The use of inference rules to support intelligent data processing is an increasingly important tool in many areas of computer science. In database systems, rules are used in semantic query optimization as a method for reducing query processing costs. The savings is dependent on the ability of experts to supply a set of useful rules and the ability of the optimizer to quickly find the appropriate transformations generated by these rules. Unfortunately, the most useful rules are not always those that would or could be specified by an expert. This paper describes the architecture of a system having two interrelated components: a combined conventional/semantic query optimizer, and an automatic rule deriver. Our automatic rule derivation method uses intermediate results from the optimization process to direct the search for learning new rules. Unlike a system employing only user-specified rules, a system with an automatic capability can derive rules that may be true only in the current state of the database and can modify the rule set to reflect changes in the database and its usage pattern. This system has been implemented as an extension of the EXODUS conventional query optimizer generator. We describe the implementation, and show how semantic query optimization is an extension of conventional optimization in this context.

Natural language database access requires support of both query and update capabilities. Although a great deal of research effort has gone to support natural language database query, little effort has gone to support update. We describe a model of action that supports natural language database update, as well as query, and the implementation of a system that supports the model. A major goal of this research is to design a system that is easily transportable both to different database domains and different database management systems.

Article A transportable natural language database update system Share on Author: Sharon Salveter Boston University, Boston, MA Boston University, Boston, MAView Profile Authors Info & Claims PODS '84: Proceedings of the 3rd ACM SIGACT-SIGMOD symposium on Principles of database systemsApril 1984 Pages 239–247https://doi.org/10.1145/588011.588046Online:02 April 1984Publication History 4citation165DownloadsMetricsTotal Citations4Total Downloads165Last 12 Months5Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Although a great deal of research effort has been expended in support of natural language (NL) database querying, little effort has gone to NL database update. One reason for this state of affairs is that in NL querying, one can tie nouns and stative verbs in the query to database objects (relation names, attributes and domain values). In many cases this correspondence seems sufficient to interpret NL queries. NL update seems to require database counterparts for active verbs, such as "hire," "schedule" and "enroll," rather than for stative entities. There seem to be no natural candidates to fill this role.We suggest a database counterpart for active verbs, which we call verbgraphs. The verbgraphs may be used to support NL update. A verbgraph is a structure for representing the various database changes that a given verb might describe. In addition to describing the variants of a verb, they may be used to disambiguate the update command. Other possible uses of verbgraphs include, specification of defaults, prompting of the user to guide but not dictate user interaction and enforcing a variety of types of database integrity constraints.

One of the main goals of database systems, relational systems in particular, is to provide a degree of physical data independence for users and programs. Users should not need to know the exact physical storage structures to use the database, and should be protected from changes in those structures. We attempt to go a step further, to logical data independence. We want an interface to a relational database where a user need not be concerned with how the data has been partitioned into various relations. The natural relation schemes to be used, from a semantic point of view, may be decomposed in the database for normalization or redundancy reasons. Our approach essentially loads all the semantics onto the attributes. In our query language tuple variables are not bound to specific relations. Rather, the system uses the set of attributes, say X, that appear in a query with a tuple variable, say t, to combine the database relations to form a single relation with scheme X over which t ranges. We describe our method for constructing such a relation given the associated set of attributes X. When tuple variables are bound implicitly, the logical connectives 'and', 'or', and 'not' take on 'semantic overtones' since they can affect the binding. We discuss the motivation behind the chosen semantics for these connectives. Our goal is a powerful, yet concise, query language with natural semantics.

Knowledge representation schemes are either based on a set of primitives or not. The decision of whether or not to have a primitive-based scheme is crucial since it affects the knowledge that is stored and how that knowledge may be processed. We suggest that a knowledge representation scheme may not initially have primitives, but may evolve into a primitive-based scheme by inferring a set of primitive meaning units based on previous experience. We describe a program that infers its own primitive set and discuss how the inferred primitives may affect the organization of existing information and the subsequent incorporation of new information.