Knowledge bases and databases: converging technologies, diverginginterests

The convergence of knowledge-based and database technologies in the development of expert databases which store not only values but chunks of knowledge is discussed. The knowledge-based programming perspective is examined, and differences between knowledge bases and databases are identified. It is argued that in spite of converging technologies, the interests of knowledge-base specialists and database specialists differ, and their research directions diverge     

Knowledge capitalisation through case bases and knowledge engineering for road safety analysis

New generation knowledge-based systems should be fully integrated into their environment, by exploiting existing information sources, and should be flexible and easily extensible. This article describes the architecture of an organisational memory (OM) for road safety analysis. Starting from the design of a knowledge-based system, we show how we address knowledge capitalisation issues through the building of an OM. We present its main components and describe how knowledge engineering techniques can be exploited to build and enrich it. We then describe the major task that exploits the OM as decision support for site analysis. We also explain how domain knowledge can be exploited and capitalised using case-based reasoning and collaborative work.     

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     

Distributed data bases

Distributed data bases provide a means for storing, managing and accessing large volumes of structured data. This technology is coupled with current developments in distributed systems. This paper references a distributed system under development by the Surveys and Mapping Branch of the Department of Energy, Mines and Resources in Canada, for the production of faired drawings from a geographic data base. Some of the notions and considerations involved in the development of distributed data bases also are discussed.     

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     

Temporal probabilistic object bases

There are numerous applications where we have to deal with temporal uncertainty associated with objects. The ability to automatically store and manipulate time, probabilities, and objects is important. We propose a data model and algebra for temporal probabilistic object bases (TPOBs), which allows us to specify the probability with which an event occurs at a given time point. In explicit TPOB-instances, the sets of time points along with their probability intervals are explicitly enumerated. In implicit TPOB-instances, sets of time points are expressed by constraints and their probability intervals by probability distribution functions. Thus, implicit object base instances are succinct representations of explicit ones; they allow for an efficient implementation of algebraic operations, while their explicit counterparts make defining algebraic operations easy. We extend the relational algebra to both explicit and implicit instances and prove that the operations on implicit instances correctly implement their counterpart on explicit instances.     

Combining multiple knowledge bases

Combining knowledge present in multiple knowledge base systems into a single knowledge base is discussed. A knowledge based system can be considered an extension of a deductive database in that it permits function symbols as part of the theory. Alternative knowledge bases that deal with the same subject matter are considered. The authors define the concept of combining knowledge present in a set of knowledge bases and present algorithms to maximally combine them so that the combination is consistent with respect to the integrity constraints associated with the knowledge bases. For this, the authors define the concept of maximality and prove that the algorithms presented combine the knowledge bases to generate a maximal theory. The authors also discuss the relationships between combining multiple knowledge bases and the view update problem     

Large engineering knowledge bases

This paper concerns the development of numerous knowledge bases for engineering applications and integrating them into one knowledge environment applicable in different problem domains. It discusses steps made towards building large heterogeneous engineering knowledge bases. It analyses the requirements to large knowledge bases, presents their architecture and discusses the content and size of general-purpose engineering knowledge bases.     

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.     

Interpolation using wavelet bases

Efficient solutions to regularization problems can be obtained using orthogonal wavelet bases for preconditioning. Good approximate solutions can be obtained in only two or three iterations, with each iteration requiring only O(n) operations and O(n) storage locations. Two- and three-dimensional examples are shown using both synthetic and real range data     

On finding cycle bases and fundamental cycle bases with a shortest maximal cycle

An undirected biconnected graph G with nonnegative integer lengths on the edges is given. The problem we consider is that of finding a cycle basis B of G such that the length of the longest cycle included in B is the smallest among all cycle bases of G. We first observe that Horton's algorithm [SIAM J. Comput. 16 (2) (1987) 358-366] provides a fast solution of the problem that extends the one given by Chickering et al. [Inform. Process. Lett. 54 (1995) 55-58] for uniform graphs. On the other hand we show that, if the basis is required to be fundamental, then the problem is NP-hard and cannot be approximated within 2−?, ∀?>0, even with uniform lengths, unless P=NP. This problem remains NP-hard even restricted to the class of complete graphs; in this case it cannot be approximated within 13/11−?, ∀?>0, unless P=NP; it is instead approximable within 2 in general, and within 3/2 if the triangle inequality holds.     

Discovering regularities from knowledge bases

Knowledge bases open new horizons for machine learning research. One challenge is to design learning programs to expand the knowledge base using the knowledge that is currently available. This article addresses the problem of discovering regularities in large knowledge bases that contain many assertions in different domains. the article begins with a definition of regularities and gives the motivation for such a definition. It then outlines a framework that attempts to integrate induction with knowledge. Although the implementation of the framework currently uses only a statistical method for confirming hypotheses, its application to a real knowledge base has shown some encouraging and interesting results. © 1992 John Wiley & Sons, Inc.     

Changes of disjunctively closed bases

An operator of contraction for a belief set (a theory) can be obtained by assigning to it a belief base and an operator of partial meet contraction for that base. It is argued that closure of the base under disjunction is an intuitively reasonable condition. Axiomatic characterizations are given of the contractions of belief sets that can be generated by (various types of) partial meet contraction on disjunctively closed bases. The corresponding revision operators are also characterized. Finally, some results are reported on operations on bases that are closed under material implication.I would like to thank Hans Rott and two anonymous referees for valuable comments and the Swedish Council for Research in the Humanities and Social Sciences for financial support.