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Ryan S. DhindsaAnthony W. ZoghbiDaniel KrizayChirag VasavdaDavid GoldsteinDavid T. LynchMelanie ChinMartin B. DelatyckiS. H. SubramonyManuela CortiJennifer HoyleSylvia BoeschWolfgang NachbauerCaterina MariottiKatherine D. MathewsPaola GiuntiGeorge WilmotTheresa A. ZesiewiczSusan PerlmanAngie GoldsberryMegan O’GradyColin J. MeyerChih-Hong LeeJohn T. LeCarlos J. Ballester‐RosadoAnne E. AndersonJohn W. SwannNicolas Martinez‐MajanderVille ArttoPauli YlikotilaBettina von SarnowskiUlrike Waje‐AndreassenNilüfer YeşilotMarialuisa ZeddeJuha HuhtakangasHeikki NumminenJäkälä PekkaAna Maria FonsecaPetra RedforsMarieke J.H. WermerAlessandro PezziniJukka PutaalaSecreto StudyKarin L. MeekerJulie K. WischDarrell HudsonDean W. CobleChengjie XiongGanesh M. BabulalBrian GordonSuzanne E. SchindlerCarlos CruchagaShaney FloresAylin DincerTammie L.S. BenzingerJohn MorrisBeau M. Ances
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Canada & Mexico), €1286.00(Europe), £1020.00(UK), US$1993.00 (Rest of World).Prices are exclusive of tax.Asia-Pacific GST,Keywords:
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Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text , "Annals of Physics," J. Opt. Soc. Am. 47, 258_1-258 (1957) Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
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XML Schema (W3C)
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As an important branch of semantic Web,the semantic desktop can be used to automatically extract the metadata of desktop documents when users are working with their PCs. The metadata can be used to record the characteristics of documents including the general characteristics and the characteristics related to user actions,which is helpful for retrieval. This paper provides a new document retrieval method based on conceptual graphs in the environment of semantic desktop. This new method effectively builds a document indexing structure for helping fast retrieval by using the metadata provided by the semantic desktop. According to experiments,the time performance is improved compared to the previous algorithm of the projection based on the inference process. The experimental results prove the feasibility of the approach as for retrieval time performance.
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Phrase
Thesaurus
Automatic indexing
Document Clustering
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The expansion of structured information in different applications introduces a new ambiguity in multimedia retrieval in semi-structured documents. We investigate in this paper the combination of textual and structural context for multimedia retrieval in XML document thus we present a indexing model which combines textual and structural information. We propose a geometric method who use implicitly of textual and structural context of XML elements and we are particularly interested by improve the effectiveness of various structural factors for multimedia retrieval. Using a geometric metric, we can represent structural information in XML document with a vector for each element. Given a textual query, our model lets us combine scores obtained from each sources of evidence and return a list of relevant retrieved multimedia element. Experimental evaluation is carried out using the INEX Ad Hoc Task 2007 and the Image CLEF Wikipedia Retrieval Task 2010. The results show that combination of scores of textual modality and structural modality significantly improves compared results of using a single modality.
Modality (human–computer interaction)
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Abstraction
Lexical Database
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Content-based Semantic Web retrieval only considers the content itself,without taking into account the different users,and it can not accurately reflect user needs.Aiming at the problem,this paper proposes a framework for adaptive semantic Web retrieval.For Chinese documents on the Web,it gives a kind of ontology learning method with HowNet,builds user information database by extracting the user objective,explicit and implicit information,designs the initial query ontology and personalized query ontology construction algorithm,and ultimately achieves the user's adaptive retrieval.Result shows the method has higher retrieval efficiency.
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Semantic Search
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Problem statement: Search queries are short and ambiguous and are insufficient for specifying precise user needs. To overcome this problem, some search engines suggest terms that are semantically related to the submitted queries, so that users can choose from the suggestions based on their information needs. Approach: In this study, we introduce an effective approach that captures the user’s specific context by using the WordNet based semantic relatedness measure and the measures of joint keyword occurrences in the web page. Results: The context of the user query is identified and formulated. The user query is enriched to get more relevant web pages that the user needs. Conclusion: Experimental results show that our approach has better precision and recall than the existing methods.
Semantic Search
Information needs
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Get PDF Email Share Share with Facebook Tweet This Post on reddit Share with LinkedIn Add to CiteULike Add to Mendeley Add to BibSonomy Get Citation Copy Citation Text , "The Annals of the International Geophysical Year," J. Opt. Soc. Am. 47, 978_2-978 (1957) Export Citation BibTex Endnote (RIS) HTML Plain Text Citation alert Save article
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