diff --git a/INFO_DP.bib b/INFO_DP.bib
index 7d56ec9..d7aa966 100644
--- a/INFO_DP.bib
+++ b/INFO_DP.bib
@@ -2,13 +2,27 @@
 %% http://bibdesk.sourceforge.net/
 
 
-%% Created for Nigel Stanger at 2011-07-25 16:59:08 +1200 
+%% Created for Nigel Stanger at 2011-07-29 15:45:38 +1200 
 
 
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+@techreport{dp2011-06,
+	Abstract = {Over the last few years, the voluminous increase in the academic research publications has gained significant research attention. Research has been carried out exploring novel ways of providing information services using the research content. However, the task of extracting meaningful information from research documents remains a challenge. This paper presents our research work carried out for developing intelligent information systems, exploiting the research content. We present in this paper, a linked data application which uses a new semantic publishing model for providing value added information services for the research community. The paper presents a conceptual framework for modelling contexts associated with sentences in research articles and discusses the Sentence Context Ontology, which is used to convert the information extracted from research documents into machine-understandable data. The paper also reports on supervised learning experiments carried out using conditional probabilistic models for achieving automatic context identification.},
+	Address = {Dunedin, New Zealand},
+	Author = {M.A. Angrosh and Stephen Cranefield and Nigel Stanger},
+	Date-Added = {2011-07-29 15:44:39 +1200},
+	Date-Modified = {2011-07-29 15:44:39 +1200},
+	Institution = {Department of Information Science, University of Otago},
+	Keywords = {semantic publishing models, sentence context ontology, linked data application, conditional random fields, maximum entropy markov models, citation classification, sentence context identification},
+	Month = jul,
+	Number = {2011/06},
+	Title = {Contextual information retrieval in research articles: Semantic publishing tools for the research community},
+	Type = {Discussion paper},
+	Year = {2011}}
+
 @techreport{dp2011-05,
 	Abstract = {In Normative Multi-Agent Systems (NorMAS), researchers have investigated several mechanisms for agents to learn norms. In the context of agents learning norms, the objectives of the paper are three-fold. First, this paper aims at providing an overview of different mechanisms employed by researchers for norm learning. Second, it discusses the contributions of different mechanisms to the three aspects of active learning namely learning by doing, observing and com- municating. Third, it compares two normative architectures which have an emphasis on the learning of norms. It also discusses the features that should be considered in future norm learning architectures.},
 	Address = {Dunedin, New Zealand},
@@ -90,17 +104,16 @@
 	Type = {Discussion paper},
 	Year = {2010}}
 
-@techreport{dp2011-06,
-	Abstract = {Over the last few years, the voluminous increase in the academic research publications has gained significant research attention. Research has been carried out exploring novel ways of providing information services using the research content. However, the task of extracting meaningful information from research documents remains a challenge. This paper presents our research work carried out for developing intelligent information systems, exploiting the research content. We present in this paper, a linked data application which uses a new semantic publishing model for providing value added information services for the research community. The paper presents a conceptual framework for modelling contexts associated with sentences in research articles and discusses the Sentence Context Ontology, which is used to convert the information extracted from research documents into machine-understandable data. The paper also reports on supervised learning experiments carried out using conditional probabilistic models for achieving automatic context identification.},
+@techreport{dp2011-07,
+	Abstract = {Second Life is a multi-purpose online virtual world that is increasingly being used for applications and simulations in diversified areas such as education, training, entertainment, and even for applications related to Artificial Intelligence. For the successful implementation and analysis of most of these applications, it is important to have a robust mechanism to extract low-level data from Second Life in high frequency and high accuracy. However, currently Second Life does not have a reliable or scalable inbuilt data extraction mechanism, nor the related research provides a better alternative. This paper presents a robust and reliable data extraction mechanism from Second Life. We also investigate the currently existing data extraction mechanisms in detail, identifying their limitations in extracting data with high accuracy and high frequency.},
 	Address = {Dunedin, New Zealand},
-	Author = {M.A. Angrosh and Stephen Cranefield and Nigel Stanger},
+	Author = {Surangika Ranathunga and Stephen Cranefield and Martin Purvis},
 	Date-Added = {2010-11-17 14:50:10 +1300},
-	Date-Modified = {2011-07-25 16:47:40 +1200},
+	Date-Modified = {2011-07-29 15:45:23 +1200},
 	Institution = {Department of Information Science, University of Otago},
-	Keywords = {semantic publishing models, sentence context ontology, linked data application, conditional random fields, maximum entropy markov models, citation classification, sentence context identification},
 	Month = jul,
-	Number = {2011/06},
-	Title = {Contextual information retrieval in research articles: Semantic publishing tools for the research community},
+	Number = {2011/07},
+	Title = {Extracting data from Second Life},
 	Type = {Discussion paper},
 	Year = {2011}}