Search (53 results, page 1 of 3)

0.047668897 = product of:
  0.09533779 = sum of:
    0.0066587473 = product of:
      0.02663499 = sum of:
        0.02663499 = weight(_text_:based in 1634) [ClassicSimilarity], result of:
          0.02663499 = score(doc=1634,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.18831211 = fieldWeight in 1634, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.03125 = fieldNorm(doc=1634)
      0.25 = coord(1/4)
    0.088679045 = sum of:
      0.063238226 = weight(_text_:assessment in 1634) [ClassicSimilarity], result of:
        0.063238226 = score(doc=1634,freq=2.0), product of:
          0.25917634 = queryWeight, product of:
            5.52102 = idf(docFreq=480, maxDocs=44218)
            0.04694356 = queryNorm
          0.2439969 = fieldWeight in 1634, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            5.52102 = idf(docFreq=480, maxDocs=44218)
            0.03125 = fieldNorm(doc=1634)
      0.025440816 = weight(_text_:22 in 1634) [ClassicSimilarity], result of:
        0.025440816 = score(doc=1634,freq=2.0), product of:
          0.16438834 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.04694356 = queryNorm
          0.15476047 = fieldWeight in 1634, 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=1634)
  0.5 = coord(2/4)

Abstract

Purpose - Ontologies are prone to wide semantic variability due to subjective points of view of their composers. The purpose of this paper is to propose a new approach for maximal unification of diverse ontologies for controversial domains by their relations. Design/methodology/approach - Effective matching or unification of multiple ontologies for a specific domain is crucial for the success of many semantic web applications, such as semantic information retrieval and organization, document tagging, summarization and search. To this end, numerous automatic and semi-automatic techniques were proposed in the past decade that attempt to identify similar entities, mostly classes, in diverse ontologies for similar domains. Apparently, matching individual entities cannot result in full integration of ontologies' semantics without matching their inter-relations with all other-related classes (and instances). However, semantic matching of ontological relations still constitutes a major research challenge. Therefore, in this paper the authors propose a new paradigm for assessment of maximal possible matching and unification of ontological relations. To this end, several unification rules for ontological relations were devised based on ontological reference rules, and lexical and textual entailment. These rules were semi-automatically implemented to extend a given ontology with semantically matching relations from another ontology for a similar domain. Then, the ontologies were unified through these similar pairs of relations. The authors observe that these rules can be also facilitated to reveal the contradictory relations in different ontologies. Findings - To assess the feasibility of the approach two experiments were conducted with different sets of multiple personal ontologies on controversial domains constructed by trained subjects. The results for about 50 distinct ontology pairs demonstrate a good potential of the methodology for increasing inter-ontology agreement. Furthermore, the authors show that the presented methodology can lead to a complete unification of multiple semantically heterogeneous ontologies. Research limitations/implications - This is a conceptual study that presents a new approach for semantic unification of ontologies by a devised set of rules along with the initial experimental evidence of its feasibility and effectiveness. However, this methodology has to be fully automatically implemented and tested on a larger dataset in future research. Practical implications - This result has implication for semantic search, since a richer ontology, comprised of multiple aspects and viewpoints of the domain of knowledge, enhances discoverability and improves search results. Originality/value - To the best of the knowledge, this is the first study to examine and assess the maximal level of semantic relation-based ontology unification.

