diff --git a/docs/index/iclp07.tex b/docs/index/iclp07.tex
index c8d8c9cad..72a6ddcb4 100644
--- a/docs/index/iclp07.tex
+++ b/docs/index/iclp07.tex
@@ -1122,29 +1122,27 @@ difference in this benchmark.
 \subsection{Performance of \JITI on ILP applications} \label{sec:perf:ILP}
 %-------------------------------------------------------------------------
 The need for \JITI was originally noticed in inductive logic
-programming applications.
-Table~\ref{tab:ilp:time} shows JITI performance on some learning tasks
-using the ALEPH system~\cite{ALEPH}. The dataset \Krki tries to
-learn rules from a small database of chess end-games;
-\GeneExpression learns rules for yeast gene activity given a
-database of genes, their interactions, and micro-array gene expression
-data; \BreastCancer processes real-life patient reports towards
-predicting whether an abnormality may be malignant;
-\IEProtein processes information extraction from
-paper abstracts to search proteins; \Susi learns from shopping
-patterns; and \Mesh learns rules for finite-methods mesh
-design. The datasets \Carcinogenesis, \Choline,
-\Mutagenesis, \Pyrimidines, and \Thermolysin try to
-predict chemical properties of compounds. The first three
-datasets store properties of interest as tables, but
+programming applications.  Table~\ref{tab:ilp:time} shows JITI
+performance on some learning tasks using the ALEPH
+system~\cite{ALEPH}. The dataset \Krki tries to learn rules from a
+small database of chess end-games; \GeneExpression learns rules for
+yeast gene activity given a database of genes, their interactions, and
+micro-array gene expression data; \BreastCancer processes real-life
+patient reports towards predicting whether an abnormality may be
+malignant; \IEProtein processes information extraction from paper
+abstracts to search proteins; \Susi learns from shopping patterns; and
+\Mesh learns rules for finite-methods mesh design. The datasets
+\Carcinogenesis, \Choline, \Mutagenesis, \Pyrimidines, and
+\Thermolysin try to predict chemical properties of compounds. The
+first three datasets store properties of interest as tables, but
 \Thermolysin learns from the 3D-structure of a molecule's
-conformations. Several of these datasets are standard across the Machine
-Learning literature. \GeneExpression~\cite{} and
-\BreastCancer~\cite{} were partly developed by some of the
-paper's authors. Most datasets perform simple queries in an
-extensional database. The exception is \Mutagenesis where
-several predicates are defined intensionally, requiring extensive
-computation.
+conformations. Several of these datasets are standard across the
+Machine Learning literature. \GeneExpression~\cite{ilp-regulatory06}
+and \BreastCancer~\cite{DBLP:conf/ijcai/DavisBDPRCS05} were partly
+developed by some of the paper's authors. Most datasets perform simple
+queries in an extensional database. The exception is \Mutagenesis
+where several predicates are defined intensionally, requiring
+extensive computation.
 
 %------------------------------------------------------------------------------
 \begin{table}[t]
diff --git a/docs/index/lp.bib b/docs/index/lp.bib
index d36ea34ff..8540b6078 100644
--- a/docs/index/lp.bib
+++ b/docs/index/lp.bib
@@ -567,3 +567,43 @@ institution = "Department of Artificial Intelligence, University of Edinburgh"
   isbn      = {1-58113-634-X},
   bibsource = {DBLP, http://dblp.uni-trier.de}
 }
+
+@manual{ ALEPH,
+  author = "Ashwin Srinivasan",
+  title = "The Aleph Manual",
+  url       = "http://oldwww.comlab.ox.ac.uk/oucl/groups/machlearn/Aleph/aleph_toc.html.",
+  year = "2001" }
+
+@inproceedings{ilp-regulatory06,
+  author    = {Irene M. Ong and
+               Scott E. Topper and
+               David Page and
+                Santos Costa, V\{\i}tor},
+  title     = {Inferring Regulatory Networks from Time Series Expression Data and
+  Relational Data via Inductive Logic Programming},
+  booktitle     = {Proceedings of the Sixteenth International Conference on Inductive Logic Programming, Santiago de Compostela, Spain},
+  year      = {2007}
+}
+
+@inproceedings{DBLP:conf/ijcai/DavisBDPRCS05,
+  author    = {Jesse Davis and
+               Elizabeth S. Burnside and
+               In{\^e}s Dutra and
+               David Page and
+               Raghu Ramakrishnan and
+               Santos Costa, V\'{\i}tor and
+               Jude W. Shavlik},
+  title     = {View Learning for Statistical Relational Learning: With
+               an Application to Mammography.},
+  pages     = {677-683},
+  editor    = {Leslie Pack Kaelbling and
+               Alessandro Saffiotti},
+  booktitle     = {IJCAI-05, Proceedings of the Nineteenth International Joint
+               Conference on Artificial Intelligence, Edinburgh, Scotland,
+               UK, July 30-August 5, 2005},
+  publisher = {Professional Book Center},
+  isbn      = {0938075934},
+  year = {2005},
+  bibsource = {DBLP, http://dblp.uni-trier.de}
+}
+