基于知识库分割的多知识库整合方法

知识库是集团企业云制造平台中知识服务的重要基础,知识库的质量直接决定着知识服务的质量。目前单一分散的知识库不能提供统一的知识资源全局视图,不利于知识资源的共享。多知识库整合技术已经成为该领域的研究热点之一。提出一种基于知识库分割的多知识库整合方法,采用基于蚁群聚类的分割策略,将知识库有效划分为知识块集。在知识块间利用语义概念映射生成知识块间映射图,从而实现多知识库整合。通过对算法的时间复杂度进行分析,表明该方法在时间复杂度方面要优于基于最小概念集的多知识库整合方法;实验结果也表明该方法在运行性能方面明显优于已有的方法。     

基于多知识源的语义搭配知识库的构建及应用

针对汉语文本语义搭配错误,在对《现代汉语语义信息词典》(以下简称《语义词典》)及《知网》相关属性的研究基础上,提出了基于多知识源的语义搭配知识库的构建方法,并设计出了面向汉语文本语义查错的三层语义搭配知识库结构.在此基础上,设计并实现了一个汉语文本自动查错算法.实验结果表明,基于上述方法所设计的语义搭配知识库,可以大大提高汉语文本语义查错算法错误查找的召回率,对于语义查错的研究具有一定的借鉴作用.     

Updating knowledge bases

We consider the problem of updating a knowledge base, where a knowledge base is realised as a normal (logic) program. We present procedures for deleting an atom from a normal program and inserting an atom into a normal program, concentrating particularly on the case when negative literals appear in the bodies of program clauses. We also prove various properties of the procedures including their correctness.     

Hybrid knowledge bases

Deductive databases that interact with, and are accessed by, reasoning agents in the real world (such as logic controllers in automated manufacturing, weapons guidance systems, aircraft landing systems, land-vehicle maneuvering systems, and air-traffic control systems) must have the ability to deal with multiple modes of reasoning. Specifically, the types of reasoning we are concerned with include, among others, reasoning about time, reasoning about quantitative relationships that may be expressed in the form of differential equations or optimization problems, and reasoning about numeric modes of uncertainty about the domain which the database seeks to describe. Such databases may need to handle diverse forms of data structures, and frequently they may require use of the assumption-based nonmonotonic representation of knowledge. A hybrid knowledge base is a theoretical framework capturing all the above modes of reasoning. The theory tightly unifies the constraint logic programming scheme of Jaffar and Lassez (1987), the generalized annotated logic programming theory of Kifer and Subrahmanian (1989), and the stable model semantics of Gelfond and Lifschitz (1988). New techniques are introduced which extend both the work on annotated logic programming and the stable model semantics     

Probabilistic knowledge bases

We define a new fixpoint semantics for rule based reasoning in the presence of weighted information. The semantics is illustrated on a real world application requiring such reasoning. Optimizations and approximations of the semantics are shown so as to make the semantics amenable to very large scale real world applications. We finally prove that the semantics is probabilistic and reduces to the usual fixpoint semantics of stratified Datalog if all information is certain. We implemented various knowledge discovery systems which automatically generate such probabilistic decision rules. In collaboration with a bank in Hong Kong we use one such system to forecast currency exchange rates     

Time-evolving rule-based knowledge bases

Human knowledge in any expertise area changes with respect to time. Two types of such knowledge can be identified, time independent and time dependent. It is shown that the maintenance effort of the latter is harder than that of the former. The present paper applies research results in the area of temporal databases, in order to maintain a rule-based knowledge base whose content changes with respect to the real world time. It is shown that the approach simplifies the maintenance of time dependent knowledge. It also enables the study of the evolution of knowledge with respect to time, which is knowledge on its own. Three distinct solutions are actually proposed and evaluated. Their common characteristic is that knowledge is stored in a database; therefore, all the advantages of databases are inherited by knowledge bases. Implementations are also reported.     

Updating knowledge bases II

We consider the problem of updating a knowledge base, where a knowledge base is realised as a (logic) program. In a previous paper, we presented procedures for deleting an atom from a normal program and inserting an atom into a normal program, concentrating particularly on the case when negative literals appear in the bodies of program clauses. We also proved various properties of the procedures including their correctness. Here we present mutually recursive versions of the update procedures and prove their correctness and other properties. We then generalise the procedures so that we can update an (arbitrary) program with an (arbitrary) formula. The correctness of the update procedures for programs is also proved.     

Verification of non-monotonic knowledge bases

Non-monotonic Knowledge-Based Systems (KBSs) must undergo quality assurance procedures for the following two reasons: (i) belief revision (if such is provided) cannot always guarantee the structural correctness of the knowledge base, and in certain cases may introduce new semantic errors in the revised theory; (ii) non-monotonic theories may have multiple extensions, and some types of functional errors which do not violate structural properties of a given extension are hard to detect without testing the overall performance of the KBS. This paper presents an extension of the distributed verification method, which is meant to reveal structural and functional anomalies in non-monotonic KBSs. Two classes of anomalies are considered: (i) structural anomalies which manifest themselves within a given extension (such as logical inconsistencies, structural incompleteness, and intractabilities caused by circular rule chains), and (ii) functional anomalies related to the overall performance of the KBS (such as the existence of complementary rules and some types of rule subsumptions). The corresponding verification tests are presented and illustrated on an extended example.     

