Matches in ScholarlyData for { ?s ?p ?o. }
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- item-1 hasContent nadeschda-nikitina.
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- item-2 hasContent sven-schewe.
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- paper-27 type InProceedings.
- paper-27 label "FedSearch: efficiently combining structured queries and full-text search in a SPARQL federation".
- paper-27 sameAs paper-27.
- paper-27 abstract "Combining structured queries with full-text search provides a powerful means to access distributed linked data. However, executing hybrid search queries in a federation of multiple data sources presents a number of challenges due to data source heterogeneity and lack of statistical data about keyword selectivity. To address these challenges, we present FedSearch – a novel hybrid query engine based on the SPARQL federation framework FedX. We extend the SPARQL algebra to incorporate keyword search clauses as first-class citizens and apply novel optimization techniques to improve the query processing efficiency while maintaining a meaningful ranking of results. By performing on-the-fly adaptation of the query execution plan and intelligent grouping of query clauses, we are able to reduce significantly the communication costs making our approach suitable for top-k hybrid search across multiple data sources. In experiments we demonstrate that our optimization techniques can lead to a substantial performance improvement, reducing the execution time of hybrid queries by more than an order of magnitude.".
- paper-27 hasAuthorList authorList.
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- paper-27 title "FedSearch: efficiently combining structured queries and full-text search in a SPARQL federation".
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- item-1 hasContent andriy-nikolov.
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- item-2 hasContent andreas-schwarte.
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- item-3 type ListItem.
- item-3 hasContent christian-huetter.
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- paper-28 type InProceedings.
- paper-28 label "Getting Lucky in Ontology Search: A Data-Driven Evaluation Framework for Ontology Ranking".
- paper-28 sameAs paper-28.
- paper-28 abstract "With hundreds, if not thousands, of ontologies available today in many different domains, ontology search and ranking has become an important and timely problem. When a user searches a collection of ontologies for her terms of interest, there are often dozens of ontologies that contain these terms. How does she know which ontology is the most relevant to her search? Our research group hosts BioPortal, a public repository of more than 330 ontologies in the biomedical domain. When a term that a user searches for is available in multiple ontologies, how do we rank the results and how do we measure how well our ranking works? In this paper, we develop an evaluation framework that enables developers to compare and analyze the performance of different ontology-ranking methods. Our framework is based on processing search logs and determining how often users select the top link that the search engine offers. We evaluate our framework by analyzing the data on BioPortal searches. We explore several different ranking algorithms and measure the effectiveness of each ranking by measuring how often users click on the highest ranked ontology. We collected log data from more than 4,800 BioPortal searches. Our results show that regardless of the ranking, in more than half the searches, users select the first link. Thus, it is even more critical to ensure that the ranking is appropriate if we want to have satisfied users. Our further analysis demonstrates that ranking ontologies based on page view data significantly improves the user experience, with an approximately 26% increase in the number of users who select the highest ranked ontology for the search.".
- paper-28 hasAuthorList authorList.
- paper-28 isPartOf proceedings-1.
- paper-28 title "Getting Lucky in Ontology Search: A Data-Driven Evaluation Framework for Ontology Ranking".
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- item-1 hasContent natasha-f-noy.
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- item-2 hasContent paul-alexander.
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- item-3 hasContent rave-harpaz.
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- item-4 hasContent trish-whetzel.
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- item-5 hasContent raymond-fergerson.
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- item-6 hasContent mark-musen.
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- paper-29 type InProceedings.
- paper-29 label "Exploring Scholarly Data with Rexplore".
- paper-29 sameAs paper-29.
- paper-29 abstract "Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors ‘semantically’ (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. ‘ordinary’ users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves.".
- paper-29 hasAuthorList authorList.
- paper-29 isPartOf proceedings-1.
- paper-29 title "Exploring Scholarly Data with Rexplore".
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- item-1 hasContent francesco-osborne.
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- item-2 hasContent enrico-motta.
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- item-3 hasContent paul-mulholland.
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- paper-30 type InProceedings.
- paper-30 label "Personalized Best Answer Computation in Graph Databases".
- paper-30 sameAs paper-30.
- paper-30 abstract "Though subgraph matching has been extensively studied as a query paradigm in semantic web and social network data environments, a user can get a large number of answers in response to a query. Just like Google does, these answers can be shown to the user in accordance with an importance ranking. In this paper, we present scalable algorithms to find the top-K answers to a practically important subset of SPARQL-queries, denoted as importance queries, via a suite of pruning techniques. We test our algorithms on multiple real-world graph data sets, showing that our algorithms are efficient even on networks with up to 6M vertices and 15M edges and far more efficient than popular triple stores.".
- paper-30 hasAuthorList authorList.