A comparison of automatic search query enhancement algorithms that utilise Wikipedia as a source of a priori knowledge

This paper describes the benchmarking and analysis of five Automatic Search Query Enhancement (ASQE) algorithms that utilise Wikipedia as the sole source for a priori knowledge. The contributions of this paper include: 1) A comprehensive review into current ASQE algorithms that utilise Wikipedia as the sole source for a priori knowledge; 2) benchmarking of five existing ASQE algorithms using the TREC-9 Web Topics on the ClueWeb12 data set and 3) analysis of the results from the benchmarking process to identify the strengths and weaknesses each algorithm. During the benchmarking process, 2,500 relevance assessments were performed. Results of these tests are analysed using the Average Precision @10 per query and Mean Average Precision @10 per algorithm. From this analysis we show that the scope of a priori knowledge utilised during enhancement and the available term weighting methods available from Wikipedia can further aid the ASQE process. Although approaches taken by the algorithms are still relevant, an over dependence on weighting schemes and data sources used can easily impact results of an ASQE algorithm

Image retrieval by hypertext links

This paper presents a model for retrieval of images from a large World Wide Web based collection. Rather than considering complex visual recognition algorithms, the model presented is based on combining evidence of the text content and hypertext structure of the Web. The paper shows that certain types of query are amply served by this form of representation. It also presents a novel means of gathering relevance judgements

A Wikipedia powered state-based approach to automatic search query enhancement

This paper describes the development and testing of a novel Automatic Search Query Enhancement (ASQE) algorithm, the Wikipedia N Sub-state Algorithm (WNSSA), which utilises Wikipedia as the sole data source for prior knowledge. This algorithm is built upon the concept of iterative states and sub-states, harnessing the power of Wikipedia\u27s data set and link information to identify and utilise reoccurring terms to aid term selection and weighting during enhancement. This algorithm is designed to prevent query drift by making callbacks to the user\u27s original search intent by persisting the original query between internal states with additional selected enhancement terms. The developed algorithm has shown to improve both short and long queries by providing a better understanding of the query and available data. The proposed algorithm was compared against five existing ASQE algorithms that utilise Wikipedia as the sole data source, showing an average Mean Average Precision (MAP) improvement of 0.273 over the tested existing ASQE algorithms

A Decision Support System For The Intelligence Satellite Analyst

The study developed a decision support system known as Visual Analytic Cognitive Model (VACOM) to support the Intelligence Analyst (IA) in satellite information processing task within a Geospatial Intelligence (GEOINT) domain. As a visual analytics, VACOM contains the image processing algorithms, a cognitive network of the IA mental model, and a Bayesian belief model for satellite information processing. A cognitive analysis tool helps to identify eight knowledge levels in a satellite information processing. These are, spatial, prototypical, contextual, temporal, semantic, pragmatic, intentional, and inferential knowledge levels, respectively. A cognitive network was developed for each knowledge level with data input from the subjective questionnaires that probed the analysts’ mental model. VACOM interface was designed to allow the analysts have a transparent view of the processes, including, visualization model, and signal processing model applied to the images, geospatial data representation, and the cognitive network of expert beliefs. VACOM interface allows the user to select a satellite image of interest, select each of the image analysis methods for visualization, and compare ‘ground-truth’ information against the recommendation of VACOM. The interface was designed to enhance perception, cognition, and even comprehension to the multi and complex image analyses by the analysts. A usability analysis on VACOM showed many advantages for the human analysts. These include, reduction in cognitive workload as a result of less information search, the IA can conduct an interactive experiment on each of his/her belief space and guesses, and selection of best image processing algorithms to apply to an image context

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio