add references
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\subsection{Performance of \JITI on ILP applications} \label{sec:perf:ILP}
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\subsection{Performance of \JITI on ILP applications} \label{sec:perf:ILP}
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%-------------------------------------------------------------------------
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%-------------------------------------------------------------------------
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The need for \JITI was originally noticed in inductive logic
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The need for \JITI was originally noticed in inductive logic
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programming applications.
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programming applications. Table~\ref{tab:ilp:time} shows JITI
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Table~\ref{tab:ilp:time} shows JITI performance on some learning tasks
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performance on some learning tasks using the ALEPH
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using the ALEPH system~\cite{ALEPH}. The dataset \Krki tries to
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system~\cite{ALEPH}. The dataset \Krki tries to learn rules from a
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learn rules from a small database of chess end-games;
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small database of chess end-games; \GeneExpression learns rules for
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\GeneExpression learns rules for yeast gene activity given a
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yeast gene activity given a database of genes, their interactions, and
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database of genes, their interactions, and micro-array gene expression
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micro-array gene expression data; \BreastCancer processes real-life
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data; \BreastCancer processes real-life patient reports towards
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patient reports towards predicting whether an abnormality may be
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predicting whether an abnormality may be malignant;
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malignant; \IEProtein processes information extraction from paper
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\IEProtein processes information extraction from
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abstracts to search proteins; \Susi learns from shopping patterns; and
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paper abstracts to search proteins; \Susi learns from shopping
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\Mesh learns rules for finite-methods mesh design. The datasets
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patterns; and \Mesh learns rules for finite-methods mesh
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\Carcinogenesis, \Choline, \Mutagenesis, \Pyrimidines, and
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design. The datasets \Carcinogenesis, \Choline,
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\Thermolysin try to predict chemical properties of compounds. The
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\Mutagenesis, \Pyrimidines, and \Thermolysin try to
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first three datasets store properties of interest as tables, but
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predict chemical properties of compounds. The first three
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datasets store properties of interest as tables, but
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\Thermolysin learns from the 3D-structure of a molecule's
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\Thermolysin learns from the 3D-structure of a molecule's
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conformations. Several of these datasets are standard across the Machine
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conformations. Several of these datasets are standard across the
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Learning literature. \GeneExpression~\cite{} and
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Machine Learning literature. \GeneExpression~\cite{ilp-regulatory06}
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\BreastCancer~\cite{} were partly developed by some of the
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and \BreastCancer~\cite{DBLP:conf/ijcai/DavisBDPRCS05} were partly
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paper's authors. Most datasets perform simple queries in an
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developed by some of the paper's authors. Most datasets perform simple
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extensional database. The exception is \Mutagenesis where
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queries in an extensional database. The exception is \Mutagenesis
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several predicates are defined intensionally, requiring extensive
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where several predicates are defined intensionally, requiring
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computation.
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extensive computation.
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%------------------------------------------------------------------------------
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%------------------------------------------------------------------------------
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\begin{table}[t]
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\begin{table}[t]
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@ -567,3 +567,43 @@ institution = "Department of Artificial Intelligence, University of Edinburgh"
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isbn = {1-58113-634-X},
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isbn = {1-58113-634-X},
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bibsource = {DBLP, http://dblp.uni-trier.de}
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bibsource = {DBLP, http://dblp.uni-trier.de}
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}
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}
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@manual{ ALEPH,
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author = "Ashwin Srinivasan",
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title = "The Aleph Manual",
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url = "http://oldwww.comlab.ox.ac.uk/oucl/groups/machlearn/Aleph/aleph_toc.html.",
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year = "2001" }
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@inproceedings{ilp-regulatory06,
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author = {Irene M. Ong and
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Scott E. Topper and
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David Page and
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Santos Costa, V\{\i}tor},
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title = {Inferring Regulatory Networks from Time Series Expression Data and
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Relational Data via Inductive Logic Programming},
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booktitle = {Proceedings of the Sixteenth International Conference on Inductive Logic Programming, Santiago de Compostela, Spain},
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year = {2007}
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}
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@inproceedings{DBLP:conf/ijcai/DavisBDPRCS05,
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author = {Jesse Davis and
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Elizabeth S. Burnside and
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In{\^e}s Dutra and
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David Page and
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Raghu Ramakrishnan and
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Santos Costa, V\'{\i}tor and
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Jude W. Shavlik},
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title = {View Learning for Statistical Relational Learning: With
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an Application to Mammography.},
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pages = {677-683},
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editor = {Leslie Pack Kaelbling and
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Alessandro Saffiotti},
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booktitle = {IJCAI-05, Proceedings of the Nineteenth International Joint
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Conference on Artificial Intelligence, Edinburgh, Scotland,
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UK, July 30-August 5, 2005},
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publisher = {Professional Book Center},
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isbn = {0938075934},
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year = {2005},
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bibsource = {DBLP, http://dblp.uni-trier.de}
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}
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