Controlled Natural Languages for Knowledge Representation and Reasoning

Controlled natural languages (CNLs) are effective languages for knowledge representation and reasoning. They are designed based on certain natural languages with restricted lexicon and grammar. CNLs are unambiguous and simple as opposed to their base languages. They preserve the expressiveness and coherence of natural languages. In this paper, it mainly focuses on a class of CNLs, called machine-oriented CNLs, which have well-defined semantics that can be deterministically translated into formal languages to do logical reasoning. Although a number of machine-oriented CNLs emerged and have been used in many application domains for problem solving and question answering, there are still many limitations: First, CNLs cannot handle inconsistencies in the knowledge base. Second, CNLs are not powerful enough to identify different variations of a sentence and therefore might not return the expected inference results. Third, CNLs do not have a good mechanism for defeasible reasoning. This paper addresses these three problems and proposes a research plan for solving these problems. It also shows the current state of research: a paraconsistent logical framework from which six principles that guide the user to encode CNL sentences were created. Experiment results show this paraconsistent logical framework and these six principles can consistently and effectively solve word puzzles with injections of inconsistencies

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries

Estimating Information in Earth System Data with Machine Learning

El aprendizaje automático ha hecho grandes avances en la ciencia e ingeniería actuales en general y en las ciencias de la Tierra en particular. Sin embargo, los datos de la Tierra plantean problemas particularmente difíciles para el aprendizaje automático debido no sólo al volumen de datos implicado, sino también por la presencia de correlaciones no lineales tanto espaciales como temporales, por una gran diversidad de fuentes de ruido y de incertidumbre, así como por la heterogeneidad de las fuentes de información involucradas. Más datos no implica necesariamente más información. Por lo tanto, extraer conocimiento y contenido informativo mediante el análisis y el modelado de datos resulta crucial, especialmente ahora donde el volumen y la heterogeneidad de los datos aumentan constantemente. Este hecho requiere avances en métodos que puedan cuantificar la información y caracterizar las distribuciones e incertidumbres con precisión. Cuantificar el contenido informativo a los datos y los modelos de nuestro sistema son problemas no resueltos en estadística y el aprendizaje automático. Esta tesis introduce nuevos modelos de aprendizaje automático para extraer conocimiento e información a partir de datos de observación de la Tierra. Proponemos métodos núcleo ('kernel methods'), procesos gaussianos y gaussianización multivariada para tratar la incertidumbre y la cuantificación de la información, y aplicamos estos métodos a una amplia gama de problemas científicos del sistema terrestre. Estos conllevan muchos tipos de problemas de aprendizaje, incluida la clasificación, regresión, estimación de densidad, síntesis, propagación de errores y estimación de medidas teóricas de la información. También demostramos cómo funcionan estos métodos con diferentes fuentes de datos, provenientes de distintos sensores (radar, multiespectrales, hiperespectrales), productos de datos (observaciones, reanálisis y simulaciones de modelos) y cubos de datos (agregados de varias fuentes de datos espacial-temporales ). Las metodologías presentadas nos permiten cuantificar y visualizar cuáles son las características relevantes que gobiernan distintos métodos núcleo, tales como clasificadores, métodos de regresión o incluso las medidas de independencia estadística, como propagar mejor los errores y las distorsiones de los datos de entrada con procesos gaussianos, así como dónde y cuándo se puede encontrar más información en cubos arbitrarios espacio-temporales. Las técnicas presentadas abren una amplia gama de posibles casos de uso y de aplicaciones, con las que prevemos un uso más extenso y robusto de algoritmos estadísticos en las ciencias de la Tierra y el clima.Machine learning has made great strides in today's Science and engineering in general and Earth Sciences in particular. However, Earth data poses particularly challenging problems for machine learning due to not only the volume of data, but also the spatial-temporal nonlinear correlations, noise and uncertainty sources, and heterogeneous sources of information. More data does not necessarily imply more information. Therefore, extracting knowledge and information content using data analysis and modeling is important and is especially prevalent in an era where data volume and heterogeneity is steadily increasing. This calls for advances in methods that can quantify information and characterize distributions accurately. Quantifying information content within our system's data and models are still unresolved problems in statistics and machine learning. This thesis introduces new machine learning models to extract knowledge and information from Earth data. We propose kernel methods, Gaussian processes and multivariate Gaussianization to handle uncertainty and information quantification and we apply these methods to a wide range of Earth system science problems. These involve many types of learning problems including classification, regression, density estimation, synthesis, error propagation and information-theoretic measures estimation. We also demonstrate how these methods perform with different data sources including sensory data (radar, multispectral, hyperspectral, infrared sounders), data products (observations, reanalysis and model simulations) and data cubes (aggregates of various spatial-temporal data sources). The presented methodologies allow us to quantify and visualize what are the salient features driving kernel classifiers, regressors or dependence measures, how to better propagate errors and distortions of input data with Gaussian processes, and where and when more information can be found in arbitrary spatial-temporal data cubes. The presented techniques open a wide range of possible use cases and applications and we anticipate a wider adoption in the Earth sciences

The construction of the gang in British Columbia : Mafioso, gangster, or thug? An examination of the uniqueness of the BC gangster phenomenon

This thesis explores the structure, demographics, and history of gangs in British Columbia (BC), Canada, through a social constructionist lens. The purpose of this research is for the reader to consider the current state of gangs in BC as inherently different from other places in the world, to assist in understanding why there may be misconceptions, and to promote the research and implementation of more appropriate context-specific interventions. Building on previous work conducted as a Vancouver Police officer of over 27 years, I participated in field observations with gang units in Toronto and Hobbema, Canada; Chicago and Los Angeles, USA; and London, England. I also examined gang typologies and definitions in academic literature as a segment of the historical context of gang research and highlight how these bodies of literature contribute to the social construction of gangs. A historical review of media-reported gang violence in BC from 1903 to 2012 demonstrates that gang violence is not a new phenomenon, and its history is an essential element in the constructed concept of the gang. As well, I conducted semi-structured interviews with participants who either police gangs, work with gangs, or were former gang members to get their perspectives on the issue. The research findings highlight that gangs in BC are distinct from other locations. Whereas traditional at-risk youth dominate gangs elsewhere, BC has a large number of youth involved in gangs who do not appear to possess the typical antecedents to gang involvement. Because of these differences, it is crucial that anti-gang initiatives and policies be adapted to the BC context to effectively reduce gang activity and ultimately eliminate gangs

Barry Smith an sich

Festschrift in Honor of Barry Smith on the occasion of his 65th Birthday. Published as issue 4:4 of the journal Cosmos + Taxis: Studies in Emergent Order and Organization. Includes contributions by Wolfgang Grassl, Nicola Guarino, John T. Kearns, Rudolf Lüthe, Luc Schneider, Peter Simons, Wojciech Żełaniec, and Jan Woleński

Active learning of link specifications using decision tree learning

In this work we presented an implementation that uses decision trees to learn highly accurate link specifications. We compared our approach with three state-of-the-art classifiers on nine datasets and showed, that our approach gives comparable results in a reasonable amount of time. It was also shown, that we outperform the state-of-the-art on four datasets by up to 30%, but are still behind slightly on average. The effect of user feedback on the active learning variant was inspected pertaining to the number of iterations needed to deliver good results. It was shown that we can get FScores above 0.8 with most datasets after 14 iterations

Resource discovery in heterogeneous digital content environments

The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice.The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice