Proceedings Article10.1109/WI.2016.0068
Learning Processes Based on Data Sources with Certainty Levels in Linked Open Data
Jesse Xi Chen,Marek Reformat,Ronald R. Yager +2 more
- 01 Oct 2016
pp 429-434
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TL;DR: This paper uses an RDF-based participatory learning process to aggregate information obtained from multiple data stores and provides mechanisms that determine overall certainty in combined data based on levels of confidence in already known pieces of information and new ones.
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Abstract: Linked Open Data (LOD) consists of numerous data stores that are highly interconnected. LOD stores use Resource Description Framework (RDF) as a data representation format. A graph-based nature of RDF brings an opportunity to develop new approaches for accumulating data from multiple sources characterized by different levels of confidence in them. Recently, a participatory learning mechanism has been extended to cope with RDF. It is an attractive way of integrating new pieces of information with already known ones. Further, it has been recognized that pieces of information describing entities can have a disjunctive or conjunctive form. This paper uses an RDF-based participatory learning process to aggregate information obtained from multiple data stores. This process provides mechanisms that determine overall certainty in combined data based on levels of confidence in already known pieces of information and new ones. The behavior of such a process used for integrating information equipped with different levels of uncertainty is presented, and a simple case study is included.
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Citations
Clustering of Propositions Equipped with Uncertainty
Marek Reformat,Jesse Xi Chen,Ronald R. Yager +2 more
- 11 Jun 2018
TL;DR: Graph-based data representation formats enable more advanced processing of data that leads to better utilization of information stored and available on the web and create methods and techniques that can assimilate new data and build knowledge-like data structures.
1
Semantic Web: Graphs, Imprecision and Knowledge Generation
Marek Reformat
- 01 Jan 2021
TL;DR: In this paper, the authors present a framework for knowledge generation based on knowledge graphs and fuzzy set theory, which can handle vagueness and uncertainty in data representation formats that can handle the imprecise nature of knowledge in various domains.
References
Linked Data - the story so far
TL;DR: The authors describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked data community as it moves forward.
The Semantic Web Revisited
TL;DR: It is argued that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have progressed steadily over the past five years.
Linked Open Data Aggregation: Conflict Resolution and Aggregate Quality
Tom #x B,Knap,Jan Michelfeit,Martin Necasky +3 more
- 16 Jul 2012
TL;DR: Two crucial aspects of the data aggregation process in ODCleanStore - resolution of data conflicts and computation of aggregate quality helping consumers to decide whether the aggregated data are worth using are described.
Toward a theory of conjunctive variables
TL;DR: The reasoning mechanism developed is seen as an extension of the theory of approximate reasoning and the concepts of assurety and rebuff play a central role in this theory.
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Participatory Learning of Propositional Knowledge
TL;DR: The objective here is to extend the participatory learning paradigm (PLP) to environments in which the authors are interested in learning information and knowledge expressed in terms of declarative statements and to provide a version of the PLP that is appropriate for the task of learningdeclarative knowledge.
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