51,227 research outputs found

    Towards Hybridization of Knowledge Representation and Machine Learning

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    Machine learning and knowledge representation are two fields of artificial intelligence that lead with intelligent reasoning, each one differently. When Knowledge Representation (KR) focuses more on the epistemological face of knowledge to carry out a power-expression model with detriment of the computation efficiency. Machine learning pays more attention to the computation efficiency, often with loss of expressing power. In this paper we show that features of one may overwhelm drawbacks of the other. Taking the uncertainty artefact from machine learning and symbolic representation from KR, we propose in this paper a new memory modelling for knowledge based systems which is at the same time machine-learning structure and a knowledge representation model. In terms of machine learning, our structure allows an unlimited flexibility where no restraining architecture is imposed at the beginning (think about decision trees). Classification can be performed with incomplete vectors where the most likely corresponding class is assigned to the vector with missing attributes. Viewed as a knowledge model, a basic knowledge is easily specified graphically. Inference is defined by rules expressed in the same manner, where the existing sub-instances are used to generate new connections and entities. Inference on existing knowledge is described in two algorithms. Our approach is mainly based on the representation of the context concept. Our model brings together advantages of the symbolic knowledge representation, namely human to computer knowledge coding, and those of machine learning structures, namely ease of efficient coding of inference to perform the so-called intelligent tasks like pattern recognition, prediction and others. Its graphical representation allows visualization of both dynamic and static sides of the model (i.e. inference and knowledge)

    Indexing the Event Calculus with Kd-trees to Monitor Diabetes

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    Personal Health Systems (PHS) are mobile solutions tailored to monitoring patients affected by chronic non communicable diseases. A patient affected by a chronic disease can generate large amounts of events. Type 1 Diabetic patients generate several glucose events per day, ranging from at least 6 events per day (under normal monitoring) to 288 per day when wearing a continuous glucose monitor (CGM) that samples the blood every 5 minutes for several days. This is a large number of events to monitor for medical doctors, in particular when considering that they may have to take decisions concerning adjusting the treatment, which may impact the life of the patients for a long time. Given the need to analyse such a large stream of data, doctors need a simple approach towards physiological time series that allows them to promptly transfer their knowledge into queries to identify interesting patterns in the data. Achieving this with current technology is not an easy task, as on one hand it cannot be expected that medical doctors have the technical knowledge to query databases and on the other hand these time series include thousands of events, which requires to re-think the way data is indexed. In order to tackle the knowledge representation and efficiency problem, this contribution presents the kd-tree cached event calculus (\ceckd) an event calculus extension for knowledge engineering of temporal rules capable to handle many thousands events produced by a diabetic patient. \ceckd\ is built as a support to a graphical interface to represent monitoring rules for diabetes type 1. In addition, the paper evaluates the \ceckd\ with respect to the cached event calculus (CEC) to show how indexing events using kd-trees improves scalability with respect to the current state of the art.Comment: 24 pages, preliminary results calculated on an implementation of CECKD, precursor to Journal paper being submitted in 2017, with further indexing and results possibilities, put here for reference and chronological purposes to remember how the idea evolve
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