Abstract
The problem of constructing and expanding the temporal knowledge base for the information-control system is considered. This knowledge base is formally represented by the Markov logic network. It is shown that the behavior of the control object of a given class can be reflected in the form of a set of weighted temporal rules. These rules are formed on the basis of identifying links between events that reflect known variants of the behavior of the control object. A method is proposed for calculating the weights of temporal rules in a Markov logic network for a given level of detail of the control object. The level of detail is determined by the context for executing the sequences of control actions and for weighted temporal rules is specified by selecting subsets of the event attributes. The method includes such basic phases: preparation of a subset of temporal rules for a given level of detail; finding the weights of the rules taking into account the a priori probabilities of the event traces. The method creates conditions for supporting management decisions in information management systems at various levels of detail of complex management objects. Decision support is provided by predicting the probability of success in executing a sequence of actions that implement the management function in the current situation. These probabilities are determined using the weights of the temporal rules.
Highlights
Modern methods of automated construction of knowledge bases [1, 2] use information from databases, sites on the Internet, and logs that reflect the functioning of various systems and processes, to isolate sets of dependencies in the format of rules and templates [3].In contrast to the traditional paradigm of building knowledge bases like “creating knowledge by people – use of knowledge by people” [4], in this case the paradigm “automated creation of knowledge – use of knowledge in information systems” is realized
The result of the work is a method for determining the weights of temporal rules in a Markov logic network, oriented to an eventual description of a control object functioning in real time
Using weighted rules allows to support the adoption of management decisions by selecting one of the several most likely sequences of actions for the current state of the control object
Summary
Modern methods of automated construction of knowledge bases [1, 2] use information from databases, sites on the Internet, and logs that reflect the functioning of various systems and processes, to isolate sets of dependencies in the format of rules and templates [3].In contrast to the traditional paradigm of building knowledge bases like “creating knowledge by people – use of knowledge by people” [4], in this case the paradigm “automated creation of knowledge – use of knowledge in information systems” is realized. The key advantage of the paradigm of automated knowledge base construction is a significant reduction in the costs of allocating and integrating the required dependencies, which allows to continually add to the set of existing knowledge as we access new data [5, 6]. This advantage provides an opportunity to use the knowledge bases built by the automated method for a wide range of information systems – from information retrieval to information-control systems [7]. Due to the peculiarities of the tasks solved by such information systems, the interpretation of temporal attributes by allocating temporal dependencies is not given enough attention
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