In this thesis, methods of creating an information retrieval (IR) model within the Fin-Tech domain are explored. Given the domain-specific and data-scarce environment, methods of artificially generating data to train and evaluate IR models are implemented and their limitations are discussed. The generative model GPT-J 6B is used to generate pseudo-queries for a document corpus, resulting in a training- and test-set of 148 and 166 query-document pairs respectively. Transformer-based models, fine-tuned- and original versions, are put to the test against the baseline model BM25 which historically has been seen as an effective document retrieval model. The models are evaluated using mean reciprocal rank at k (MRR@k) and time-cost to retrieve relevant documents. The main findings are that the historical BM25 model performs well in comparison to the transformer alternatives, it reaches the highest score for MRR@2 = 0.612. The results show that for MRR@5 and MRR@10, a combination model of BM25 and a cross encoder slightly outperforms the baseline reaching scores of MRR@5 = 0.655 and MRR@10 = 0.672. However, the increase in performance is slim and may not be enough to motivate an implementation. Finally, further research using real-world data is required to argue that transformer-based models are more robust in a real-world setting.

When exploring large, high-dimensional datasets, analysts often utilize two techniques for reducing the data to make exploration more tractable. The first technique, dimension reduction, reduces the high-dimensional dataset into a low-dimensional space while preserving high-dimensional structures. The second, clustering, groups similar observations while simultaneously separating dissimilar observations. Existing work presents a number of systems and approaches that utilize these techniques; however, these techniques can cooperate or conflict in unexpected ways. The core contribution of this work is the systematic examination of the design space at the intersection of dimension reduction and clustering when building intelligent, interactive tools in visual analytics. I survey existing techniques for dimension reduction and clustering algorithms in visual analytics tools, and I explore the design space for creating projections and interactions that include dimension reduction and clustering algorithms in the same visual interface. Further, I implement and evaluate three prototype tools that implement specific points within this design space. Finally, I run a cognitive study to understand how analysts perform dimension reduction (spatialization) and clustering (grouping) operations. Contributions of this work include surveys of existing techniques, three interactive tools and usage cases demonstrating their utility, design decisions for implementing future tools, and a presentation of complex human organizational behaviors. / Doctor of Philosophy / When an analyst is exploring a dataset, they seek to gain insight from the data. With data sets growing larger, analysts require techniques to help them reduce the size of the data while still maintaining its meaning. Two commonly-utilized techniques are dimension reduction and clustering. Dimension reduction seeks to eliminate unnecessary features from the data, reducing the number of columns to a smaller number. Clustering seeks to group similar objects together, reducing the number of rows to a smaller number. The contribution of this work is to explore how dimension reduction and clustering are currently being used in interactive visual analytics systems, as well as to explore how they could be used to address challenges faced by analysts in the future. To do so, I survey existing techniques and explore the design space for creating visualizations that incorporate both types of computations. I look at methods by which an analyst could interact with those projections in other to communicate their interests to the system, thereby producing visualizations that better match the needs of the analyst. I develop and evaluate three tools that incorporate both dimension reduction and clustering in separate computational pipelines. Finally, I conduct a cognitive study to better understand how users think about these operations, in order to create guidelines for better systems in the future.

No / Objective It has been documented that pain in people with dementia is often under-reported and poorly detected. The reasons for this are not clearly defined. This project aimed to explore semantic concepts of pain in people with dementia and whether this is associated with clinical pain report. Design Cohort study with nested cross-sectional analysis. Setting Acute general hospital medical wards for older people. Subjects People with dementia (N = 26) and control participants (N = 13). Methods Two subtests of semantic memory for pain: 1) Identifying painful situations from a standardized range of pictures; 2) Describing the concept of pain. Participants also indicated whether they were in pain or not, were observed for pain (PAINAD scale) and completed the Wong–Baker FACES scale to indicate pain severity. Results Compared with the control group, people with dementia were less able to identify painful situations and used fewer categories to define their concept of pain. In turn, the performance on these two measures was related to the reported presence and, albeit less strongly, to the reported severity of pain, indicating that a reduction in semantic memory for pain is associated with a decline in reported pain. Conclusions This study is the first to show that semantic memory for pain is diminished in dementia patients. When using clinical pain tools, clinicians should consider these effects which may bias clinical pain ratings when they evaluate and manage pain in these patients. This might improve the recognition and management of pain in people with dementia. / Alzheimer's Society and the BUPA Foundation (Grant reference number: 131).

