The Research On Technology Of Extracting And Fusing Non-canonical Knowledge

At present,most of the knowledge system can only deal with canonical knowledge which has the characteristics of well structure,enclosed area,coordinated content,clear extension and integrity content.If a system encounters the issues which are slightly beyond or slightly contrary to the knowledge of these features,it may face collapse,and show its extreme vulnerability.Due to the urgent need of practical applications,the research of computer science has had many tremendous changes.The people change the research from the problem of object-oriented to content-oriented;from the problem of well structure to ill structure;from the closed-oriented world to open world;from the problems of consistent and precise content to incomplete, inaccurate and inconsistent content.These trends in the knowledge processing research show an extremely difficult topics which was less studied in the past,namely non-canonical knowledge processing.The non-canonical knowledge is refers to the intractability of knowledge connotation, including the uncertainty knowledge(fuzzy and uncertain knowledge,random and imprecise knowledge),or the incomplete knowledge(contents incomplete knowledge and structure incomplete knowledge) or the non-consistent knowledge(contradiction knowledge,noise and redundant knowledge) or the non-permanent knowledge(time-varying knowledge and heuristic knowledge).The most typical application of the non.canonical knowledge is the knowledge processing on the Internet.However,the most knowledge on the internet is non-structural or semi-structural, and it exists in various forms.The knowledge takes natural language as the carrier and locates in the hundreds of millions of pages,which are increased,disappeared or changed by one million of magnitude per day,and are fraught with contradictions of the facts,data and viewpoints.The internet's rapid development and wide application require a knowledge sharing and utilization with flexible,credible,concerted,in-depth way in an open,dynamic environment.Therefore,the realization of this goal to a great extent depends on the advancements in non-canonical knowledge processing technology.The tunnel project and other large projects are the significantly complex systems which have more variable,non-linear,uncertainty and delay characteristics.There is a huge of real-time data in these projects.Extracting non-canonical knowledge from these data is another typical application area.These data need to use various methods and the mathematical model to be described.The effects of using an arbitrary single model is limited,the results synthesis of all model is a big problem,so that the problem of canonical knowledge integration is proposed.The dissertation takes the knowledge processing of the Internet and the Shield Construction Risk Control Knowledge Management as the main background,circles the non-canonical knowledge extraction and fusing issues to explore and research a number of key technologies,and applies them in the solution of practical problems.The dissertation firstly introduces the definition of non-canonical knowledge,and then summarizes non-canonical knowledge processing research status in basic aspect and applied aspect.The second chapter of the dissertation studies the extraction of the content incomplete knowledge based on rough sets.We proposed an Attribute Extension Algorithm of irregular decision table(AEA) and an Extraction Algorithm of Approximate Sequence Decision Rules (EAASDR).AEA selects the important attributes from the original decision table and forms an optimal decision table.The rule extracts from this optimized table is possibly inconsistent and adds new attributes can eliminate such inconsistencies.EAASDR can extract complete knowledge or the knowledge met the specific request from the boundary region of decision information systems.The third chapter of the dissertation researches extraction of the noise knowledge based on neural networks and neural network ensemble.We propose the Staircase Activation function Multi-Layer Perceptron(SAMLP) using a step function as the activation function and extract rules from the SAMLP.We also propose an ensemble SAMLP and use it to extract rules.We present a Hybrid Model based on Rough sets and Neural networks(HMRN) extracting rules from the incomplete and sound information table.The forth chapter of the dissertation researches fusion the contradictions knowledge based on belief revision.We study a model of fusing inconsistency knowledge based on social belief negotiation and consider both a postulational and a procedural approach to social contraction. We also study the knowledge fusion method in the possible logical semantics.According to whether or not the information resources are consistent,contradictory,redundant or independent, we study each kind of fusion operator and design a fusion knowledge frame based on the standard possible logic and explain the frame implementation with an example.The fifth chapter of the dissertation researches fusion non-permanent knowledge.Based on the information filtering technology,non-permanent knowledge is converted into uncertainty structure knowledge expressed with three kinds of types(probability theory,evidence theory and possible theory).The dissertation introduces the method to fuse homogeneous type uncertain structure knowledge and the heterogeneous type uncertain structure knowledge.In the sixth chapter we have launched an application research of extraction and fusing non-canonical knowledge in the project of Shield Construction Risk Control Knowledge Management System.With the extracting and fusing technology of non-canonical knowledge, the project has established a model platform of Shield Construction risk knowledge management. The platform realizes the risk management process from the risk identification,risk assessment, risk warning,risk control and risk post-processing.The platform offers a new angle to rethink and explore shield tunnel project risk control techniques and strategies,and to promote risk-control technology innovation.