Quantum Information Dynamics and Open World Science

One of the fundamental insights of quantum mechanics is that complete knowledge of the state of a quantum system is not possible. Such incomplete knowledge of a physical system is the norm rather than the exception. This is becoming increasingly apparent as we apply scientific methods to increasingly complex situations. Empirically intensive disciplines in the biological, human, and geosciences all operate in situations where valid conclusions must be drawn, but deductive completeness is impossible. This paper argues that such situations are emerging examples of {it Open World} Science. In this paradigm, scientific models are known to be acting with incomplete information. Open World models acknowledge their incompleteness, and respond positively when new information becomes available. Many methods for creating Open World models have been explored analytically in quantitative disciplines such as statistics, and the increasingly mature area of machine learning. This paper examines the role of quantum theory and quantum logic in the underpinnings of Open World models, examining the importance of structural features of such as non-commutativity, degrees of similarity, induction, and the impact of observation. Quantum mechanics is not a problem around the edges of classical theory, but is rather a secure bridgehead in the world of science to come

EpiphaNet: An Interactive Tool to Support Biomedical Discoveries

Background. EpiphaNet (http://epiphanet.uth.tmc.edu) is an interactive knowledge discovery system, which enables researchers to explore visually sets of relations extracted from MEDLINE using a combination of language processing techniques. In this paper, we discuss the theoretical and methodological foundations of the system, and evaluate the utility of the models that underlie it for literature‐based discovery. In addition, we present a summary of results drawn from a qualitative analysis of over six hours of interaction with the system by basic medical scientists. Results: The system is able to simulate open and closed discovery, and is shown to generate associations that are both surprising and interesting within the area of expertise of the researchers concerned. Conclusions: EpiphaNet provides an interactive visual representation of associations between concepts, which is derived from distributional statistics drawn from across the spectrum of biomedical citations in MEDLINE. This tool is available online, providing biomedical scientists with the opportunity to identify and explore associations of interest to them

Modelo para descoberta de conhecimento baseado em associação semântica e temporal entre elementos textuais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2016.O aumento da complexidade nas atividades organizacionais, a vertiginosa expansão da Internet e os avanços da sociedade do conhecimento são alguns dos responsáveis pelo volume inédito de dados digitais. Essa crescente massa de dados apresenta grande potencial para a análise de padrões e descoberta de conhecimento. Nesse sentido, a análise dos relacionamentos presentes nesse imenso volume de informações pode proporcionar novos e, possivelmente, inesperados insights. A presente pesquisa constatou a escassez de trabalhos que consideram adequadamente a semântica e a temporalidade dos relacionamentos entre elementos textuais, características consideradas importantes para a descoberta de conhecimento. Assim, este trabalho propõe um modelo para descoberta de conhecimento que conta com uma ontologia de alto-nível para a representação de relacionamentos e com a técnica Latent Semantic Indexing (LSI) para determinar a força de associação entre termos que não se relacionam diretamente. A representação do conhecimento de domínio, bem como, a determinação da força associativa entre os termos são realizadas levando em conta o tempo em que os relacionamentos ocorrem. A avaliação do modelo foi realizada a partir de dois tipos de experimentos: um que trata da classificação de documentos e outro que trata da associação semântica e temporal entre termos. Os resultados demonstram que o modelo: i) possui potencial para ser aplicado em tarefas intensivas em conhecimento, como a classificação e ii) é capaz de apresentar curvas da força associativa entre dois termos ao longo do tempo, contribuindo para o levantamento de hipóteses e, consequentemente, para a descoberta de conhecimento.Abstract : The increased complexity in organizational activities, the rapid expansion of the Internet and advances in the knowledge society are some of those responsible for the unprecedented volume of digital data. This growing body of data has great potential for pattern analysis and knowledge discovery. In this sense, the analysis of relationships present in this immense volume of information can provide new and possibly unexpected insights. This research found shortages of studies that adequately consider the semantics and the temporality of relationships between textual elements considered important features for knowledge discovery. This work proposes a model of knowledge discovery comprising a high-level ontology for the representation of relationships and the LSI technique to determine the strength of association between terms that do not relate directly. The representation of domain knowledge and the determination of the associative strength between the terms are made taking into account the time in which the relationships occur. The evaluation of the model was made from two types of experiments: one that deals with the classification of documents and another concerning semantics and temporal association between terms. The results show that the model: i) has the potential to be used as a text classifier and ii) is capable of displaying curves of associative force between two terms over time, contributing to the raising of hypotheses and therefore to discover of knowledge

Towards Operational Abduction from a Cognitive Perspective

Diminishing awareness is a consequence of the information explosion: disciplines are becoming increasingly specialized; individuals and groups are becoming ever more insular. This article considers how awareness can be enhanced via operational abductive systems. The goal is to generate and justify suggestions which can span disparate islands of knowledge. Knowledge representation is motivated from a cognitive perspective. Words and concepts are represented as vectors in a high dimensional semantic space automatically derived from a text corpus. Various mechanisms will be presented for computing suggestions from semantic space: information flow, semantic similarity, pre-inductive generalization. The overall goal of this article is to introduce semantic space to the model-based reasoning and abduction community and to illustrate its potential for principled, operational abduction by semi-automatically replicating the Swanson Raynaud/fish oil discovery in medical text