Matches in ScholarlyData for { ?s ?p ?o. }
- item-6 type ListItem.
- item-6 hasContent rik-van-de-walle.
- item-6 previous item-5.
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasItem item-5.
- authorList hasLastItem item-5.
- item-1 type ListItem.
- item-1 hasContent saad-malik-missen.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent mohammed-attik.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent mickael-coustaty.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent antoine-doucet.
- item-4 next item-5.
- item-4 previous item-3.
- item-5 type ListItem.
- item-5 hasContent cyril-faucher.
- item-5 previous item-4.
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasLastItem item-4.
- item-1 type ListItem.
- item-1 hasContent lei-zhang.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent wentao-chen.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent thanh-tran.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent achim-rettinger.
- item-4 previous item-3.
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent mayank-kejriwal.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent daniel-p-miranker.
- item-2 previous item-1.
- 11 creator heiko-paulheim.
- 11 creator robert-meusel.
- 11 type InProceedings.
- 11 label "Heuristics for Fixing Common Errors in Deployed schema.org Microdata".
- 11 sameAs 11.
- 11 abstract "Being promoted by major search engines such as Google, Yahoo!, Bing, and Yandex, Microdata embedded in web pages, especially using schema.org, has become one of the most important markup languages for the Web. However, actually deployed Microdata is most often not free from errors, which limits its practical use. In this paper, we use the WebDataCommons corpus of Microdata extracted from more than 250 million web pages for a quantitative analysis of common mistakes in Microdata provision. Since it is unrealistic that data providers will fix all the mistakes and provide clean and correct data, we discuss a set of heuristics that can be applied on the data consumer side to fix many of those mistakes in a post-processing step. We apply those heuristics to provide an improved Microdata corpus.".
- 11 hasAuthorList authorList.
- 11 isPartOf proceedings.
- 11 keyword "Data Quality".
- 11 keyword "Microdata".
- 11 keyword "schema.org".
- 11 title "Heuristics for Fixing Common Errors in Deployed schema.org Microdata".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent robert-meusel.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent heiko-paulheim.
- item-2 previous item-1.
- 116 creator amol-mittal.
- 116 creator craig-a-knoblock.
- 116 creator pedro-szekely.
- 116 creator ramnandan-krishnamurthy.
- 116 type InProceedings.
- 116 label "Assigning Semantic Labels to Data Sources".
- 116 sameAs 116.
- 116 abstract "There is a huge demand to be able to integrate heterogeneous data sources, which requires mapping the attributes of a source to the concepts and relationships defined in a domain ontology. In this paper, we present a new approach to the problem of identifying these mappings, which we call semantic labeling. Previous approaches map each data value individually, typically by learning a model based on features extracted from the data using supervised machine learning techniques. Our approach differs from existing approaches in that we take a holistic view of the data values corresponding to a semantic label and use techniques that treat this data collectively, which makes it possible to capture characteristic properties of the values associated with a semantic label as a whole. Our approach supports both textual and numeric data and proposes the top-k semantic labels along with their associated confidence scores. Our experiments show that the approach has higher label prediction accuracy, has lower time complexity, and is more scalable than existing systems.".
- 116 hasAuthorList authorList.
- 116 isPartOf proceedings.
- 116 keyword "Semantic Web".
- 116 keyword "Semantic labeling".
- 116 keyword "Source modeling".
- 116 title "Assigning Semantic Labels to Data Sources".
- authorList type List.
- authorList hasFirstItem item-1.