In order to compete with the global giants, enterprises are concentrating on their core competencies and collaborating with organizations that compliment their skills and core activities. The current trend is to develop temporary alliances of independent enterprises, in which companies can come together to share skills, core competencies and resources. However, knowledge sharing and communication among multidiscipline companies is a complex and challenging problem. In a collaborative environment, the meaning of knowledge is drastically affected by the context in which it is viewed and interpreted; thus necessitating the treatment of structure as well as semantics of the data stored in enterprise repositories. Keeping the present market and technological scenario in mind, this research aims to propose tools and techniques that can enable companies to assimilate distributed information resources and achieve their business goals.

The semantic model developed in this research was in response to the difficulty a group of mathematics learners had with conventional mathematical language and their interpretation of mathematical constructs. In order to develop the model ideas from linguistics, psycholinguistics, cognitive psychology, formal languages and natural language processing were investigated. This investigation led to the identification of four main processes: the parsing process, syntactic processing, semantic processing and conceptual processing. The model showed the complex interdependency between these four processes and provided a theoretical framework in which the behaviour of the mathematics learner could be analysed. The model was then extended to include the use of technological artefacts into the learning process. To facilitate this aspect of the research, the theory of instrumentation was incorporated into the semantic model. The conclusion of this research was that although the cognitive processes were interdependent, they could develop at different rates until mastery of a topic was achieved. It also found that the introduction of a technological artefact into the learning environment introduced another layer of complexity, both in terms of the learning process and the underlying relationship between the four cognitive processes.

In recent years, the use of using metadata to describe and share resources has grown in importance, especially in the context of the Semantic Web.  However, access to metadata is difficult for users without experience with description logic or formal languages, and currently this description applies to most web users.  There is a strong need for interfaces that provide easy access to semantic metadata, enabling novice users to browse, query and create it easily. This thesis describes a natural language generation interface to semantic metadata called LIBER (Language Interface for Browsing and Editing Rdf), driven by domain ontologies which are integrated with domain-specific linguistic information.  LIBER uses the linguistic information to generate fluent descriptions and search terms through syntactic aggregation. The tool contains three modules to support metadata creation, querying and browsing, which implement the WYSIWYM (What You See Is What You Meant) natural language generation approach.  Users can add and remove information by editing system-generated feedback texts.  Two studies have been conducted to evaluate LIBER’s usability, and compare it to a different Semantic Web interface.  The studies showed subjects with no prior experience of the Semantic Web could use LIBER effectively to create, search and browse metadata, and were a useful source of ideas in which to improve LIBER’s usability.  However, the results of these studies were less positive than we had hoped, and users actually preferred the other Semantic Web tool.  This has raised questions about which user audience LIBER should aim for, and the extent to which the underlying ontologies influence the usability of the interface. LIBER’s portability to other domains is supported by a tool with which ontology developers without a background in linguistics can prepare their ontologies for use in LIBER by adding the necessary linguistic information.

The purpose of this work is to characterize the problem of semantic presuppositions generated by definite descriptions in subject-position in the light of a reassessment of the semantic framework of the Russell/Strawson controversy and analyze what would be the early and the later Wittgenstein's solution to such a problem. In the first part, the Russell/Strawson controversy is characterized. On the basis of Strawson's account, a general concept of semantic presupposition against which other theories may be tested is constructed. This allows the formulation of the problem above mentioned. Further analysis of the accounts involved reveals that Frege's concept of a 'semantic prerequisite' generated by definite descriptions in subject-position is an instance of the general concept. But Frege also held the view that simple proper names do not generate semantic prerequisites. The Fregean referential dualism suggests that the Russell/Strawson controversy, as far as only these authors' accounts are involved, is undecidable at the purely semantic level. This is the semantic framework against which Wittgenstein's philosophies are tested in the second part. The "Tractatus" adopts a modified version of the Russellian Theory of Descriptions. Even so, the Tractarian account seems to be ultimately equivalent to Russell's. Further analysis reveals that the doctrine of simple signs in isolation, but not its conjunction with the picture theory, is consistent with the general concept of semantic presupposition. The "Investigations " adopts the programmatic principle of searching for the use of the words. But the question about the 'referring use' of descriptions in a specific language-game is consistent with, and in the spirit of, the "Investigations". The framework of the question involves the appeal to the Kripkean notions of 'semantic referent' and 'speaker's referent'. The analysis of the referring use in the language-game of reporting an event reveals that the later Wittgenstein tends to reject the semantic concept of presupposition. Further analysis reveals that he would tend to reject Russell's Theory of Descriptions and most of the variants of the pragmatic concept. The analysis seems to confirm that the Russell/Strawson dispute is idle at the purely semantic level. Even so, the later Wittgenstein's account of language is such that it is possible to imagine some particular language-games in which relationships occur that bear some analogies with the one of semantic presupposition.

