Search ALOE

{"m_searchStringConjunction":"OR","m_searchStrings":{"aloe_tagValue":"ranking"}}
Perform faceted search

Search Results

We found 7 Resources matching ranking

Resources 1-7:

Showing 10 20 30 50 100 items per page
Sort by:
  • Algorithm Measures Human Pecking Order  - Technology Review

    Views: 316 Average Rating:
    •  
    Added by Martin on 2012-01-04 09:34:43.0


    Algorithm Measures Human Pecking Order  - Technology Review The way people copy each other's linguistic style reveals their pecking order.
  • An Enhanced Indexing And Ranking Technique On The Semantic Web

    Views: 270 Average Rating:
    •  
    Added by benbanbun on 2011-12-01 09:38:09.0


    An Enhanced Indexing And Ranking Technique On The Semantic Web With the fast growth of the Internet, more and more information is available on the Web. The Semantic Web has many features which cannot be handled by using the traditional search engines. It extracts metadata for each discovered Web documents in RDF or OWL formats, and computes relations between documents. We proposed a hybrid indexing and ranking technique for the Semantic Web which finds relevant documents and computes the similarity among a set of documents. First, it returns with the most related document from the repository of Semantic Web Documents (SWDs) by using a modified version of the ObjectRank technique. Then, it creates a sub-graph for the most related SWDs. Finally, It returns the hubs and authorities of these document by using the HITS algorithm. Our technique increases the quality of the results and decreases the execution time of processing the user's query. Ahmed Tolba, Nabila Eladawi, Mohammed Elmogy
  • A SURVEY OF EIGENVECTOR METHODS FOR WEB INFORMATION RETRIEVAL

    Views: 798 Average Rating:
    •  
    Added by benbanbun on 2010-11-12 10:37:19.0


    A SURVEY OF EIGENVECTOR METHODS FOR WEB INFORMATION RETRIEVAL Web information retrieval is significantly more challenging than traditional well controlled, small document collection information retrieval. One main difference between traditional information retrieval and Web information retrieval is the Web's hyperlink structure. This structure has been exploited by several of today's leading Web search engines, particularly Google and Teoma. In this survey paper, we focus on Web information retrieval methods that use eigenvector computations, presenting the three popular methods of HITS, PageRank, and SALSA. AMY N. LANGVILLE, CARL D. MEYER
  • TripleRank: Ranking Semantic Web Data By Tensor Decomposition

    Views: 168 Average Rating:
    •  
    Added by benbanbun on 2010-11-09 11:25:13.0


    TripleRank: Ranking Semantic Web Data By Tensor Decomposition The Semantic Web fosters novel applications targeting a more efficient and satisfying exploitation of the data available on the web, e.g. faceted browsing of linked open data. Large amounts and high diversity of knowledge in the Semantic Web pose the challenging question of appropriate relevance ranking for producing fine-grained and rich descriptions of the available data, e.g. to guide the user along most promising knowledge aspects. Existing methods for graph-based authority ranking lack support for fine-grained latent coherence between resources and predicates (i.e. support for link semantics in the linked data model). In this paper, we present TripleRank, a novel approach for faceted authority ranking in the context of RDF knowledge bases. TripleRank captures the additional latent semantics of Semantic Web data by means of statistical methods in order to produce richer descriptions of the available data. We model the Semantic Web by a 3-dimensional tensor that enables the seamless representation of arbitrary semantic links. For the analysis of that model, we apply the PARAFAC decomposition, which can be seen as a multi-modal counterpart to Web authority ranking with HITS. The result are groupings of resources and predicates that characterize their authority and navigational (hub) properties with respect to identified topics. We have applied TripleRank to multiple data sets from the linked open data community and gathered encouraging feedback in a user evaluation where TripleRank results have been exploited in a faceted browsing scenario. Thomas Franz, Antje Schultz, Sergej Sizov, Steffen Staab
  • Using Naming Authority to Rank Data and Ontologies for Web Search

    Views: 198 Average Rating:
    •  
    Added by benbanbun on 2010-11-09 11:16:12.0


    Using Naming Authority to Rank Data and Ontologies for Web Search The focus of web search is moving away from returning relevant documents towards returning structured data as results to user queries. A vital part in the architecture of search engines are link-based ranking algorithms, which however are targeted towards hypertext documents. Existing ranking algorithms for structured data, on the other hand, require manual input of a domain expert and are thus not applicable in cases where data integrated from a large number of sources exhibits enormous variance in vocabularies used. In such environments, the authority of data sources is an important signal that the ranking algorithm has to take into account. This paper presents algorithms for prioritising data returned by queries over web datasets expressed in RDF. We introduce the notion of naming authority which provides a correspondence between identifiers and the sources which can speak authoritatively for these identifiers. Our algorithm uses the original PageRank method to assign authority values to data sources based on a naming authority graph, and then propagates the authority values to identifiers referenced in the sources. We conduct performance and quality evaluations of the method on a large web dataset. Our method is schema-independent, requires no manual input, and has applications in search, query processing, reasoning, and user interfaces over integrated datasets. Andreas Harth, Sheila Kinsella, Stefan Decker
  • Integrating the Probabilistic Model BM25/BM25F into Lucene.

    Views: 263 Average Rating:
    •  
    Added by Rafael on 2010-01-28 13:18:17.0


    Integrating the Probabilistic Model BM25/BM25F into Lucene.
  • PostRank™ Analytics

    Views: 230 Average Rating:
    •  
    Added by Martin on 2009-09-24 18:10:18.0


    PostRank™ Analytics