51,227 research outputs found
Towards Hybridization of Knowledge Representation and Machine Learning
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
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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