Computational Scientific Knowledge Discovery Task

References

(Buchanan & Livingston, 2004) ⇒ Bruce G. Buchanan, and Gary R. Livingston. (2004). “Toward Automated Discovery in the Biological Sciences.” In: AI Magazine 25(1).
- Knowledge discovery programs in the biological sciences require flexibility in the use of symbolic data and semantic information. Because of the volume of non-numeric, as well as numeric, data, the programs must be able to explore a large space of possibly interesting relationships to discover those that are novel and interesting. Thus, the framework for the discovery program must facilitate proposing and selecting the next task to perform and performing the selected tasks. The framework we describe, called the agenda- and justification-based framework, has several properties that are desirable in semi-autonomous discovery systems: It provides a mechanism for estimating the plausibility of tasks, it uses heuristics to propose and perform tasks, and it facilitates the encoding of general discovery strategies and the use of background knowledge. We have implemented the framework and our heuristics in a prototype program, HAMB, and have evaluated them in the domain of protein crystallization. Our results demonstrate that both reasons given for performing tasks and estimates of the interestingness of the concepts and hypotheses examined by HAMB contribute to its performance and that the program can discover novel, interesting relationships in biological data.

(Bannai et al., 2002) ⇒ Hideo Bannai, Yoshinori Tamada, Osamu Maruyama, Kenta Nakai and Satoru Miyano. (2002). “Extensive feature detection of N-terminal protein sorting signals.” In: Bioinforatics, 18(2).
- Several very important aspects in the process of scientific knowledge discovery are: 1) the generation or discovery of good attributes, and ways of looking at the data, which is then used to explain the data, 2) the incorporation of and reflection on existing knowledge, and 3) the trial and error interaction between the expert and the problem.