Data Portal @ linkeddatafragments.org

Matches in ScholarlyData for { ?s ?p ?o. }

• authorList hasLastItem item-2.
• item-1 type ListItem.
• item-1 hasContent oezguer-luetfue-oezcep.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent ralf-moeller.
• item-2 previous item-1.
• paper-19 type InProceedings.
• paper-19 label "Formal Verification of Data Provenance Records".
• paper-19 sameAs paper-19.
• paper-19 abstract "Data provenance is the history of derivation of a data artifact from its original sources. As the real-life provenance records can likely cover thousands of data items and derivation steps, one of the pressing challenges becomes development of formal frameworks for their automated verification. In this paper, we consider data expressed in standard Semantic Web ontology languages, such as OWL, and define a novel verification formalism called provenance specification logic, building on dynamic logic. We validate our proposal by modeling the test queries presented in The First Provenance Challenge, and conclude that the logic core of such queries can be successfully captured in our formalism.".
• paper-19 hasAuthorList authorList.
• paper-19 isPartOf proceedings-2.
• paper-19 title "Formal Verification of Data Provenance Records".
• authorList type List.
• authorList hasFirstItem item-1.
• authorList hasItem item-1.
• authorList hasItem item-2.
• authorList hasItem item-3.
• authorList hasLastItem item-3.
• item-1 type ListItem.
• item-1 hasContent szymon-klarman.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent stefan-schlobach.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent luciano-serafini.
• item-3 previous item-2.
• paper-20 type InProceedings.
• paper-20 label "Provenance for SPARQL queries".
• paper-20 sameAs paper-20.
• paper-20 abstract "Determining trust of data available in the Semantic Web is fundamental for applications and users, in particular for linked open data obtained from SPARQL endpoints. There exist several proposals in the literature to annotate SPARQL query results with values from abstract models, adapting the seminal works on provenance for annotated relational databases. We provide an approach capable of providing provenance information for a large and significant fragment of SPARQL 1.1, including for the first time the major non-monotonic constructs under multiset semantics. The approach is based on the translation of SPARQL into relational queries over annotated relations with values of the most general m-semiring, and in this way also refuting a claim in the literature that the OPTIONAL construct of SPARQL cannot be captured appropriately with the known abstract models.".
• paper-20 hasAuthorList authorList.
• paper-20 isPartOf proceedings-2.
• paper-20 title "Provenance for SPARQL queries".
• authorList type List.
• authorList hasFirstItem item-1.
• authorList hasItem item-1.
• authorList hasItem item-2.
• authorList hasItem item-3.
• authorList hasLastItem item-3.
• item-1 type ListItem.
• item-1 hasContent carlos-viegas-damasio.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent anastasia-analyti.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent grigoris-antoniou.
• item-3 previous item-2.
• paper-21 type InProceedings.
• paper-21 label "DeFacto - Deep Fact Validation".
• paper-21 sameAs paper-21.
• paper-21 abstract "One of the main tasks when creating and maintaining knowledge bases is to validate facts and provide sources for them in order to ensure correctness and traceability of the provided knowledge. So far, this task is often addressed by human curators in a three-step process: issuing appropriate keyword queries for the statement to check using standard search engines, retrieving potentially relevant documents and screening those documents for relevant content. The drawbacks of this process are manifold. Most importantly, it is very time-consuming as the experts have to carry out several search processes and must often read several documents. In this article, we present DeFacto (Deep Fact Validation) - an algorithm for validating facts by finding trustworthy sources for it on the Web. DeFacto aims to provide an effective way of validating facts by supplying the user with relevant excerpts of webpages as well as useful additional information including a score for the confidence DeFacto has in the correctness of the input fact.".
• paper-21 hasAuthorList authorList.
• paper-21 isPartOf proceedings-2.
• paper-21 title "DeFacto - Deep Fact Validation".
• 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 jens-lehmann.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent daniel-gerber.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent mohamed-morsey.
• item-3 next item-4.
• item-3 previous item-2.
• item-4 type ListItem.
• item-4 hasContent axel-cyrille-ngonga-ngomo.
• item-4 previous item-3.
• paper-22 type InProceedings.
• paper-22 label "A Machine Learning Approach for Instance Matching Based on Similarity Metrics".
• paper-22 sameAs paper-22.
• paper-22 abstract "The Linking Open Data (LOD) project is an ongoing effort to construct a global data space, i.e. the Web of Data. One important part of this project is to establish owl:sameAs links among structured data sources. Such links indicate equivalent instances that refer to the same real-world object. The problem of discovering owl:sameAs links between pairwise data sources is called instance matching. Most of the existing approaches addressing this problem rely on the quality of prior schema matching, which is not always good enough in the LOD scenario. In this paper, we propose a schema-independent instance-pair similarity metric based on several general descriptive features. We transform the instance matching problem to the binary classification problem and solve it by machine learning algorithms. Furthermore, we employ some transfer learning methods to utilize the existing owl:sameAs links in LOD to reduce the demand for labeled data. We carry out experiments on some datasets of OAEI2010. The results show that our method performs well on real-world LOD data and outperforms the participants of OAEI2010.".
• paper-22 hasAuthorList authorList.
• paper-22 isPartOf proceedings-2.
• paper-22 title "A Machine Learning Approach for Instance Matching Based on Similarity Metrics".
• 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 hasItem item-6.
• authorList hasLastItem item-6.
• item-1 type ListItem.
• item-1 hasContent shu-rong.
• item-1 next item-2.

• first
• previous
• next