de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Thesis

Lineage Enabled Query Answering in Uncertain Knowledge Bases

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44678

Iqbal,  Javeria
International Max Planck Research School, MPI for Informatics, Max Planck Society;
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45609

Theobald,  Martin
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45041

Michel,  Sebastian
Databases and Information Systems, MPI for Informatics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Iqbal, J. (2011). Lineage Enabled Query Answering in Uncertain Knowledge Bases. Master Thesis, Universität des Saarlandes, Saarbrücken.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0027-A1EA-B
Abstract
We present a unified framework for query answering over uncertain RDF knowledge bases. Specifically, our proposed design combines correlated base facts with a query driven, top down deductive grounding phase of first-order logic formulas (i.e., Horn rules) followed by a probabilistic inference phase. In addition to static input correlations among base facts, we employ the lineage structure obtained from processing the rules during grounding phase, in order to trace the logical dependencies of query answers (i.e., derived facts) back to the base facts. Thus, correlations (or more precisely: dependencies) among facts in a knowledge base may arise from two sources: 1) static input dependencies obtained from real-world observations; and 2) dynamic dependencies induced at query time by the rule-based lineage structure of the query answer. Our implementation employs state-of-the-art inference techniques: We apply exact inference whenever tractable, the detection of shared factors, shrink- age of Boolean formula when feasible, and Gibbs sampling in the general case. Our experiments are conducted on real data sets with synthetic expansion of correlated base facts. The experimental evaluation demonstrates the practical viability and scalability of our approach, achieving interactive query response times over a very large knowledge base. The experimental results provide the success guarantee of our presented framework.