Alan Woodley

Overview of the INEX 2008 Ad Hoc Track

This paper gives an overview of the INEX 2008 Ad Hoc Track. The main goals of the Ad Hoc Track were two-fold. The first goal was to investigate the value of the internal document structure (as provided by the XML mark-up) for retrieving relevant information. This is a continuation of INEX 2007 and, for this reason, the retrieval results are liberalized to arbitrary passages and measures were chosen to fairly compare systems retrieving elements, ranges of elements, and arbitrary passages. The second goal was to compare focused retrieval to article retrieval more directly than in earlier years. For this reason, standard document retrieval rankings have been derived from all runs, and evaluated with standard measures. In addition, a set of queries targeting Wikipedia have been derived from a proxy log, and the runs are also evaluated against the clicked Wikipedia pages. The INEX 2008 Ad Hoc Track featured three tasks: For the Focused Task a ranked-list of non-overlapping results (elements or passages) was needed. For the Relevant in Context Task non-overlapping results (elements or passages) were returned grouped by the article from which they came. For the Best in Context Task a single starting point (element start tag or passage start) for each article was needed. We discuss the results for the three tasks, and examine the relative effectiveness of element and passage retrieval. This is examined in the context of content only (CO, or Keyword) search as well as content and structure (CAS, or structured) search. Finally, we look at the ability of focused retrieval techniques to rank articles, using standard document retrieval techniques, both against the judged topics as well as against queries and clicks from a proxy log.

NLPX at INEX 2004

XML information retrieval (XML-IR) systems aim to provide users with highly exhaustive and highly specific results. To interact with XML-IR systems users must express both their content and structural needs in the form of a structured query. Historically, these structured queries have been formatted using formal languages such as XPath or NEXI. Unfortunately, formal query languages are very complex and too difficult to be used by experienced, let alone casual, users and are too closely bound to the underlying physical structure of the collection. Hence, recent research has investigated the idea of specifying users’ content and structural requirements via natural language queries (NLQs). The NLP track was established at INEX 2004 to promote research into this area, and QUT participated with the system NLPX. Here, we discuss changes we’ve made to the system since last year, as well as our participation in INEX 2005.

Social licence to operate and the coal seam gas industry: What can be learnt from already established mining operations?

Abstract The recent growth of the coal seam gas industry has increased pressure on regional communities. Debate surrounding the industry is intense and a social licence to operate has yet to be granted to the industry in its entirety. This article presents an analysis of social issues surrounding the coal seam gas industry, making comparisons between two case studies: The Ranger and Jabiluka mines and the Yandicoogina mine. It presents the results of a desktop study, focussed on three topics: community identity; procedural justice; and distributive justice – which provides a means for comparison and draws attention to central concerns. It is found that: power imbalances; changing community identities; potentially inequitable distributions of long-term benefits; and the process to distribute those benefits and negative perceptions of the industry as a whole, serve to undermine the provision of a social licence to operate by communities and has the potential to impose significant negative impacts on companies within the industry.

Social water assessment protocol: a step towards connecting mining, water and human rights

The human right to water has recently been recognized by both the United Nations General Assembly and the Human Rights Council. As the mining industry interacts with water on multiple levels, it is important that these interactions respect the human right to water. Currently, a disconnect exists between mine site water management practices and the recognition of water from a human rights perspective. It has been argued that the Minerals Council of Australia Water Accounting Framework can be used to strengthen the connection between water management and human rights. This article extends this connection through the use of a Social Water Assessment Protocol (SWAP). The SWAP is a scoping tool consisting of a set of questions classified into taxonomic themes under leading topics with suggested sources of data that enable mine sites to better understand the local water context in which they operate. Three of the themes contained in the SWAP – gender, Indigenous peoples and health – are discussed to demonstrate how the protocol may be useful in assisting mining companies to consider their impacts on the human right to water.