The purpose of this study was to examine whether repetition affects the experience of game animation. Through an experiment we divided people with gaming habits into two groups to view an animated run-cycle. The cycle, almost a second long, was repeated continuously. One of the groups viewed the cycle for 10 seconds whereas the other group viewed the same cycle, but for 120 seconds. The participants experiences were documented through a questionnaire that was later supplemented with a more thorough interview. Their statements were compiled using open coding in order to find similarities and differences between the two groups.The results of the experiment show tendencies that the group that viewed the cycle for 120 seconds expressed a change of experience with time, often for the worse, even if the animation itself didn't change. This differs from the other group, which had viewed the cycle for 10 seconds, which generally didn't report any changes of experience. From this we draw the conclusion that, in an experiment stripped of story, environment and context, game animation that repeats frequently tends to cause a change of experience with repetitions. How and why this change of experience occurs can only be discussed in this study even though there's similarities with theories as PECMA Flow and semanticsatiation. Further studies into the subject is required to fully understand the phenomenon and to apply the findings in real, complex games. / Syftet med den här studien är att undersöka om repetition påverkar upplevelsen av spelanimation. Genom ett experiment delade vi in personer med spelvana i två grupper för att titta på en animerad springcykel. Cykeln var knappt en sekund lång och upprepades kontinuerligt. En grupp fick titta på cykeln i 10 sekunder och den andragruppen fick se samma cykel, fast i 120 sekunder. Deltagarnas upplevelser registrerades via ett frågeformulär som sedan kompletterades av en mer djupgående intervju. Deras svar sammanställdes via öppen kodning för att finna samband och skillnader mellan de två grupperna. Experimentets resultat visar på tendenser att gruppen som sett på cykeln i 120 sekunder uttryckte att upplevelsen av animationen förändrades med tiden, ofta till det sämre, även om animationen inte ändrades. Detta skiljde sig från gruppen som endast såg 10 sekunder av springcykeln som generellt inte uttryckte någon upplevelseförändring. Utifrån detta drar vi slutsatsen att, i ett experiment avskilt från story, miljö och kontext, tenderar spelanimation som upprepas att orsaka en upplevelseförändring över tid. Hur och varför denna upplevelseförändring uppstår går endast att diskutera i den här undersökningen även om det går att se samband med teorier som PECMA Flow och semantic satiation. Vidare studier inom ämnet krävs för att fullt förstå fenomenet och för att kunna applicera fynden i verkliga, komplexa spel.

