Spatially structured cognitve models of semantic information:the implications for computerised databases

Existing theories of semantic cognition propose models of cognitive processing occurring in a conceptual space, where ‘meaning’ is derived from the spatial relationships between concepts’ mapped locations within the space. Information visualisation is a growing area of research within the field of information retrieval, and methods for presenting database contents visually in the form of spatial data management systems (SDMSs) are being developed. This thesis combined these two areas of research to investigate the benefits associated with employing spatial-semantic mapping (documents represented as objects in two- and three-dimensional virtual environments are proximally mapped dependent on the semantic similarity of their content) as a tool for improving retrieval performance and navigational efficiency when browsing for information within such systems. Positive effects associated with the quality of document mapping were observed; improved retrieval performance and browsing behaviour were witnessed when mapping was optimal. It was also shown using a third dimension for virtual environment (VE) presentation provides sufficient additional information regarding the semantic structure of the environment that performance is increased in comparison to using two-dimensions for mapping. A model that describes the relationship between retrieval performance and browsing behaviour was proposed on the basis of findings. Individual differences were not found to have any observable influence on retrieval performance or browsing behaviour when mapping quality was good. The findings from this work have implications for both cognitive modelling of semantic information, and for designing and testing information visualisation systems. These implications are discussed in the conclusions of this work.

ImageSpace: An Environment for Image Ontology Management

More and more researchers have realized that ontologies will play a critical role in the development of the Semantic Web, the next generation Web in which content is not only consumable by humans, but also by software agents. The development of tools to support ontology management including creation, visualization, annotation, database storage, and retrieval is thus extremely important. We have developed ImageSpace, an image ontology creation and annotation tool that features (1) full support for the standard web ontology language DAML+OIL; (2) image ontology creation, visualization, image annotation and display in one integrated framework; (3) ontology consistency assurance; and (4) storing ontologies and annotations in relational databases. It is expected that the availability of such a tool will greatly facilitate the creation of image repositories as islands of the Semantic Web.

Truly interactive approaches to browsing image databases

Image collections are ever growing and hence efficient and effective tools to manage these repositories are highly sought after. In this paper, we present effective image browsing systems that are operated on a large multi-touch environment for truly interactive exploration. Not only do image browsers pose a useful alternative to retrieval-based systems, they also provide a visualisation of the whole image collection and allow users to interactively explore particular parts of the collection. Our systems are based on the idea that visually similar images are located close to each other in the visualisation, that image thumbnails are arranged on a regular lattice (either a regular grid projected onto a sphere or a hexagonal lattice), and that large image datasets can be accessed through a hierarchical tree structure. A pilot study has shown that the presented systems do indeed work well and are preferred compared to conventional image browsers. © 2011 IEEE.

Versioning Concept Schemes for Persistent Retrieval

Things change. Words change, meaning changes and use changes both words and meaning. In information access systems this means concept schemes such as thesauri or clas- sification schemes change. They always have. Concept schemes that have survived have evolved over time, moving from one version, often called an edition, to the next. If we want to manage how words and meanings - and as a conse- quence use - change in an effective manner, and if we want to be able to search across versions of concept schemes, we have to track these changes. This paper explores how we might expand SKOS, a World Wide Web Consortium (W3C) draft recommendation in order to do that kind of tracking.The Simple Knowledge Organization System (SKOS) Core Guide is sponsored by the Semantic Web Best Practices and Deployment Working Group. The second draft, edited by Alistair Miles and Dan Brickley, was issued in November 2005. SKOS is a “model for expressing the basic structure and content of concept schemes such as thesauri, classification schemes, subject heading lists, taxonomies, folksonomies, other types of controlled vocabulary and also concept schemes embedded in glossaries and terminologies” in RDF. How SKOS handles version in concept schemes is an open issue. The current draft guide suggests using OWL and DCTERMS as mechanisms for concept scheme revision.As it stands an editor of a concept scheme can make notes or declare in OWL that more than one version exists. This paper adds to the SKOS Core by introducing a tracking sys- tem for changes in concept schemes. We call this tracking system vocabulary ontogeny. Ontogeny is a biological term for the development of an organism during its lifetime. Here we use the ontogeny metaphor to describe how vocabularies change over their lifetime. Our purpose here is to create a conceptual mechanism that will track these changes and in so doing enhance information retrieval and prevent document loss through versioning, thereby enabling persistent retrieval.

