Search (16 results, page 1 of 1)

0.028190564 = product of:
  0.05638113 = sum of:
    0.05638113 = product of:
      0.11276226 = sum of:
        0.11276226 = weight(_text_:network in 2393) [ClassicSimilarity], result of:
          0.11276226 = score(doc=2393,freq=8.0), product of:
            0.22917621 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.05146125 = queryNorm
            0.492033 = fieldWeight in 2393, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2393)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.

0.024650764 = product of:
  0.049301527 = sum of:
    0.049301527 = product of:
      0.098603055 = sum of:
        0.098603055 = weight(_text_:22 in 1352) [ClassicSimilarity], result of:
          0.098603055 = score(doc=1352,freq=4.0), product of:
            0.18020853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05146125 = queryNorm
            0.54716086 = fieldWeight in 1352, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=1352)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 2.2003 17:25:39
22. 2.2003 18:17:40

0.022552451 = product of:
  0.045104902 = sum of:
    0.045104902 = product of:
      0.090209804 = sum of:
        0.090209804 = weight(_text_:network in 1254) [ClassicSimilarity], result of:
          0.090209804 = score(doc=1254,freq=8.0), product of:
            0.22917621 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.05146125 = queryNorm
            0.3936264 = fieldWeight in 1254, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.03125 = fieldNorm(doc=1254)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Knowledge management in digital libraries is a universal problem. Keyword-based searching is applied everywhere no matter whether the resources are indexed databases or full-text Web pages. In keyword matching, the valuable content description and indexing of the metadata, such as the subject descriptors and the classification notations, are merely treated as common keywords to be matched with the user query. Without the support of vocabulary control tools, such as classification systems and thesauri, the intelligent labor of content analysis, description and indexing in metadata production are seriously wasted. New retrieval paradigms are needed to exploit the potential of the metadata resources. Could classification and thesauri, which contain the condensed intelligence of generations of librarians, be used in a digital library to organize the networked information, especially metadata, to facilitate their usability and change the digital library into a knowledge management environment? To examine that question, we designed and implemented a new paradigm that incorporates a classification system, a thesaurus and metadata. The classification and the thesaurus are merged into a concept network, and the metadata are distributed into the nodes of the concept network according to their subjects. The abstract concept node instantiated with the related metadata records becomes a knowledge node. A coherent and consistent knowledge network is thus formed. It is not only a framework for resource organization but also a structure for knowledge navigation, retrieval and learning. We have built an experimental system based on the Chinese Classification and Thesaurus, which is the most comprehensive and authoritative in China, and we have incorporated more than 5000 bibliographic records in the computing domain from the Peking University Library. The result is encouraging. In this article, we review the tools, the architecture and the implementation of our experimental system, which is called Vision.

0.01993374 = product of:
  0.03986748 = sum of:
    0.03986748 = product of:
      0.07973496 = sum of:
        0.07973496 = weight(_text_:network in 85) [ClassicSimilarity], result of:
          0.07973496 = score(doc=85,freq=4.0), product of:
            0.22917621 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.05146125 = queryNorm
            0.34791988 = fieldWeight in 85, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=85)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The amount of knowledge accumulated in published scientific papers has increased due to the continuing progress being made in scientific research. Since numerous papers have only reported fragments of scientific facts, there are possibilities for discovering new knowledge by connecting these facts. We therefore developed a system called BioTermNet to draft a conceptual network with hybrid methods of information extraction and information retrieval. Two concepts are regarded as related in this system if (a) their relationship is clearly described in MEDLINE abstracts or (b) they have distinctively co-occurred in abstracts. PRIME data, including protein interactions and functions extracted by NLP techniques, are used in the former, and the Singhalmeasure for information retrieval is used in the latter. Relationships that are not clearly or directly described in an abstract can be extracted by connecting multiple concepts. To evaluate how well this system performs, Swanson's association between Raynaud's disease and fish oil and that between migraine and magnesium were tested with abstracts that had been published before the discovery of these associations. The result was that when start and end concepts were given, plausible and understandable intermediate concepts connecting them could be detected. When only the start concept was given, not only the focused concept (magnesium and fish oil) but also other probable concepts could be detected as related concept candidates. Finally, this system was applied to find diseases related to the BRCA1 gene. Some other new potentially related diseases were detected along with diseases whose relations to BRCA1 were already known.

