148 research outputs found

    Commonsense Properties from Query Logs and Question Answering Forums

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    Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums, and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality

    An Automatic Intelligent System for Document Processing and Fruition

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    With the increasing amount of documents available on-line, the need for intelligent digital libraries, that allow to automatize the document processing tasks and to suitably organize and make available the documents so as to provide personalized and focused access, becomes more and more pressing. This paper proposes an integrated system that merges intelligent modules covering all the phases involved in a document lifecycle, from acquisition, to processing, to information extraction, to personalized fruition for final users. The role and possible cooperation of Machine Learning and Data Mining techniques in the system is highlighted and discussed, along with their importance to provide effective support to both the building and the fruition of the Digital Library and the underlying knowledge base

    Bridging semantic gap: learning and integrating semantics for content-based retrieval

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    Digital cameras have entered ordinary homes and produced^incredibly large number of photos. As a typical example of broad image domain, unconstrained consumer photos vary significantly. Unlike professional or domain-specific images, the objects in the photos are ill-posed, occluded, and cluttered with poor lighting, focus, and exposure. Content-based image retrieval research has yet to bridge the semantic gap between computable low-level information and high-level user interpretation. In this thesis, we address the issue of semantic gap with a structured learning framework to allow modular extraction of visual semantics. Semantic image regions (e.g. face, building, sky etc) are learned statistically, detected directly from image without segmentation, reconciled across multiple scales, and aggregated spatially to form compact semantic index. To circumvent the ambiguity and subjectivity in a query, a new query method that allows spatial arrangement of visual semantics is proposed. A query is represented as a disjunctive normal form of visual query terms and processed using fuzzy set operators. A drawback of supervised learning is the manual labeling of regions as training samples. In this thesis, a new learning framework to discover local semantic patterns and to generate their samples for training with minimal human intervention has been developed. The discovered patterns can be visualized and used in semantic indexing. In addition, three new class-based indexing schemes are explored. The winnertake- all scheme supports class-based image retrieval. The class relative scheme and the local classification scheme compute inter-class memberships and local class patterns as indexes for similarity matching respectively. A Bayesian formulation is proposed to unify local and global indexes in image comparison and ranking that resulted in superior image retrieval performance over those of single indexes. Query-by-example experiments on 2400 consumer photos with 16 semantic queries show that the proposed approaches have significantly better (18% to 55%) average precisions than a high-dimension feature fusion approach. The thesis has paved two promising research directions, namely the semantics design approach and the semantics discovery approach. They form elegant dual frameworks that exploits pattern classifiers in learning and integrating local and global image semantics

    A study of spatial data models and their application to selecting information from pictorial databases

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    People have always used visual techniques to locate information in the space surrounding them. However with the advent of powerful computer systems and user-friendly interfaces it has become possible to extend such techniques to stored pictorial information. Pictorial database systems have in the past primarily used mathematical or textual search techniques to locate specific pictures contained within such databases. However these techniques have largely relied upon complex combinations of numeric and textual queries in order to find the required pictures. Such techniques restrict users of pictorial databases to expressing what is in essence a visual query in a numeric or character based form. What is required is the ability to express such queries in a form that more closely matches the user's visual memory or perception of the picture required. It is suggested in this thesis that spatial techniques of search are important and that two of the most important attributes of a picture are the spatial positions and the spatial relationships of objects contained within such pictures. It is further suggested that a database management system which allows users to indicate the nature of their query by visually placing iconic representations of objects on an interface in spatially appropriate positions, is a feasible method by which pictures might be found from a pictorial database. This thesis undertakes a detailed study of spatial techniques using a combination of historical evidence, psychological conclusions and practical examples to demonstrate that the spatial metaphor is an important concept and that pictures can be readily found by visually specifying the spatial positions and relationships between objects contained within them

    Retrieval and Annotation of Music Using Latent Semantic Models

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    PhDThis thesis investigates the use of latent semantic models for annotation and retrieval from collections of musical audio tracks. In particular latent semantic analysis (LSA) and aspect models (or probabilistic latent semantic analysis, pLSA) are used to index words in descriptions of music drawn from hundreds of thousands of social tags. A new discrete audio feature representation is introduced to encode musical characteristics of automatically-identified regions of interest within each track, using a vocabulary of audio muswords. Finally a joint aspect model is developed that can learn from both tagged and untagged tracks by indexing both conventional words and muswords. This model is used as the basis of a music search system that supports query by example and by keyword, and of a simple probabilistic machine annotation system. The models are evaluated by their performance in a variety of realistic retrieval and annotation tasks, motivated by applications including playlist generation, internet radio streaming, music recommendation and catalogue searchEngineering and Physical Sciences Research Counci

    User-generated descriptions of individual images versus labels of groups 3 of images: A comparison using basic level theory

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    Although images are visual information sources with little or no text associated with them, users still tend to use text to describe images and formulate queries. This is because digital libraries and search engines provide mostly text query options and rely on text annotations for representation and retrieval of the semantic content of images. While the main focus of image research is on indexing and retrieval of individual images, the general topic of image browsing and indexing, and retrieval of groups of images has not been adequately investigated. Comparisons of descriptions of individual images as well as labels of groups of images supplied by users using cognitive models are scarce. This work fills this gap. Using the basic level theory as a framework, a comparison of the descriptions of individual images and labels assigned to groups of images by 180 participants in three studies found a marked difference in their level of abstraction. Results confirm assertions by previous researchers in LIS and other fields that groups of images are labeled using more superordinate level terms while individual image descriptions are mainly at the basic level. Implications for design of image browsing interfaces, taxonomies, thesauri, and similar tools are discussed

    Applying blended conceptual spaces to variable choice and aesthetics in data visualisation

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    Computational creativity is an active area of research within the artificial intelligence domain that investigates what aspects of computing can be considered as an analogue to the human creative process. Computers can be programmed to emulate the type of things that the human mind can. Artificial creativity is worthy of study for two reasons. Firstly, it can help in understanding human creativity and secondly it can help with the design of computer programs that appear to be creative. Although the implementation of creativity in computer algorithms is an active field, much of the research fails to specify which of the known theories of creativity it is aligning with. The combination of computational creativity with computer generated visualisations has the potential to produce visualisations that are context sensitive with respect to the data and could solve some of the current automation problems that computers experience. In addition theories of creativity could theoretically compute unusual data combinations, or introducing graphical elements that draw attention to the patterns in the data. More could be learned about the creativity involved as humans go about the task of generating a visualisation. The purpose of this dissertation was to develop a computer program that can automate the generation of a visualisation, for a suitably chosen visualisation type over a small domain of knowledge, using a subset of the computational creativity criteria, in order to try and explore the effects of the introduction of conceptual blending techniques. The problem is that existing computer programs that generate visualisations are lacking the creativity, intuition, background information, and visual perception that enable a human to decide what aspects of the visualisation will expose patterns that are useful to the consumer of the visualisation. The main research question that guided this dissertation was, “How can criteria derived from theories of creativity be used in the generation of visualisations?”. In order to answer this question an analysis was done to determine which creativity theories and artificial intelligence techniques could potentially be used to implement the theories in the context of those relevant to computer generated visualisations. Measurable attributes and criteria that were sufficient for an algorithm that claims to model creativity were explored. The parts of the visualisation pipeline were identified and the aspects of visualisation generation that humans are better at than computers was explored. Themes that emerged in both the computational creativity and the visualisation literature were highlighted. Finally a prototype was built that started to investigate the use of computational creativity methods in the ‘variable choice’, and ‘aesthetics’ stages of the data visualisation pipeline.School of ComputingM. Sc. (Computing

    Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

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    This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond
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