Mind the gaps: cognitive-inspired AI for high-level visual sensemaking. Towards abstract concept image classification

The abundance of visual data and the push for robust AI are driving the need for automated visual sensemaking. Computer Vision (CV) faces growing demand for models that can discern not only what images "represent," but also what they "evoke." This is a demand for tools mimicking human perception at a high semantic level, categorizing images based on concepts like freedom, danger, or safety. However, automating this process is challenging due to entropy, scarcity, subjectivity, and ethical considerations. These challenges not only impact performance but also underscore the critical need for interoperability. This dissertation focuses on abstract concept-based (AC) image classification, guided by three technical principles: situated grounding, performance enhancement, and interpretability. We introduce ART-stract, a novel dataset of cultural images annotated with ACs, serving as the foundation for a series of experiments across four key domains: assessing the effectiveness of the end-to-end DL paradigm, exploring cognitive-inspired semantic intermediaries, incorporating cultural and commonsense aspects, and neuro-symbolic integration of sensory-perceptual data with cognitive-based knowledge. Our results demonstrate that integrating CV approaches with semantic technologies yields methods that surpass the current state of the art in AC image classification, outperforming the end-to-end deep vision paradigm. The results emphasize the role semantic technologies can play in developing both effective and interpretable systems, through the capturing, situating, and reasoning over knowledge related to visual data. Furthermore, this dissertation explores the complex interplay between technical and socio-technical factors. By merging technical expertise with an understanding of human and societal aspects, we advocate for responsible labeling and training practices in visual media. These insights and techniques not only advance efforts in CV and explainable artificial intelligence but also propel us toward an era of AI development that harmonizes technical prowess with deep awareness of its human and societal implications.

The Use of Latent Semantic Indexing to Mitigate OCR Effects of Related Document Images

Due to both the widespread and multipurpose use of document images and the current availability of a high number of document images repositories, robust information retrieval mechanisms and systems have been increasingly demanded. This paper presents an approach to support the automatic generation of relationships among document images by exploiting Latent Semantic Indexing (LSI) and Optical Character Recognition (OCR). We developed the LinkDI (Linking of Document Images) service, which extracts and indexes document images content, computes its latent semantics, and defines relationships among images as hyperlinks. LinkDI was experimented with document images repositories, and its performance was evaluated by comparing the quality of the relationships created among textual documents as well as among their respective document images. Considering those same document images, we ran further experiments in order to compare the performance of LinkDI when it exploits or not the LSI technique. Experimental results showed that LSI can mitigate the effects of usual OCR misrecognition, which reinforces the feasibility of LinkDI relating OCR output with high degradation.

Asymmetric page split generalized index search trees for formal concept analysis

Formal Concept Analysis is an unsupervised machine learning technique that has successfully been applied to document organisation by considering documents as objects and keywords as attributes. The basic algorithms of Formal Concept Analysis then allow an intelligent information retrieval system to cluster documents according to keyword views. This paper investigates the scalability of this idea. In particular we present the results of applying spatial data structures to large datasets in formal concept analysis. Our experiments are motivated by the application of the Formal Concept Analysis idea of a virtual filesystem [11,17,15]. In particular the libferris [1] Semantic File System. This paper presents customizations to an RD-Tree Generalized Index Search Tree based index structure to better support the application of Formal Concept Analysis to large data sources.