Date

20. 1.2015 18:30:22

0.032109328 = product of:
  0.064218655 = sum of:
    0.008323434 = product of:
      0.033293735 = sum of:
        0.033293735 = weight(_text_:based in 2192) [ClassicSimilarity], result of:
          0.033293735 = score(doc=2192,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.23539014 = fieldWeight in 2192, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2192)
      0.25 = coord(1/4)
    0.055895224 = product of:
      0.11179045 = sum of:
        0.11179045 = weight(_text_:assessment in 2192) [ClassicSimilarity], result of:
          0.11179045 = score(doc=2192,freq=4.0), product of:
            0.25917634 = queryWeight, product of:
              5.52102 = idf(docFreq=480, maxDocs=44218)
              0.04694356 = queryNorm
            0.43132967 = fieldWeight in 2192, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.52102 = idf(docFreq=480, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2192)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

This book constitutes the refereed proceedings of the 8th Metadata and Semantics Research Conference, MTSR 2014, held in Karlsruhe, Germany, in November 2014. The 23 full papers and 9 short papers presented were carefully reviewed and selected from 57 submissions. The papers are organized in several sessions and tracks. They cover the following topics: metadata and linked data: tools and models; (meta) data quality assessment and curation; semantic interoperability, ontology-based data access and representation; big data and digital libraries in health, science and technology; metadata and semantics for open repositories, research information systems and data infrastructure; metadata and semantics for cultural collections and applications; semantics for agriculture, food and environment.

Content

Metadata and linked data.- Tools and models.- (Meta)data quality assessment and curation.- Semantic interoperability, ontology-based data access and representation.- Big data and digital libraries in health, science and technology.- Metadata and semantics for open repositories, research information systems and data infrastructure.- Metadata and semantics for cultural collections and applications.- Semantics for agriculture, food and environment.

0.024938494 = product of:
  0.049876988 = sum of:
    0.0047084456 = product of:
      0.018833783 = sum of:
        0.018833783 = weight(_text_:based in 4796) [ClassicSimilarity], result of:
          0.018833783 = score(doc=4796,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.13315678 = fieldWeight in 4796, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.03125 = fieldNorm(doc=4796)
      0.25 = coord(1/4)
    0.04516854 = weight(_text_:term in 4796) [ClassicSimilarity], result of:
      0.04516854 = score(doc=4796,freq=2.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.20621133 = fieldWeight in 4796, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.03125 = fieldNorm(doc=4796)
  0.5 = coord(2/4)

Abstract

Key recommendations of the report are: - That library leaders identify sets of data as possible candidates for early exposure as Linked Data and foster a discussion about Open Data and rights; - That library standards bodies increase library participation in Semantic Web standardization, develop library data standards that are compatible with Linked Data, and disseminate best-practice design patterns tailored to library Linked Data; - That data and systems designers design enhanced user services based on Linked Data capabilities, create URIs for the items in library datasets, develop policies for managing RDF vocabularies and their URIs, and express library data by re-using or mapping to existing Linked Data vocabularies; - That librarians and archivists preserve Linked Data element sets and value vocabularies and apply library experience in curation and long-term preservation to Linked Data datasets.

0.022482082 = product of:
  0.08992833 = sum of:
    0.08992833 = weight(_text_:frequency in 97) [ClassicSimilarity], result of:
      0.08992833 = score(doc=97,freq=2.0), product of:
        0.27643865 = queryWeight, product of:
          5.888745 = idf(docFreq=332, maxDocs=44218)
          0.04694356 = queryNorm
        0.32531026 = fieldWeight in 97, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.888745 = idf(docFreq=332, maxDocs=44218)
          0.0390625 = fieldNorm(doc=97)
  0.25 = coord(1/4)

Abstract

Robertson and Spärck Jones pioneered experimental probabilistic models (Binary Independence Model) with both a typology generalizing the Boolean model, a frequency counting to calculate elementary weightings, and their combination into a global probabilistic estimation. However, this model did not consider indexing terms dependencies. An extension to mixture models (e.g., using a 2-Poisson law) made it possible to take into account these dependencies from a macroscopic point of view (BM25), as well as a shallow linguistic processing of co-references. New approaches (language models, for example "bag of words" models, probabilistic dependencies between requests and documents, and consequently Bayesian inference using Dirichlet prior conjugate) furnished new solutions for documents structuring (categorization) and for index smoothing. Presently, in these probabilistic models the main issues have been addressed from a formal point of view only. Thus, linguistic properties are neglected in the indexing language. The authors examine how a linguistic and semantic modeling can be integrated in indexing languages and set up a hybrid model that makes it possible to deal with different information retrieval problems in a unified way.

0.019961866 = product of:
  0.07984746 = sum of:
    0.07984746 = weight(_text_:term in 4293) [ClassicSimilarity], result of:
      0.07984746 = score(doc=4293,freq=4.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.3645336 = fieldWeight in 4293, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4293)
  0.25 = coord(1/4)

Abstract

In recent years, the term "semantics" has been widely used in various fields of research and particularly in areas related to information technology. One of the motivators of such an appropriation is the vision of the Semantic Web, a set of developments underway, which might allow one to obtain better results when querying on the web. However, it is worth asking what kind of semantics we can find in the Semantic Web, considering that studying the subject is a complex and controversial endeavor. Working within this context, we present an account of semantics, relying on the main linguist approaches, in order to then analyze what semantics is within the scope of information technology. We critically evaluate a spectrum, which proposes the ordination of instruments (models, languages, taxonomic structures, to mention but a few) according to a semantic scale. In addition to proposing a new extended spectrum, we suggest alternative interpretations with the aim of clarifying the use of the term "semantics" in different contexts. Finally, we offer our conclusions regarding the semantic in the Semantic Web and mention future directions and complementary works.

0.014115169 = product of:
  0.056460675 = sum of:
    0.056460675 = weight(_text_:term in 99) [ClassicSimilarity], result of:
      0.056460675 = score(doc=99,freq=2.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.25776416 = fieldWeight in 99, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.0390625 = fieldNorm(doc=99)
  0.25 = coord(1/4)

Abstract

A new model is proposed to retrieve information by building automatically a semantic metatext structure for texts that allow searching and extracting discourse and semantic information according to certain linguistic categorizations. This paper presents approaches for searching and mining full text with semantic categories. The model is built up from two engines: The first one, called EXCOM (Djioua et al., 2006; Alrahabi, 2010), is an automatic system for text annotation, related to discourse and semantic maps, which are specification of general linguistic ontologies founded on the Applicative and Cognitive Grammar. The annotation layer uses a linguistic method called Contextual Exploration, which handles the polysemic values of a term in texts. Several 'semantic maps' underlying 'point of views' for text mining guide this automatic annotation process. The second engine uses semantic annotated texts, produced previously in order to create a semantic inverted index, which is able to retrieve relevant documents for queries associated with discourse and semantic categories such as definition, quotation, causality, relations between concepts, etc. (Djioua & Desclés, 2007). This semantic indexation process builds a metatext layer for textual contents. Some data and linguistic rules sets as well as the general architecture that extend third-party software are expressed as supplementary information.

0.014115169 = product of:
  0.056460675 = sum of:
    0.056460675 = weight(_text_:term in 266) [ClassicSimilarity], result of:
      0.056460675 = score(doc=266,freq=2.0), product of:
        0.21904005 = queryWeight, product of:
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.04694356 = queryNorm
        0.25776416 = fieldWeight in 266, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.66603 = idf(docFreq=1130, maxDocs=44218)
          0.0390625 = fieldNorm(doc=266)
  0.25 = coord(1/4)

