Matches in ScholarlyData for { ?s ?p ?o. }
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- item-1 hasContent carlos-pedrinaci.
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- item-2 hasContent john-domingue.
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- item-3 hasContent ana-karla-alves-de-medeiros.
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- 293 creator joerg-unbehauen.
- 293 creator sebastian-dietzold.
- 293 creator soeren-auer.
- 293 type InProceedings.
- 293 label "xOperator - Interconnecting the Semantic Web and Instant Messaging Networks".
- 293 sameAs 293.
- 293 abstract "Instant Messaging is in addition to Web and Email the most popular service on the Internet. With xOperator we present a strategy and implementation which deeply integrates Instant Messaging networks with the Semantic Web. The xOperator concept is based on the idea of creating an overlay network of collaborative information agents on top of social IM networks. It can be queried using a controlled and easily extensible language based on AIML templates. Such a deep integration of semantic technologies and Instant Messaging bears a number of advantages and benefits for users when compared to the separated use of Semantic Web technologies and IM, the most important ones being context awareness as well as provenance and trust. We showcase how the xOperator approach naturally facilitates contacts and calendar management as well as access to large scale heterogeneous information sources.".
- 293 hasAuthorList authorList.
- 293 hasTopic Collaborative_software.
- 293 hasTopic Computer_security.
- 293 hasTopic Data_integration.
- 293 hasTopic Privacy.
- 293 hasTopic Search_engine.
- 293 hasTopic Semantic_Web.
- 293 hasTopic Social_network.
- 293 hasTopic Software_agent.
- 293 hasTopic Trust_%28social_sciences%29.
- 293 hasTopic User_interface.
- 293 isPartOf proceedings.
- 293 keyword "agent".
- 293 keyword "instant messaging".
- 293 keyword "rdf".
- 293 keyword "sparql".
- 293 keyword "xmpp".
- 293 title "xOperator - Interconnecting the Semantic Web and Instant Messaging Networks".
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- item-1 hasContent sebastian-dietzold.
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- item-3 hasContent soeren-auer.
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- 3 creator andre-bolles.
- 3 creator jonas-jacobi.
- 3 creator marco-grawunder.
- 3 type InProceedings.
- 3 label "Streaming SPARQL - Extending SPARQL to process data streams".
- 3 sameAs 3.
- 3 abstract "A lot of work has been done in the area of data stream processing. Most of the previous approaches regard only relational or XML based streams but do not cover semantic richer RDF based stream elements. In our work, we extend SPARQL, the W3C recommendation for an RDF query language, to process RDF data streams. To describe the semantics of our enhancement, we extended the logical SPARQL algebra for stream processing on the foundation of a temporal relational algebra based on multi sets and provide an algorithm to transform SPARQL queries to the new extended algebra. For each logical algebra operator, we defined executable physical counterparts. To show the feasibility of our approach, we implemented it within our ODYSSEUS framework in the context of wind power plant monitoring.".
- 3 hasAuthorList authorList.
- 3 hasTopic Database_management_system.
- 3 hasTopic SPARQL.
- 3 hasTopic Search_engine.
- 3 hasTopic Semantic_Web.
- 3 isPartOf proceedings.
- 3 keyword "rdf".
- 3 keyword "sparql".
- 3 keyword "stream processing".
- 3 title "Streaming SPARQL - Extending SPARQL to process data streams".
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- item-1 hasContent andre-bolles.
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- 32 creator abraham-bernstein.
- 32 creator andre-locher.
- 32 creator christoph-kiefer.
- 32 type InProceedings.
- 32 label "Adding Data Mining Support to SPARQL via Statistical Relational Learning Methods".
- 32 sameAs 32.
- 32 abstract "In machine learning/data mining, people have been exploring how to learn models of relational data for a long time. The rational behind this is that exploiting the rich and complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. These links are usually hard to model by traditional propositional learning techniques. We extend this idea to the Semantic Web. In this paper we introduce a novel approach we call SPARQL-ML to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers. We analyze our approach thoroughly conducting three sets of experiments on synthetic as well as real-world datasets. Our analytical results show that our approach can be used for any Semantic Web dataset to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines shows that our approach is superior in terms of classification accuracy. Moreover, we show how our approach can be used for Semantic Web service classification and automatic semantic annotation.".
- 32 hasAuthorList authorList.
- 32 hasTopic Machine_learning.
- 32 hasTopic SPARQL.
- 32 hasTopic Semantic_Web.
- 32 hasTopic Semantic_Web_Services.
- 32 isPartOf proceedings.
- 32 keyword "data mining".
- 32 keyword "evaluation".