Acquisition and Reuse of Reasoning Knowledge from Textual Cases for Automated Analysis

Analysis is essential for solving complex problems such as diagnosing a patient, investigating an accident or predicting the outcome of a legal case. It is a non-trivial process even for human experts. To assist experts in this process we propose a CBR-based approach for automated problem analysis. In this approach a new problem is analysed by reusing reasoning knowledge from the analysis of a similar problem. To avoid the laborious process of manual case acquisition, the reasoning knowledge is extracted automatically from text and captured in a graph-based representation, which we dubbed Text Reasoning Graph (TRG), that consists of causal, entailment and paraphrase relations. The reuse procedure involves adaptation of a similar past analysis to a new problem by finding paths in TRG that connect the evidence in the new problem to conclusions of the past analysis. The objective is to generate the best explanation of how the new evidence connects to the conclusion. For evaluation, we built a system for analysing aircraft accidents based on the collection of aviation investigation reports. The evaluation results show that our reuse method increases the precision of the retrieved conclusions

Towards Text Mining in Climate Science:Extraction of Quantitative Variables and their Relations

This paper addresses text mining in the cross-disciplinary fields of climate science, marine science and environmental science. It is motivated by the desire for literature-based knowledge discovery from scientific publications. The particular goal is to automatically extract relations between quantitative variables from raw text. This results in rules of the form “If variable X increases, than variable Y decreases”. As a first step in this direction, an annotation scheme is proposed to capture the events of interest – those of change, cause, correlation and feedback – and the entities involved in them, quantitative variables. Its purpose is to serve as an intermediary step in the process of rule extraction. It is shown that the desired rules can indeed be automatically extracted from annotated text. A number of open challenges are discussed, including automatic annotation, normalisation of variables, reasoning with rules in combination with domain knowledge and the need for meta-knowledge regarding context of use

Towards Text Mining in Climate Science:Extraction of Quantitative Variables and their Relations

This paper addresses text mining in the cross-disciplinary fields of climate science, marine science and environmental science. It is motivated by the desire for literature-based knowledge discovery from scientific publications. The particular goal is to automatically extract relations between quantitative variables from raw text. This results in rules of the form “If variable X increases, than variable Y decreases”. As a first step in this direction, an annotation scheme is proposed to capture the events of interest – those of change, cause, correlation and feedback – and the entities involved in them, quantitative variables. Its purpose is to serve as an intermediary step in the process of rule extraction. It is shown that the desired rules can indeed be automatically extracted from annotated text. A number of open challenges are discussed, including automatic annotation, normalisation of variables, reasoning with rules in combination with domain knowledge and the need for meta-knowledge regarding context of use