Matches in ScholarlyData for { ?s ?p ?o. }
- 319 title "Signal/Collect: Graph Algorithms for the (Semantic) Web".
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- item-1 type ListItem.
- item-1 hasContent philip-stutz.
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- item-2 hasContent abraham-bernstein.
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- item-3 type ListItem.
- item-3 hasContent william-cohen.
- item-3 previous item-2.
- 334 creator craig-knoblock.
- 334 creator jose-luis-ambite.
- 334 creator rahul-parundekar.
- 334 type InProceedings.
- 334 label "Linking and Building Ontologies of Linked Data".
- 334 sameAs 334.
- 334 abstract "The Web of Linked Data is characterized by linking structured data from different sources using equivalence statements, such as owl:sameAs, as well as other types of linked properties. The ontologies behind these sources, however, remain unlinked. This paper describes an extensional approach to generate alignments between these ontologies. Specifically our algorithm produces equivalence and subsumption relationships between classes from ontologies of different Linked Data sources by exploring the space of hypotheses supported by the existing equivalence statements. We are also able to generate a complementary hierarchy of derived classes within an existing ontology or generate new classes for a second source where the ontology is not as refined as the first. We demonstrate empirically our approach using Linked Data sources from the geospatial, genetics, and zoology domains. Our algorithm discovered about 800 equivalences and 29,000 subset relationships in the alignment of five source pairs from these domains. Thus, we are able to model one Linked Data source in terms of another by aligning their ontologies and understand the semantic relationships between the two sources.".
- 334 hasAuthorList authorList.
- 334 isPartOf proceedings.
- 334 keyword "semantic web".
- 334 title "Linking and Building Ontologies of Linked Data".
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- item-1 type ListItem.
- item-1 hasContent rahul-parundekar.
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- item-2 type ListItem.
- item-2 hasContent craig-knoblock.
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- item-3 type ListItem.
- item-3 hasContent jose-luis-ambite.
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- 352 creator david-laniado.
- 352 creator peter-mika.
- 352 type InProceedings.
- 352 label "Making sense of Twitter".
- 352 sameAs 352.
- 352 abstract "Twitter enjoys enormous popularity as a microblogging service largely due to its simplicity. On the downside, there is little organization to the Twitterverse and making sense of the stream of messagespassing through the system has become a significant challenge for everyone involved. As a solution, Twitter users have adopted the convention ofadding a hash at the beginning of a word to turn it into a hashtag. Hashtags have become the means in Twitter to create threads of conversationand to build communities around particular interests.In this paper, we take a first look at whether hashtags behave as strongidentifiers, and thus whether they could serve as identifiers for the Semantic Web. We introduce some metrics that can help identify hashtagsthat show the desirable characteristics of strong identifiers. We look atthe various ways in which hashtags are used, and show through evaluation that our metrics can be applied to detect hashtags that representreal world entities.".
- 352 hasAuthorList authorList.
- 352 isPartOf proceedings.
- 352 keyword "semantic web".
- 352 title "Making sense of Twitter".
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- item-1 type ListItem.
- item-1 hasContent david-laniado.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent peter-mika.
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- 376 creator roger-castillo.
- 376 type InProceedings.
- 376 label "RDFMatView: Indexing RDF Data for SPARQL Queries".
- 376 sameAs 376.
- 376 abstract "The Semantic Web is now gaining momentum due to its efforts to create a universal medium for the exchange of semantically tagged data. The representation and querying of semantic data have been made by means of directed labelled graphs using RDF and SPARQL, standards which have been widely accepted by the scientific community. Currently, most implementations of RDF/SPARQL are based on relational database technology. But executing complex queries in these systems usually is rather slow due to the number of joins that need to be performed. In this article, we describe an indexing method using materialized SPARQL queries as indexes on RDF data sets to reduce the query processing time. We provide a formal definition of materialized SPARQL queries, a cost model to evaluate their impact on query performance, a storage scheme for the materialization, and an algorithm to find the optimal set of indexes given a query. We also introduce different approaches to integrate materialized queries into an existing SPARQL query engine.".
- 376 hasAuthorList authorList.
- 376 isPartOf proceedings.
- 376 keyword "indexing".
- 376 keyword "materialized queries".
- 376 keyword "rdf".
- 376 keyword "semantic web".
- 376 keyword "sparql".
- 376 title "RDFMatView: Indexing RDF Data for SPARQL Queries".
- authorList type List.
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- item-1 type ListItem.
- item-1 hasContent roger-castillo.
- 383 creator xin-liu.
- 383 type InProceedings.
- 383 label "A General Approach to Query the Web of Data".
- 383 sameAs 383.
- 383 abstract "With the development of the Semantic Web, an increasing amount of data with semantics has been published on the web according to the Linked Data principles and become ubiquitous. The requirement of utilizing the entire web of data to answer a query has arised since the desire for the application of such ubiquitous semantic data sources. However, limited research work has been made on the query above the web of data and there is not a formal way to describe the query processing procedure. In this paper, we propose a general query processing method on the web of data, which contains three steps: data inference configuration, data discovery, result generation and ranking. Finally, we briefly represent the work already done and the future work.".
- 383 hasAuthorList authorList.
- 383 isPartOf proceedings.
- 383 keyword "semantic web".
- 383 title "A General Approach to Query the Web of Data".
- authorList type List.
- authorList hasFirstItem item-1.
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- item-1 hasContent xin-liu.
- 385 creator amrapali-j-zaveri.