Towards a possibility-theoretic approach to uncertainty in medical data interpretation for text generation

Many real-world applications that reason about events obtained from raw data must deal with the problem of temporal uncertainty, which arises due to error or inaccuracy in data. Uncertainty also compromises reasoning where relationships between events need to be inferred. This paper discusses an approach to dealing with uncertainty in temporal and causal relations using Possibility Theory, focusing on a family of medical decision support systems that aim to generate textual summaries from raw patient data in a Neonatal Intensive Care Unit. We describe a framework to capture temporal uncertainty and to express it in generated texts by mean of linguistic modifiers. These modifiers have been chosen based on a human experiment testing the association between subjective certainty about a proposition and the participants’ way of verbalising it.peer-reviewe

Knowledge representation issues in control knowledge learning

Seventeenth International Conference on Machine Learning. Stanford, CA, USA, 29 June-2 July, 2000Knowledge representation is a key issue for any machine learning task. There have already been many comparative studies about knowledge representation with respect to machine learning in classication tasks. However, apart from some work done on reinforcement learning techniques in relation to state representation, very few studies have concentrated on the eect of knowledge representation for machine learning applied to problem solving, and more specically, to planning. In this paper, we present an experimental comparative study of the eect of changing the input representation of planning domain knowledge on control knowledge learning. We show results in two classical domains using three dierent machine learning systems, that have previously shown their eectiveness on learning planning control knowledge: a pure ebl mechanism, a combination of ebl and induction (hamlet), and a Genetic Programming based system (evock).Publicad

Pictorial Representation And Moral Knowledge

The idea that pictorial art can have cognitive value, that it can enhance our understanding of the world and of our own selves, has had many advocates in art theory and philosophical aesthetics alike. It has also been argued, however, that the power of pictorial representation to convey or enhance knowledge, in particular knowledge with moral content, is not generalized across the medium

Representation of probabilistic scientific knowledge

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2013 Soldatova et al; licensee BioMed Central Ltd.The theory of probability is widely used in biomedical research for data analysis and modelling. In previous work the probabilities of the research hypotheses have been recorded as experimental metadata. The ontology HELO is designed to support probabilistic reasoning, and provides semantic descriptors for reporting on research that involves operations with probabilities. HELO explicitly links research statements such as hypotheses, models, laws, conclusions, etc. to the associated probabilities of these statements being true. HELO enables the explicit semantic representation and accurate recording of probabilities in hypotheses, as well as the inference methods used to generate and update those hypotheses. We demonstrate the utility of HELO on three worked examples: changes in the probability of the hypothesis that sirtuins regulate human life span; changes in the probability of hypotheses about gene functions in the S. cerevisiae aromatic amino acid pathway; and the use of active learning in drug design (quantitative structure activity relation learning), where a strategy for the selection of compounds with the highest probability of improving on the best known compound was used. HELO is open source and available at https://github.com/larisa-soldatova/HELO.This work was partially supported by grant BB/F008228/1 from the UK Biotechnology & Biological Sciences Research Council, from the European Commission under the FP7 Collaborative Programme, UNICELLSYS, KU Leuven GOA/08/008 and ERC Starting Grant 240186

Knowledge representation for commonality

Domain-specific knowledge necessary for commonality analysis falls into two general classes: commonality constraints and costing information. Notations for encoding such knowledge should be powerful and flexible and should appeal to the domain expert. The notations employed by the Commonality Analysis Problem Solver (CAPS) analysis tool are described. Examples are given to illustrate the main concepts

Knowledge Representation and WordNets

Knowledge itself is a representation of “real facts”. Knowledge is a logical model that presents facts from “the real world” witch can be expressed in a formal language. Representation means the construction of a model of some part of reality. Knowledge representation is contingent to both cognitive science and artificial intelligence. In cognitive science it expresses the way people store and process the information. In the AI field the goal is to store knowledge in such way that permits intelligent programs to represent information as nearly as possible to human intelligence. Knowledge Representation is referred to the formal representation of knowledge intended to be processed and stored by computers and to draw conclusions from this knowledge. Examples of applications are expert systems, machine translation systems, computer-aided maintenance systems and information retrieval systems (including database front-ends).knowledge, representation, ai models, databases, cams