What XML-IR Users May Want

It is assumed that by focusing retrieval on a granularity lower than documents XML-IR systems will better satisfy users’ information need than traditional IR systems. Participants in INEX’s Ad-hoc track develop XML-IR systems based upon this assumption, using an evaluation methodology in the tradition of Cranfield. However, since the inception of INEX, debate has raged on how applicable some of the Ad-hoc tasks are to real users. The purpose of the User-Case Studies track is to explore the application of XML-IR systems from the users’ perspective. This paper outlines QUT’s involvement in this track. For our involvement we conducted a user experiment using an XML-IR system (GPX) and three interfaces: a standard keyword interface, a natural language interface (NLPX) and a query-by-template interface (Bricks). Following the experiment we interviewed the users about their experience and asked them - in comparison with a traditional XML-IR system - what type of tasks would they use an XML-IR system for, what extra information they would need to interact with an XML-IR system and how would they want to see XML-IR results presented. It is hoped that the outcomes of this study will bring us closer to understanding what users want from XML-IR systems.

Integrating Recursive Bayesian Estimation with Support Vector Machine to Map Probability of Flooding from Multispectral Landsat Data

This paper addresses the challenge of introducing a Bayes rules to measure flood probability from multispectral data. Machine learning classifiers were applied to map the extent of flood inundation from multispectral remote sensing imagery. The paper applies Extended Support Vector Machine classifier along with linear spectral un-mixing to obtain the classification output. K-means clustering is applied on pre and post flood images to select SVM training samples from clustering outcome of the most informative spectral band. Experiments were conducted by dividing training and testing samples into two groups. The efficiency of classifier was enhanced by introducing the Bayesian probability measure and performance was assessed by using precision and recall metrics on the pre and post Bayesian flood probability estimation. It has been observed that for some test cases in this study there was a substantial improvement in precision-recall curve with high precision values and low recall rate. The optimal flood probability threshold value has also been easily calculated by calculating and analising iteratively precision and recall.

NLPX at INEX 2006

XML information retrieval (XML-IR) systems aim to better fulfil users’ information needs than traditional IR systems by returning results lower than the document level. In order to use XML-IR systems users must encapsulate their structural and content information needs in a structured query. Historically, these structured queries have been formatted using formal languages such as NEXI. Unfortunately, formal query languages are very complex and too difficult to be used by experienced - let alone casual - users and are too closely bound to the underlying physical structure of the collection. INEX’s NLP task investigates the potential of using natural language to specify structured queries. QUT has participated in the NLP task with our system NLPX since its inception. Here, we discuss the changes we’ve made to NLPX since last year, including our efforts to port NLPX to Wikipedia. Second, we present the results from the 2006 INEX track where NLPX was the best performing participant in the Thorough and Focused tasks.

IR of XML documents: a collective ranking strategy

Within the area of Information Retrieval (IR) the importance of appropriate ranking of results has increased markedly. The importance is magnified in the case of systems dedicated to XML retrieval, since users of these systems expect the retrieval of highly relevant and highly precise components, instead of the retrieval of entire documents. As an international, coordinated effort to evaluate the performance of Information Retrieval systems, the Initiative for the Evaluation of XML Retrieval (INEX) encourages participating organisation to run queries on their search engines and to submit their result for the annual INEX workshop. In previous INEX workshops the submitted results were manually assessed by participants and the search engines were ranked in terms of performance. This paper presents a Collective Ranking Strategy that outperforms all search engines it is based on. Moreover it provides a system that is trying to facilitate the ranking of participating search engines.

Using parallel hierarchical clustering to address spatial big data challenges

Clustering can help to make large datasets more manageable by grouping together similar objects. However, most clustering approaches are unable to scale to very large datasets (e.g. more than 10 million objects). The K-Tree is a data structure and clustering algorithm that has proven to be scalable with large streaming datasets. Here, we apply the K-Tree to spatial data (satellite images) and extend from a single threaded to a multicore environment. We show that the K-Tree is able to cluster larger dataset more efficiently than baseline approaches.

Efficient Feature Selection and Nearest Neighbour Search for Hyperspectral Image Classification

Hyperspectral images typically contain hundreds of spectral bands which is one to two orders of magnitude larger than the number of bands in multispectral images. This greater volume of spectral information could lead to discoveries that are not possible with multispectral images; however, overcoming the complexity of the additional information is a computational challenge. Here, we present a solution that uses feature selection, logarithmic nearest neighbor classification and neighborhood spatial analysis to classify the land use of multiple hyperspectral images. Empirical analysis shows that our solution is as accurate as other much more complex approaches and it is orders-of-magnitude more efficient. This ascertains that our solution is scalable to larger datasets while maintaining high accuracy.