DELTA-R: a change detection approach for RDF datasets

This paper presents the DELTA-R approach that detects and classifies the changes between two versions of a linked dataset. It contributes to the state of the art firstly: by proposing a more granular classification of the resource level changes, and secondly: by automatically selecting the appropriate resource properties to identify the same resources in different versions of a linked dataset with different URIs and similar representation. The paper also presents the DELTA-R change model to represent the changes detected by the DELTA-R approach. This model bridges the gap between resource-centric and triple-centric views of changes in linked datasets. As a result, a single change detection mechanism will be able to support the use cases like interlink maintenance and dataset or replica synchronization. Additionally, the paper describes an experiment conducted to examine the accuracy of the DELTA-R approach in detecting the changes between two versions of a linked dataset. The result indicates that the accuracy of DELTA-R approach outperforms the state of the art approaches by up to 4%. It is demonstrated that the proposed more granular classification of changes helped to identifyup to 1529 additional updated resources compered to X.By means of a case study, we demonstrate the support of DELTA-R approach and change model for an interlink maintenance use case. The result shows that 100% of the broken interlinks were repaired between DBpedia person snapshot 3.7 and Freebase

LinkWiper – A System For Data Quality in Linked Open Data

Linked Open Data (LOD) provides access to large amounts of data on Web. These data sets range from high quality curated data sets to low quality sets. LOD sources often need strategies to clean up data and provide methodology for quality assessment in linked data. They allow interlinking and integrating any kind of data on the web. Links between various data sources enable software applications to operate over the aggregated data space as if it is a unique local database. However, such links may be broken, leading to data quality problems. In this thesis we present LinkWiper, an automated system for cleaning data in LOD. While this thesis focuses on problems related to dereferenced links, LinkWiper can be used to tackle any other data quality problem such as duplication and consistency. The proposed system includes two major phases. The first phase uses information retrieval-like search techniques to recommend sets of alternative links. The second phase adopts crowdsourcing mechanisms to involve workers (or users) in improving the quality of the LOD sources. We provide an implementation of LinkWiper over DBPedia, a community effort to extract structured information from Wikipedia and make this information using LOD principles. We also conduct extensive experiments to illustrate the efficiency and high precision of the proposed approach.Master of ScienceComputer and Information Science, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136065/1/LinkWiper – A System For Data Quality in Linked Open Data.pdfDescription of LinkWiper – A System For Data Quality in Linked Open Data.pdf : Master of Science Thesi

Preface of MEPDaW 2020: Managing the evolution and preservation of the data web

The MEPDaW workshop series targets one of the emerging and fundamental problems of the Web, specifically the management and preservation of evolving knowledge graphs. During the past six years, the workshop series has been gathering a community of researchers and practitioners around these challenges. To date, the series has successfully published more than 30 articles allowing more than 50 individual authors to present and share their ideas. This 6th edition, virtually co-located with the International Semantic Web Conference (ISWC 2020), gathered the community around nine research publications and one invited keynote presentation. The event took place online on the 1st of November, 2020

Repairing web service compositions based on planning graph

With the increasing acceptance of service-oriented computing, a growing area of study is the way to reuse the loosely coupled Web services, distributed throughout the Internet, to fulfill business goals in an automated fashion. When the goals cannot be satisfied by a single Web service, a chain of Web services can work together as a "composition" to satisfy the needs. The problem of finding composition plans to satisfy given requests is referred to as the Web service composition problem. In recent years, many studies have been done in this area, and various approaches have been proposed. However, most existing proposals endorse a static viewpoint over Web service composition; while in the real world, change is the rule rather than an exception. Web services may appear and disappear at any time in a non-predictable way. Therefore, valid composition plans may suddenly become invalid due to the environment changes in the business world. In this thesis, techniques to support reparation for an existing plan as a reaction to environment changes are proposed. Approaches of repair are compared to ones of re-planning, with particular attention to the time and quality of both approaches. It will be argued that the approach advocated in this thesis is a viable solution to improve the adaptation of automated Web service composition processes in the context of the real world

Security Aspects in Web of Data Based on Trust Principles. A brief of Literature Review

Within scientific community, there is a certain consensus to define "Big Data" as a global set, through a complex integration that embraces several dimensions from using of research data, Open Data, Linked Data, Social Network Data, etc. These data are scattered in different sources, which suppose a mix that respond to diverse philosophies, great diversity of structures, different denominations, etc. Its management faces great technological and methodological challenges: The discovery and selection of data, its extraction and final processing, preservation, visualization, access possibility, greater or lesser structuring, between other aspects, which allow showing a huge domain of study at the level of analysis and implementation in different knowledge domains. However, given the data availability and its possible opening: What problems do the data opening face? This paper shows a literature review about these security aspects

Security Aspects in Web of Data Based on Trust Principles. A brief of Literature Review

Within scientific community, there is a certain consensus to define "Big Data" as a global set, through a complex integration that embraces several dimensions from using of research data, Open Data, Linked Data, Social Network Data, etc. These data are scattered in different sources, which suppose a mix that respond to diverse philosophies, great diversity of structures, different denominations, etc. Its management faces great technological and methodological challenges: The discovery and selection of data, its extraction and final processing, preservation, visualization, access possibility, greater or lesser structuring, between other aspects, that allow showing a huge domain of study at the level of analysis and implementation in different knowledge domains. However, given the data availability and its possible opening: What problems do the data opening face? This paper shows a literature review about these security aspects