Literature-based knowledge discovery in climate science

Climate change caused by anthropogenic activity is one of the biggest challenges of our time. Researchers are striving to understand the effects of global warming on the ecological systems of the oceans, and how these ecological systems influence the global climate, a line of research that is crucial in order to counteract or adapt to the effects of global warming. A major challenge that researchers in this area are facing, is the huge amount of potentially relevant literature, as insights from widely different fields such as biology, chemistry, climatology and oceanography can prove crucial in understanding the effects of global warming on the oceans. To alleviate some of the work load from researchers, information extraction tools can be used to extract relevant information from the scientific literature automatically, and discovery support tools can be developed to assist researchers in their efforts. This master thesis conducts fundamental research into the development of discovery support tools for oceanographic climate science, focusing primarily on the information extraction component

Literature-based knowledge discovery in climate science

Climate change caused by anthropogenic activity is one of the biggest challenges of our time. Researchers are striving to understand the effects of global warming on the ecological systems of the oceans, and how these ecological systems influence the global climate, a line of research that is crucial in order to counteract or adapt to the effects of global warming. A major challenge that researchers in this area are facing, is the huge amount of potentially relevant literature, as insights from widely different fields such as biology, chemistry, climatology and oceanography can prove crucial in understanding the effects of global warming on the oceans. To alleviate some of the work load from researchers, information extraction tools can be used to extract relevant information from the scientific literature automatically, and discovery support tools can be developed to assist researchers in their efforts. This master thesis conducts fundamental research into the development of discovery support tools for oceanographic climate science, focusing primarily on the information extraction component

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