Abstract

We present a survey of the current state of Simple Knowledge Organization System (SKOS) vocabularies on the Web. Candidate vocabularies were gathered through collections and web crawling, with 478 identified as complying to a given definition of a SKOS vocabulary. Analyses were then conducted that included investigation of the use of SKOS constructs; the use of SKOS semantic relations and lexical labels; and the structure of vocabularies in terms of the hierarchical and associative relations, branching factors and the depth of the vocabularies. Even though SKOS concepts are considered to be the core of SKOS vocabularies, our findings were that not all SKOS vocabularies published explicitly declared SKOS concepts in the vocabularies. Almost one-third of th SKOS vocabularies collected fall into the category of term lists, with no use of any SKOS semantic relations. As concept labelling is core to SKOS vocabularies, a surprising find is that not all SKOS vocabularies use SKOS lexical labels, whether skos:prefLabel or skos:altLabel, for their concepts. The branching factors and maximum depth of the vocabularies have no direct relationship to the size of the vocabularies. We also observed some common modelling slips found in SKOS vocabularies. The survey is useful when considering, for example, converting artefacts such as OWL ontologies into SKOS, where a definition of typicality of SKOS vocabularies could be used to guide the conversion. Moreover, the survey results can serve to provide a better understanding of the modelling styles of the SKOS vocabularies published on the Web, especially when considering the creation of applications that utilize these vocabularies.

0.010893034 = product of:
  0.021786068 = sum of:
    0.005885557 = product of:
      0.023542227 = sum of:
        0.023542227 = weight(_text_:based in 4553) [ClassicSimilarity], result of:
          0.023542227 = score(doc=4553,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.16644597 = fieldWeight in 4553, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4553)
      0.25 = coord(1/4)
    0.015900511 = product of:
      0.031801023 = sum of:
        0.031801023 = weight(_text_:22 in 4553) [ClassicSimilarity], result of:
          0.031801023 = score(doc=4553,freq=2.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.19345059 = fieldWeight in 4553, 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=4553)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.

Date

16.11.2018 14:22:01

0.007950256 = product of:
  0.031801023 = sum of:
    0.031801023 = product of:
      0.063602045 = sum of:
        0.063602045 = weight(_text_:22 in 2090) [ClassicSimilarity], result of:
          0.063602045 = score(doc=2090,freq=2.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.38690117 = fieldWeight in 2090, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=2090)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.0055651786 = product of:
  0.022260714 = sum of:
    0.022260714 = product of:
      0.04452143 = sum of:
        0.04452143 = weight(_text_:22 in 3283) [ClassicSimilarity], result of:
          0.04452143 = score(doc=3283,freq=2.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.2708308 = fieldWeight in 3283, 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=3283)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

0.0047701527 = product of:
  0.019080611 = sum of:
    0.019080611 = product of:
      0.038161222 = sum of:
        0.038161222 = weight(_text_:22 in 4649) [ClassicSimilarity], result of:
          0.038161222 = score(doc=4649,freq=2.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.23214069 = fieldWeight in 4649, 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=4649)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

26.12.2011 13:40:22

0.0047701527 = product of:
  0.019080611 = sum of:
    0.019080611 = product of:
      0.038161222 = sum of:
        0.038161222 = weight(_text_:22 in 662) [ClassicSimilarity], result of:
          0.038161222 = score(doc=662,freq=2.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.23214069 = fieldWeight in 662, 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=662)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 3.2013 19:29:20

0.0047701527 = product of:
  0.019080611 = sum of:
    0.019080611 = product of:
      0.038161222 = sum of:
        0.038161222 = weight(_text_:22 in 2024) [ClassicSimilarity], result of:
          0.038161222 = score(doc=2024,freq=2.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.23214069 = fieldWeight in 2024, 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=2024)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Source

Bulletin of the Association for Information Science and Technology. 41(2015) no.4, S.18-22

0.0047701527 = product of:
  0.019080611 = sum of:
    0.019080611 = product of:
      0.038161222 = sum of:
        0.038161222 = weight(_text_:22 in 3197) [ClassicSimilarity], result of:
          0.038161222 = score(doc=3197,freq=2.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.23214069 = fieldWeight in 3197, 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=3197)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

24. 8.2016 14:03:22

0.0039351755 = product of:
  0.015740702 = sum of:
    0.015740702 = product of:
      0.031481404 = sum of:
        0.031481404 = weight(_text_:22 in 1155) [ClassicSimilarity], result of:
          0.031481404 = score(doc=1155,freq=4.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.19150631 = fieldWeight in 1155, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1155)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

17.12.2013 12:51:22

0.0033293737 = product of:
  0.013317495 = sum of:
    0.013317495 = product of:
      0.05326998 = sum of:
        0.05326998 = weight(_text_:based in 3926) [ClassicSimilarity], result of:
          0.05326998 = score(doc=3926,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.37662423 = fieldWeight in 3926, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0625 = fieldNorm(doc=3926)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

Modern information retrieval systems advance user experience on the basis of concept-based rather than keyword-based query answering.

