Explaining Data Incompleteness in Knowledge Aggregation

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Citation: Honglei Zeng and Richard Fikes. (2005) Explaining Data Incompleteness in Knowledge Aggregation. In KSL-05-04, 2005.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Honglei Zeng and Richard Fikes
title Explaining Data Incompleteness in Knowledge Aggregation
number KSL-05-04
institution Knowledge Systems, AI Laboratory
address Stanford, CA, USA
year 2005
Bibtex more
note Technical Report
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abstract Knowledge aggregation is the problem of taking information from multiple heterogeneous sources and aggregating it into a unified knowledge base. One of the main challenges in that work has been dealing with data incompleteness because data sources seldom contain complete answers to a user's query. Current approaches leverage users' preferences over data sources when trying to aggregate incomplete data. Nevertheless, these approaches are not adequate to satisfy users' needs to trust aggregated data before they can use them with confidence in the presence of incomplete information. We believe such trust may be earned by providing users with the explanations for incomplete data. In this paper, we construct a decision tree-based classifier to acquire context knowledge about data sources and build an aggregation system capable of explaining incomplete data with learned context knowledge. Further, our approach provides a new method to characterize sources that may help users better understand the discrepancies between sources.

KSL Technical Report ID: KSL-05-04
Facts about Explaining Data Incompleteness in Knowledge AggregationRDF feed
Abstract Knowledge aggregation is the problem of ta … Knowledge aggregation is the problem of taking information from multiple heterogeneous sources and aggregating it into a unified knowledge base. One of the main challenges in that work has been dealing with data incompleteness because data sources seldom contain complete answers to a user's query. Current approaches leverage users' preferences over data sources when trying to aggregate incomplete data. Nevertheless, these approaches are not adequate to satisfy users' needs to trust aggregated data before they can use them with confidence in the presence of incomplete information. We believe such trust may be earned by providing users with the explanations for incomplete data. In this paper, we construct a decision tree-based classifier to acquire context knowledge about data sources and build an aggregation system capable of explaining incomplete data with learned context knowledge. Further, our approach provides a new method to characterize sources that may help users better understand the discrepancies between sources. erstand the discrepancies between sources.
Address Stanford, CA, USA  +
Author Honglei Zeng and Richard Fikes  +
Bibtype techreport  +
Has author Honglei Zeng and Richard Fikes  +
Has identifier KSL-05-04  +
Has publishing details 2005  +
Has title Explaining Data Incompleteness in Knowledge Aggregation  +
Has where published KSL-05-04  +
Has year 2005  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-05-04  +
Note Technical Report
Number KSL-05-04  +
Process note NO  +
Title Explaining Data Incompleteness in Knowledge Aggregation  +
Year 2005  +
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