment without real updates of the original domain model, which is encoded as a knowledge base of conceptual graphs. A component called mediator enables the import by assigning dummy metadata annotations for the imported items. However, some functionality of the original system is lost, when processing the imported content, due to the lack of proper metadata annotation which cannot be associated fully automatically. So the paper presents an interoperability scenario when appropriate content items are viewed from the perspective of the original world and can be (partially) reused there.

Source

Conceptual structures: knowledge architectures for smart applications: 15th International Conference on Conceptual Structures, ICCS 2007, Sheffield, UK, July 22 - 27, 2007 ; proceedings. Eds.: U. Priss u.a