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- 135 creator blai-bonet.
- 135 creator daniel-izquierdo.
- 135 creator maria-esther-vidal.
- 135 type InProceedings.
- 135 label "An Expressive and Efficient Solution to the Service Selection Problem".
- 135 sameAs 135.
- 135 abstract "Given the large number of Semantic Web Services that can be created from online sources by using existing annotation tools, expressive formalisms and efficient and scalable approaches to solve the service selection problem are required to make these services widely available to the users. In this paper, we propose a framework that is grounded on logic and the Local-As-View approach for representing instances of the service selection problem. In our approach,Web services are semantically described using LAV mappings in terms of generic concepts from an ontology, user requests correspond to conjunctive queries on the generic concepts and, in addition, the user may specify a set of preferences that are used to rank the possible solutions to the given request. The LAV formulation allows us to cast the service selection problem as a query rewriting problem that must consider the relationships among the concepts in the ontology and the ranks induced by the preferences. Then, building on related work, we devise an encoding of the resulting query rewriting problem as a logical theory whose models are in correspondence with the solutions of the user request, and in presence of preferences, whose best models are in correspondence with the best-ranked solutions. Thus, by exploiting known properties of modern SAT solvers, we provide an efficient and scalable solution to the service selection problem. The approach provides the basis to represent a large number of real-world situations and interesting user requests.".
- 135 hasAuthorList authorList.
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- 135 keyword "semantic web".
- 135 title "An Expressive and Efficient Solution to the Service Selection Problem".
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- item-1 hasContent daniel-izquierdo.
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- item-2 hasContent maria-esther-vidal.
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- 144 creator kostis-kyzirakos.
- 144 creator manolis-koubarakis.
- 144 creator zoi-kaoudi.
- 144 type InProceedings.
- 144 label "SPARQL Query Optimization on Top of DHTs".
- 144 sameAs 144.
- 144 abstract "We study the problem of SPARQL query optimization on top of distributed hash tables. Existing works on SPARQL query processing in such environments have never been implemented in a real system, or do not utilize any optimization techniques and thus exhibit poor performance. Our goal in this paper is to propose efficient and scalable algorithms for optimizing SPARQL basic graph pattern queries. We augment a known distributed query processing algorithm with query optimization strategies that improve performance in terms of query response time and bandwidth usage. We implement our techniques in the system Atlas and study their performance experimentally in a local cluster.".
- 144 hasAuthorList authorList.
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- 144 keyword "semantic web".
- 144 title "SPARQL Query Optimization on Top of DHTs".
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- item-1 hasContent zoi-kaoudi.
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- 146 creator clement-jonquet.
- 146 creator mark-a-musen.
- 146 creator natalya-noy.
- 146 creator nigam-shah.
- 146 creator paea-lependu.
- 146 creator paul-r-alexander.
- 146 type InProceedings.
- 146 label "Optimize First, Buy Later: Analyzing Metrics to Ramp-up Very Large Knowledge Bases".
- 146 sameAs 146.
- 146 abstract "As knowledge bases move into the landscape of larger ontologies and have terabytes of related data, we must work on optimizing the performance of our tools. We are easily tempted to buy bigger machines or to fill rooms with armies of little ones to address the scalability problem. Yet, careful analysis and evaluation of the characteristics of our data-using metrics-often leads to dramatic improvements in performance. Firstly, are current scalable systems scalable enough? We found that for large or deep ontologies (some as large as 500,000 classes) it is hard to say because benchmarks obscure the load-time costs for materialization. Therefore, to expose those costs, we have synthesized a set of more representative ontologies. Secondly, in designing for scalability, how do we manage knowledge over time? By optimizing for data distribution and ontology evolution, we have reduced the population time, including materialization, for the NCBO Resource Index, a knowledge base of 16.4 billion annotations linking 2.4 million terms from 200 ontologies to 3.5 million data elements, from one week to less than one hour for one of the large datasets on the same machine.".
- 146 hasAuthorList authorList.
- 146 isPartOf proceedings.
- 146 keyword "semantic web".
- 146 title "Optimize First, Buy Later: Analyzing Metrics to Ramp-up Very Large Knowledge Bases".
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