0.003180102 = product of:
  0.012720408 = sum of:
    0.012720408 = product of:
      0.025440816 = sum of:
        0.025440816 = weight(_text_:22 in 1626) [ClassicSimilarity], result of:
          0.025440816 = score(doc=1626,freq=2.0), product of:
            0.16438834 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04694356 = queryNorm
            0.15476047 = fieldWeight in 1626, 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=1626)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

20. 1.2015 18:30:22

0.003114344 = product of:
  0.012457376 = sum of:
    0.012457376 = product of:
      0.049829505 = sum of:
        0.049829505 = weight(_text_:based in 230) [ClassicSimilarity], result of:
          0.049829505 = score(doc=230,freq=14.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.35229972 = fieldWeight in 230, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.03125 = fieldNorm(doc=230)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

Currently, techniques for content description and query processing in Information Retrieval (IR) are based on keywords, and therefore provide limited capabilities to capture the conceptualizations associated with user needs and contents. Aiming to solve the limitations of keyword-based models, the idea of conceptual search, understood as searching by meanings rather than literal strings, has been the focus of a wide body of research in the IR field. More recently, it has been used as a prototypical scenario (or even envisioned as a potential "killer app") in the Semantic Web (SW) vision, since its emergence in the late nineties. However, current approaches to semantic search developed in the SW area have not yet taken full advantage of the acquired knowledge, accumulated experience, and technological sophistication achieved through several decades of work in the IR field. Starting from this position, this work investigates the definition of an ontology-based IR model, oriented to the exploitation of domain Knowledge Bases to support semantic search capabilities in large document repositories, stressing on the one hand the use of fully fledged ontologies in the semantic-based perspective, and on the other hand the consideration of unstructured content as the target search space. The major contribution of this work is an innovative, comprehensive semantic search model, which extends the classic IR model, addresses the challenges of the massive and heterogeneous Web environment, and integrates the benefits of both keyword and semantic-based search. Additional contributions include: an innovative rank fusion technique that minimizes the undesired effects of knowledge sparseness on the yet juvenile SW, and the creation of a large-scale evaluation benchmark, based on TREC IR evaluation standards, which allows a rigorous comparison between IR and SW approaches. Conducted experiments show that our semantic search model obtained comparable and better performance results (in terms of MAP and P@10 values) than the best TREC automatic system.

0.003058225 = product of:
  0.0122329 = sum of:
    0.0122329 = product of:
      0.0489316 = sum of:
        0.0489316 = weight(_text_:based in 3829) [ClassicSimilarity], result of:
          0.0489316 = score(doc=3829,freq=6.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.34595144 = fieldWeight in 3829, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=3829)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

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.

0.0029132022 = product of:
  0.011652809 = sum of:
    0.011652809 = product of:
      0.046611235 = sum of:
        0.046611235 = weight(_text_:based in 62) [ClassicSimilarity], result of:
          0.046611235 = score(doc=62,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.3295462 = fieldWeight in 62, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0546875 = fieldNorm(doc=62)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

The paper discusses the challenges of categorising documents and "items of the world" to promote knowledge sharing in large communities of interest. We present the DOCMA method (Document and Item-based Model for Action) dedicated to end-users who have minimal or no knowledge of information science. Community members can elicit structure and indexed business items stemming from their query including projects, actors, products, places of interest, and geo-situated objects. This hybrid method was applied in a collaborative Web portal in the field of sustainability for the past two years.