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Fusing Automatically Extracted Annotations for the Semantic Web

Nikolov, Andriy (2010). Fusing Automatically Extracted Annotations for the Semantic Web. PhD thesis The Open University.

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Abstract

This research focuses on the problem of semantic data fusion. Although various solutions have been developed in the research communities focusing on databases and formal logic, the choice of an appropriate algorithm is non-trivial because the performance of each algorithm and its optimal configuration parameters depend on the type of data, to which the algorithm is applied. In order to be reusable, the fusion system must be able to select appropriate techniques and use them in combination.
Moreover, because of the varying reliability of data sources and algorithms performing fusion subtasks, uncertainty is an inherent feature of semantically annotated data and has to be taken into account by the fusion system. Finally, the issue of schema heterogeneity can have a negative impact on the fusion performance. To address these issues, we propose KnoFuss: an architecture for Semantic Web data integration based on the principles of problem-solving methods. Algorithms dealing with different fusion subtasks are represented as components of a modular architecture, and their capabilities are described formally. This allows the architecture to select appropriate methods and configure them depending on the processed data. In order to handle uncertainty, we propose a novel algorithm based on the Dempster-Shafer belief propagation. KnoFuss employs this algorithm to reason about uncertain data and method results in order to refine the fused knowledge base. Tests show that these solutions lead to improved fusion performance. Finally, we addressed the problem of data fusion in the presence of schema heterogeneity. We extended the KnoFuss framework to exploit results of automatic schema alignment tools and proposed our own schema matching algorithm aimed at facilitating data fusion in the Linked Data environment. We conducted experiments with this approach and obtained a substantial improvement in performance in comparison with public data repositories.

Item Type: Thesis (PhD)
Copyright Holders: 2010 The Author
Project Funding Details:
Funded Project NameProject IDFunding Body
Not SetNot SetKnowledge Media Institute
Not SetNot SetEC grant number IST-FP6-026978
Keywords: Semantic Web; data integration; knowledge fusion; entity resolution; inconsistency resolution; ontology matching
Academic Unit/Department: Knowledge Media Institute
Item ID: 34012
Depositing User: Andriy Nikolov
Date Deposited: 18 Jul 2012 08:07
Last Modified: 24 Oct 2012 07:30
URI: http://oro.open.ac.uk/id/eprint/34012
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