The semantic interference effect in the picture-word paradigm: An event-related fMRI study employing overt responses

We used event-related functional magnetic resonance imaging (fMRI) to investigate neural responses associated with the semantic interference (SI) effect in the picture-word task. Independent stage models of word production assume that the locus of the SI effect is at the conceptual processing level (Levelt et al. [1999]: Behav Brain Sci 22:1-75), whereas interactive models postulate that it occurs at phonological retrieval (Starreveld and La Heij [1996]: J Exp Psychol Learn Mem Cogn 22:896-918). In both types of model resolution of the SI effect occurs as a result of competitive, spreading activation without the involvement of inhibitory links. These assumptions were tested by randomly presenting participants with trials from semantically-related and lexical control distractor conditions and acquiring image volumes coincident with the estimated peak hemodynamic response for each trial. Overt vocalization of picture names occurred in the absence of scanner noise, allowing reaction time (RT) data to be collected. Analysis of the RT data confirmed the SI effect. Regions showing differential hemodynamic responses during the SI effect included the left mid section of the middle temporal gyrus, left posterior superior temporal gyrus, left anterior cingulate cortex, and bilateral orbitomedial prefrontal cortex. Additional responses were observed in the frontal eye fields, left inferior parietal lobule, and right anterior temporal and occipital cortex. The results are interpreted as indirectly supporting interactive models that allow spreading activation between both conceptual processing and phonological retrieval levels of word production. In addition, the data confirm that selective attention/response suppression has a role in resolving the SI effect similar to the way in which Stroop interference is resolved. We conclude that neuroimaging studies can provide information about the neuroanatomical organization of the lexical system that may prove useful for constraining theoretical models of word production. (C) 2001 Wiley-Liss, Inc.

Document retrieval for e-mail search and discovery using Formal Concept Analysis

This paper discusses a document discovery tool based on Conceptual Clustering by Formal Concept Analysis. The program allows users to navigate e-mail using a visual lattice metaphor rather than a tree. It implements a virtual. le structure over e-mail where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in e-mail discovery. The system described provides more flexibility in retrieving stored e-mails than what is normally available in e-mail clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems and aid knowledge discovery in document collections.

Methods for automatic and assisted image annotation

Personal memories composed of digital pictures are very popular at the moment. To retrieve these media items annotation is required. During the last years, several approaches have been proposed in order to overcome the image annotation problem. This paper presents our proposals to address this problem. Automatic and semi-automatic learning methods for semantic concepts are presented. The automatic method is based on semantic concepts estimated using visual content, context metadata and audio information. The semi-automatic method is based on results provided by a computer game. The paper describes our proposals and presents their evaluations.

Computing semantic relatedness using DBPedia

Extracting the semantic relatedness of terms is an important topic in several areas, including data mining, information retrieval and web recommendation. This paper presents an approach for computing the semantic relatedness of terms using the knowledge base of DBpedia — a community effort to extract structured information from Wikipedia. Several approaches to extract semantic relatedness from Wikipedia using bag-of-words vector models are already available in the literature. The research presented in this paper explores a novel approach using paths on an ontological graph extracted from DBpedia. It is based on an algorithm for finding and weighting a collection of paths connecting concept nodes. This algorithm was implemented on a tool called Shakti that extract relevant ontological data for a given domain from DBpedia using its SPARQL endpoint. To validate the proposed approach Shakti was used to recommend web pages on a Portuguese social site related to alternative music and the results of that experiment are reported in this paper.

Linked Data based Health Information Representation, Visualization and Retrieval System on the Semantic Web

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Semantic enrichment of knowledge sources supported by domain ontologies

This thesis introduces a novel conceptual framework to support the creation of knowledge representations based on enriched Semantic Vectors, using the classical vector space model approach extended with ontological support. One of the primary research challenges addressed here relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. This research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents. The relevant achievements pursued by this thesis are the following: (i) conceptualization of a model that enables the semantic enrichment of knowledge sources supported by domain experts; (ii) development of a method for extending the traditional vector space, using domain ontologies; (iii) development of a method to support ontology learning, based on the discovery of new ontological relations expressed in non-structured information sources; (iv) development of a process to evaluate the semantic enrichment; (v) implementation of a proof-of-concept, named SENSE (Semantic Enrichment kNowledge SourcEs), which enables to validate the ideas established under the scope of this thesis; (vi) publication of several scientific articles and the support to 4 master dissertations carried out by the department of Electrical and Computer Engineering from FCT/UNL. It is worth mentioning that the work developed under the semantic referential covered by this thesis has reused relevant achievements within the scope of research European projects, in order to address approaches which are considered scientifically sound and coherent and avoid “reinventing the wheel”.