Reasoning in inconsistent knowledge bases

Databases and knowledge bases could be inconsistent in many ways. For example, during the construction of an expert system, we may consult many different experts. Each expert may provide us with a group of rules and facts which are self-consistent. However, when we coalesce the facts and rules provided by these different experts, inconsistency may arise. Alternatively, knowledge bases may be inconsistent due to the presence of some erroneous information. Thus, a framework for reasoning about knowledge bases that contain inconsistent information is necessary. However, existing frameworks for reasoning with inconsistency do not support reasoning by cases and reasoning with the law of excluded middle (“everything is either true or false”). In this paper, we show how reasoning with cases, and reasoning with the law of excluded middle may be captured. We develop a declarative and operational semantics for knowledge bases that are possibly inconsistent. We compare and contrast our work with work on explicit and non-monotonic modes of negation in logic programs and suggest under what circumstances one framework may be preferred over another     

Combining knowledge with many-valued logics

During the process of knowledge acquisition from different experts it is usual that contradictions occur. Therefore strategies are needed for dealing with divergent statements and conflicts. We provide a formal framework to represent, process and combine distributed knowledge. The representation formalism is many-valued logic, which is a widely accepted method for expressing uncertainty, vagueness, contradictions and lack of information. Combining knowledge as proposed here makes use of the bilattice approach, which turns out to be very flexible and suggestive in the context of combining divergent information. We give some guidelines for choosing truth value spaces, assigning truth values and defining global operators to encode integration strategies.     

Combining knowledge with many-valued logics

We compare EER and OO data models from the point of view of design quality. Quality is measured in terms of (a) correctness of the conceptual schemas being designed, (b) time to complete the design task, and (c) designers' preferences of the models. Result of an experimental comparison of the two models reveal that the EER model surpasses the OO model for designing unary and ternary relationships, it takes less time to design EER schemas, and the EER model is preferred by designers. We conclude that even if the objective is to implement an OO database schema, the recommended procedure is to: (1) create an EER conceptual scheme, (2) map it to an OO schema, and augment the target schema with behavioral constructs that are unique to the OO approach.     

Combining knowledge from different sources

Abstract: The present paper deals with the problem of an assessment of symptoms in medical diagnosis. A unified interpretation of symptoms is necessary to estimate their significance in a diagnosis. Yet, even if they are properly defined, different evaluations of them based on experts' knowledge or statistical estimation are possible. The present study aims at combining evaluations that may originate from an expert or can be found from statistical features of the data, as well as those determined for 'easy' and 'difficult' diagnostic cases. A model of diagnostic inference is proposed in the framework of the Dempster–Shafer theory extended for fuzzy focal elements. The basic probability assignment defined in this theory estimates weights of symptoms. Two basic probability assignments can be created and then combined. In this way weights of symptoms represent knowledge common for two kinds of data or obtained from an expert and from data. Thus, a combination of heuristics and data mining results becomes possible. An algorithm of the basic probability assignment calculation is suggested and tested for medical data: a database from the internet and individually gathered data.     

Operation management in object-oriented knowledge bases

An object-oriented knowledge base is a database that is constructed on the object data model. Using mathematical logic as formal representation, an object-oriented knowledge base can be constructed to support classification, aggregation, generalization, and association. It further extends existing databases with procedural semantics. In this article we consider the problems of automatic software reusability, object management, and transaction planning with the aid of knowledge and meta-knowledge. We describe the approaches to automate the processes of classifying objects, instantiating abstract algorithms, and ordering conjunctive operations.     

Inference guiding in propositional knowledge bases

When initial data are not sufficient to accomplish inference in a knowledge-based system, more information may be needed. The task of inference guiding or question-asking in that situation is to select missing information to confirm. We present an inference guiding method for propositional knowledge-based systems. Our computational experiments show that the new method is significantly better than the methods currently used in many knowledge-based systems. This revised version was published online in June 2006 with corrections to the Cover Date.     

Multi-level knowledge discovery from rule bases

The discovery of multi-level knowledge is important to allow queries at and across different levels of abstraction. While there are some similarities between our research and that of others in this area, the work reported in this paper does not directly involve databases and is differently motivated. Our research is interested in taking data in the form of rule-bases and finding multi-level knowledge. This paper describes our motivation, our preferred technique for acquiring the initial knowledge known as Ripple-Down Rules, the use of Formal Concept Analysis to develop an abstraction hierarchy, and our application of these ideas to knowledge bases from the domain of chemical pathology. We also provide an example of how the approach can be applied to other prepositional knowledge bases and suggest that it can be used as an additional phase to many existing data mining approaches.     

Updating knowledge bases while maintaining their consistency

When updating a knowledge base, several problems may arise. One of the most important problems is that of integrity constraints satisfaction. The classic approach to this problem has been to develop methods forchecking whether a given update violates an integrity constraint. An alternative approach consists of trying to repair integrity constraints violations by performing additional updates thatmaintain knowledge base consistency. Another major problem in knowledge base updating is that ofview updating, which determines how an update request should be translated into an update of the underlying base facts. We propose a new method for updating knowledge bases while maintaining their consistency. Our method can be used for both integrity constraints maintenance and view updating. It can also be combined with any integrity checking method for view updating and integrity checking. The kind of updates handled by our method are: updates of base facts, view updates, updates of deductive rules, and updates of integrity constraints. Our method is based on events and transition rules, which explicity define the insertions and deletions induced by a knowledge base update. Using these rules, an extension of the SLDNF procedure allows us to obtain all possible minimal ways of updating a knowledge base without violating any integrity constraint.