0.017255535 = product of:
  0.03451107 = sum of:
    0.03451107 = product of:
      0.06902214 = sum of:
        0.06902214 = weight(_text_:22 in 5295) [ClassicSimilarity], result of:
          0.06902214 = score(doc=5295,freq=4.0), product of:
            0.18020853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05146125 = queryNorm
            0.38301262 = fieldWeight in 5295, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5295)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 7.2006 17:56:22

0.016914338 = product of:
  0.033828676 = sum of:
    0.033828676 = product of:
      0.06765735 = sum of:
        0.06765735 = weight(_text_:network in 2124) [ClassicSimilarity], result of:
          0.06765735 = score(doc=2124,freq=2.0), product of:
            0.22917621 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.05146125 = queryNorm
            0.29521978 = fieldWeight in 2124, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=2124)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The concept of an "information space" provides a powerful metaphor for guiding the design of interactive retrieval systems. We present a case study of related article search, a browsing tool designed to help users navigate the information space defined by results of the PubMed® search engine. This feature leverages content-similarity links that tie MEDLINE® citations together in a vast document network. We examine the effectiveness of related article search from two perspectives: a topological analysis of networks generated from information needs represented in the TREC 2005 genomics track and a query log analysis of real PubMed users. Together, data suggest that related article search is a useful feature and that browsing related articles has become an integral part of how users interact with PubMed.

0.016914338 = product of:
  0.033828676 = sum of:
    0.033828676 = product of:
      0.06765735 = sum of:
        0.06765735 = weight(_text_:network in 4043) [ClassicSimilarity], result of:
          0.06765735 = score(doc=4043,freq=2.0), product of:
            0.22917621 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.05146125 = queryNorm
            0.29521978 = fieldWeight in 4043, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=4043)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This paper presents a clustered approach to designing an overall ontological model together with a general rule-based component that serves as a mapping device. By observational criteria, a multi-lingual team of experts excerpts concepts from general communication in the media. The team, then, finds equivalent expressions in English, German, French, and Spanish. On the basis of a set of ontological and lexical relations, a conceptual network is built up. Concepts are thought to be universal. Objects unique in time and space are identified by names and will be explained by the universals as their instances. Our approach relies on multi-relational descriptions of concepts. It provides a powerful tool for documentation and conceptual language learning. First and foremost, our multi-lingual, polyhierarchical ontology fills the gap of semantically-based information retrieval by generating enhanced and improved queries for internet search

0.014095282 = product of:
  0.028190564 = sum of:
    0.028190564 = product of:
      0.05638113 = sum of:
        0.05638113 = weight(_text_:network in 5703) [ClassicSimilarity], result of:
          0.05638113 = score(doc=5703,freq=2.0), product of:
            0.22917621 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.05146125 = queryNorm
            0.2460165 = fieldWeight in 5703, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5703)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Structured thesauri encode equivalent, hierarchical, and associative relationships and have been developed as indexing/retrieval tools. Despite the fact that these tools provide a rich semantic network of vocabulary terms, they are seldom employed for automatic query expansion (QE) activities. This article reports on an experiment that examined whether thesaurus terms, related to query in a specified semantic way (as synonyms and partial-synonyms (SYNs), narrower terms (NTs), related terms (RTs), and broader terms (BTs)), could be identified as having a more positive impact on retrieval effectiveness when added to a query through automatic QE. The research found that automatic QE via SYNs and NTs increased relative recall with a decline in precision that was not statistically significant, and that automatic QE via RTs and BTs increased relative recall with a decline in precision that was statistically significant. Recallbased and a precision-based ranking orders for automatic QE via semantically encoded thesauri terminology were identified. Mapping results found between enduser query terms and the ProQuest Controlled Vocabulary (1997) (the thesaurus used in this study) are reported, and future research foci related to the investigation are discussed

0.014095282 = product of:
  0.028190564 = sum of:
    0.028190564 = product of:
      0.05638113 = sum of:
        0.05638113 = weight(_text_:network in 2215) [ClassicSimilarity], result of:
          0.05638113 = score(doc=2215,freq=2.0), product of:
            0.22917621 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.05146125 = queryNorm
            0.2460165 = fieldWeight in 2215, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2215)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Purpose - The purpose of this paper is to explore query expansion via conceptual distance in thesaurus indexed collections Design/methodology/approach - An extract of the National Museum of Science and Industry's collections database, indexed with the Getty Art and Architecture Thesaurus (AAT), was the dataset for the research. The system architecture and algorithms for semantic closeness and the matching function are outlined. Standalone and web interfaces are described and formative qualitative user studies are discussed. One user session is discussed in detail, together with a scenario based on a related public inquiry. Findings are set in context of the literature on thesaurus-based query expansion. This paper discusses the potential of query expansion techniques using the semantic relationships in a faceted thesaurus. Findings - Thesaurus-assisted retrieval systems have potential for multi-concept descriptors, permitting very precise queries and indexing. However, indexer and searcher may differ in terminology judgments and there may not be any exactly matching results. The integration of semantic closeness in the matching function permits ranked results for multi-concept queries in thesaurus-indexed applications. An in-memory representation of the thesaurus semantic network allows a combination of automatic and interactive control of expansion and control of expansion on individual query terms. Originality/value - The application of semantic expansion to browsing may be useful in interface options where thesaurus structure is hidden.