A diversidade biológica é essencial para a sustentabilidade da vida na Terra e motiva numerosos esforços para coleta de dados sobre espécies, dando origem a uma grande quantidade de informação. Esses dados são geralmente armazenados em bancos de dados relacionais. Pesquisadores usam esses bancos de dados para extrair conhecimento e compartilhar novas descobertas. No entanto, atualmente a busca tradicional (baseada em palavras-chave) já não é adequada para ser usada em grandes quantidades de dados heterogêneos, como os de biodiversidade. Ela tem baixa precisão e revocação para esse tipo de dado. Este trabalho apresenta uma nova arquitetura para abordar esse problema aplicando técnicas de buscas semânticas em dados sobre biodiversidade e usando formatos e ferramentas da Web Semântica para representar esses dados. A busca semântica tem como objetivo melhorar a acurácia dos resultados de buscas com o uso de ontologias para entender os objetivos dos usuários e o significado contextual dos termos utilizados. Este trabalho também apresenta os resultados de testes usando um conjunto de dados representativos sobre biodiversidade do Instituto Nacional de Pesquisas da Amazônia (INPA) e do Museu Paraense Emílio Goeldi (MPEG). Ontologias permitem que conhecimento seja organizado em espaços conceituais de acordo com seu significado. Para a busca semântica funcionar, um ponto chave é a criação de mapeamentos entre os dados (neste caso, dados sobre biodiversidade do INPA e MPEG) e termos das ontologias que os descrevem, neste caso: a classificação taxonômica de espécies e a OntoBio, a ontologia de biodiversidade do INPA. Esses mapeamentos foram criados depois que extraímos a classificação taxonômica do site Catalog of Life (CoL) e criamos uma nova versão da OntoBio. Um protótipo da arquitetura foi construído e testado usando casos de uso e dados do INPA e MPEG. Os resultados dos testes mostraram que a abordagem da busca semântica tinha uma melhor precisão (28% melhor) e revocação (25% melhor) quando comparada com a busca por palavras-chave. Eles também mostraram que é possível conectar facilmente os dados mapeados a outras fontes de dados abertas, como a fonte Amazon Forest Linked Data do Instituto Nacional de Pesquisas Espaciais. (INPE) / Biological diversity is of essential value to life sustainability on Earth and motivates many efforts to collect data about species. That gives rise to a large amount of information. Biodiversity data, in most cases, is stored in relational databases. Researchers use this data to extract knowledge and share their new discoveries about living things. However, nowadays the traditional search approach (based basically on keywords matching) is not appropriate to be used in large amounts of heterogeneous biodiversity data. Search by keyword has low precision and recall in this kind of data. This work presents a new architecture to tackle this problem using a semantic search system for biodiversity data and semantic web formats and tools to represent this data. Semantic search aims to improve search accuracy by using ontologies to understand user objectives and the contextual meaning of terms used in the search to generate more relevant results. This work also presents test results using a set of representative biodiversity data from the National Research Institute for the Amazon (INPA) and the Emilio Gueldi Museum in Pará (MPEG). Ontologies allow knowledge to be organized into conceptual spaces in accordance to its meaning. For semantic search to work, a key point is to create mappings between the data (in this case, INPAs and MPEGs biodiversity data) and the ontologies describing it, in this case: the species taxonomy (a taxonomy is an ontology where each class can have just one parent) and OntoBio, INPAs biodiversity ontology. These mappings were created after we extracted the taxonomic classification from the Catalogue of Life (CoL) website and created a new version of OntoBio. A prototype of the architecture was built and tested using INPAs and MPEGs use cases and data. The results showed that the semantic search approach had a better precision (28% improvement) and recall (25% improvement) when compared to keyword based search. They also showed that it was possible to easily connect the mapped data to other Linked Open Data sources, such as the Amazon Forest Linked Data from the National Institute for Space Research (INPE)

Com os avanços dos processos da microeletrônica temos dispositivos menores e com maior poder de computação e comunicação. Um Ambiente Pervasivo contém diferentes dispositivos, tais como sensores, atuadores, eletroeletrônicos e dispositivos móveis que interagem com a pessoa de forma natural ao conhecer o contexto. A diversidade de dispositivos e informações do Ambiente Pervasivo introduz um problema de interoperabilidade. Um Ambiente Pervasivo é dinâmico devido à mobilidade do usuário, a variedade de dispositivos. Neste trabalho, é proposto um modelo semântico de contexto para permitir interoperabilidade e fornecer suporte ao dinamismo do Ambiente Pervasivo. O modelo proposto contém características da modelagem de contexto realizadas por trabalhos anteriores, assim como sua integração com a modelagem de preferências das pessoas, políticas de privacidade e serviços. Verificou-se que o modelo de contexto proposto é adequado mediante sua aplicação em um Estudo de Caso e mediante testes realizados. Mostra-se que a modelo de contexto utilizado ontologias e Serviços Web Semânticos permite tratar com informação incompleta e inconsistente, bem como fornece suporte na interoperabilidade e ao dinamismo do Ambiente Pervasivo. / Advances in microelectronic processes have allowed smaller devices with more computation and communication power. Pervasive environment contains different devices like electronic sensor, actuators and mobile devices which interact with the person naturally after the context is known. The device and information diversity introduce an interoperability problem. Pervasive environments are dynamics because of user\'s mobility and a variety of devices. In this work, we propose a context model to allow interoperability and to give support to pervasive environment dynamism. The proposed model contains features of context modeling developed in previous works, as well as, their integration with the modeling of the people\'s preferences, privacy policies and services. It was verified that the context model is appropriate by their application in a Case Study and by accomplished tests. It is shown that the model of context using ontologies and Semantic Web Services allow us to work with inconsistent and incomplete information, as well as gives support to interoperability and dynamism of the Pervasive Environment.