Knowledge extraction

The next question is how to represent the reacting bonds of the reaction center. We wanted to develop a method for reaction classification that can be used for knowledge extraction from reaction databases for the prediction of the products of a reaction. Thus, we could only use physicochemical values of the reactants, because these should tell us what products we obtain. 

A wider variety of reaction types involving reactions at bonds to oxygen atom bearing functional groups was investigated by the same kind of methodology [30]. Reaction classification is an essential step in knowledge extraction from reaction databases. This topic is discussed in Section 10.3.1 of this book. 

Clearly, the extraction of knowledge on chemical reactions from the information contained in reaction databases (see Section 5.12) is quite a challenging problem. The huge amount of information stored in reaction databases - the larger ones contain several million reactions - renders them quite attractive for knowledge extraction. However, only a few attempts have been made to extract knowledge automatically. 

Before concluding this section it should be emphasi/cd that knowledge extraction from reaction databases is still a challenging problem having many important applications. There is still room for new approaches to this task, Furthermore, groat efforts should be made to improve the depth of information stored in reaction databases. With the introduction of electronic lab journals, the primary information on a chemical reaction gained in the laboratoiy becomes directly available. 

More elaborate scheme.s can he envisaged. Thus, a. self-organizing neural network as obtained by the classification of a set of chemical reactions as outlined in Section 3,5 can be interfaced with the EROS system to select the reaction that acmaliy occurs from among various reaction alternatives. In this way, knowledge extracted from rcaetion databases can be interfaced with a reaction prediction system,... 

RuleMaker is a knowledge extraction utility for building and testing the decision logic contained within Radial modules. The logic is specified as a table of examples of correct expert decisions for each module. RuleMaker transforms each example set into an equivalent decision tree, and automatically generates the body of the module in the form of Radial code. System builders may also choose to enter Radial code directly, although they usually prefer to work with example tables. 

Knowledge Extraction. Expert systems are usually used to solve hard" problems for which the solution methodology is not documented. An expert is a person who can provide the highest quality answers or advice for a specific problem domain. Unless the expert routinely teaches the problem-solving method, he or she will probably have difficulty in clearly describing the method. 

M. Holena, M. Baerns, Feedforward neural networks in catalysis, a tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction, Catal. Today, 81 (2003), 485-494. 

Improvement of the Search Strategy by Means of Knowledge Extraction... 

While most combinatorial researches reported up to now involve the use of GA, using the traditional crossover and mutation operators (e.g. WGS 1), it has also been proposed to design new operators for each specific application, to improve search efficiency by means of knowledge extraction [32]. Hence, new methods that combine ES with a knowledge extraction engine have been reported recently within the field of heterogeneous catalysis, such as mining association rules [12, 18, 30, 33] and neural networks [19, 29, 34]. 

Carufhers, J.M., Lauterbach, J.A., Thomson, K.T., Venkatasubramanian, V., Snively, C.M., Bhan, A., Katare, S. and Oskarsdottir, G. (2003) Catalyst design knowledge extraction from high-throughput experimentation. J. Catal., 216, 98. 

Holena, M., Baerns, M., Feedforward neural networks in catalysis, a tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction, Catal. Today 2003, 81, 485-494. Serra, J. M., Corma, A., Argente, E., Valero, S., Botti, V., Neuronal networks for modeling of kinetic reaction data applicable to catalyst scale up and process control and optimization in the frame of combinatorial catalysis, Appl. Catal. A 2003, 254, 133-145. 

Kohlus R, Bottlinger M. 1993. Particle shape analysis as an example of knowledge extraction by neural nets. Part. Part. Syst. Charact. 10 275 -278. 

To undertake QSRR studies one needs two kinds of input data. One is a set of quantitatively comparable retention data (dependent variable) for a sufficiently large (for statistical reasons) set of analytes. The other is a set of quantities (independent variables) assumed to account for structural differences among the chromatographed analytes. Through the use of chemometric computational techniques, retention parameters are characterized in terms of various descriptors of analytes (or their combinations) or in terms of systematic knowledge extracted (learned) from these descriptors. 

The catalyst has been designed as given in Ref The proposed process of knowledge extraction consists in planning HTE experiments in a way allowing for the discrimination of the models of catalytic reactions, determination of the kinetic constants, and relating them to the catalyst microstructure. The proposed forward modeling is realized by the application of the... 

The inference engine, the procedure which generates the consequences, conclusions, or decisions from the existing knowledge extracted from the knowledge base... 

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