0.012201506 = product of:
  0.024403011 = sum of:
    0.024403011 = product of:
      0.048806023 = sum of:
        0.048806023 = weight(_text_:22 in 1026) [ClassicSimilarity], result of:
          0.048806023 = score(doc=1026,freq=2.0), product of:
            0.18020853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05146125 = queryNorm
            0.2708308 = fieldWeight in 1026, 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=1026)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Research and advanced technology for digital libraries : 7th European Conference, proceedings / ECDL 2003, Trondheim, Norway, August 17-22, 2003

0.012201506 = product of:
  0.024403011 = sum of:
    0.024403011 = product of:
      0.048806023 = sum of:
        0.048806023 = weight(_text_:22 in 1852) [ClassicSimilarity], result of:
          0.048806023 = score(doc=1852,freq=2.0), product of:
            0.18020853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05146125 = queryNorm
            0.2708308 = fieldWeight in 1852, 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=1852)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

11. 2.2011 18:22:58

0.011276226 = product of:
  0.022552451 = sum of:
    0.022552451 = product of:
      0.045104902 = sum of:
        0.045104902 = weight(_text_:network in 175) [ClassicSimilarity], result of:
          0.045104902 = score(doc=175,freq=2.0), product of:
            0.22917621 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.05146125 = queryNorm
            0.1968132 = fieldWeight in 175, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.03125 = fieldNorm(doc=175)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

There are many advantages for Digital Libraries in indexing with classifications or thesauri, but some current disincentive in the lack of flexible retrieval tools that deal with compound descriptors. This demonstration of a research prototype illustrates a matching function for compound descriptors, or multi-concept subject headings, that does not rely on exact matching but incorporates term expansion via thesaurus semantic relationships to produce ranked results that take account of missing and partially matching terms. The matching function is based on a measure of semantic closeness between terms.The work is part of the EPSRC funded FACET project in collaboration with the UK National Museum of Science and Industry (NMSI) which includes the National Railway Museum. An export of NMSI's Collections Database is used as the dataset for the research. The J. Paul Getty Trust's Art and Architecture Thesaurus (AAT) is the main thesaurus in the project. The AAT is a widely used thesaurus (over 120,000 terms). Descriptors are organised in 7 facets representing separate conceptual classes of terms.The FACET application is a multi tiered architecture accessing a SQL Server database, with an OLE DB connection. The thesauri are stored as relational tables in the Server's database. However, a key component of the system is a parallel representation of the underlying semantic network as an in-memory structure of thesaurus concepts (corresponding to preferred terms). The structure models the hierarchical and associative interrelationships of thesaurus concepts via weighted poly-hierarchical links. Its primary purpose is real-time semantic expansion of query terms, achieved by a spreading activation semantic closeness algorithm. Queries with associated results are stored persistently using XML format data. A Visual Basic interface combines a thesaurus browser and an initial term search facility that takes into account equivalence relationships. Terms are dragged to a direct manipulation Query Builder which maintains the facet structure.

0.010458433 = product of:
  0.020916866 = sum of:
    0.020916866 = product of:
      0.041833732 = sum of:
        0.041833732 = weight(_text_:22 in 2419) [ClassicSimilarity], result of:
          0.041833732 = score(doc=2419,freq=2.0), product of:
            0.18020853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05146125 = queryNorm
            0.23214069 = fieldWeight in 2419, 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=2419)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

16.11.2008 16:22:48

0.008715361 = product of:
  0.017430723 = sum of:
    0.017430723 = product of:
      0.034861445 = sum of:
        0.034861445 = weight(_text_:22 in 1428) [ClassicSimilarity], result of:
          0.034861445 = score(doc=1428,freq=2.0), product of:
            0.18020853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05146125 = queryNorm
            0.19345059 = fieldWeight in 1428, 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=1428)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 3.2003 19:35:46

0.008715361 = product of:
  0.017430723 = sum of:
    0.017430723 = product of:
      0.034861445 = sum of:
        0.034861445 = weight(_text_:22 in 56) [ClassicSimilarity], result of:
          0.034861445 = score(doc=56,freq=2.0), product of:
            0.18020853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05146125 = queryNorm
            0.19345059 = fieldWeight in 56, 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=56)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 7.2006 16:32:43

0.006972289 = product of:
  0.013944578 = sum of:
    0.013944578 = product of:
      0.027889157 = sum of:
        0.027889157 = weight(_text_:22 in 1163) [ClassicSimilarity], result of:
          0.027889157 = score(doc=1163,freq=2.0), product of:
            0.18020853 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05146125 = queryNorm
            0.15476047 = fieldWeight in 1163, 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=1163)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism, and Security, SIAM Data Mining Conference, Bethesda, MD, 20-22 April, 2006. [http://www.siam.org/meetings/sdm06/workproceed/Link%20Analysis/15.pdf]