CLASSIC: a structural data model for objects

Abstract

Abstractclassic is a data model that encourages the description o f o b j e cts not only in terms of their relations to other known objects, but in terms of a level of intensional structurea sw ell. The classic language of structured descriptions permits i partial descriptions of individuals, under an`open world' assumption, ii answers to queries either as extensional lists of valueso ra sd e s criptions that necessarily hold of all possible answers, and iiia n easily extensible s c hema, which can be accessed uniformly with the data. One of the strengths of the approach is that the same language plays multiple roles in the processes of de ning and populating the DB, as well as querying and answering.classic for which w e h a ve a prototype main-memory implementation can actively discover new information about objects from several sources: it can recognize new classes under which an object falls based on a description of the object, it can propagate some deductive consequences of DB updates, it has simple procedural recognizers, and it supports a limited form of forward-chaining rules to derive new conclusions about known objects.The kind of language of descriptions and queries presented here provides a new arena for the search for languages that are more expressive than conventional DBMS languages, but for which query processing is still tractable. This spaceo f languages di ers from the subsets of predicate calculus hitherto exploredb y deductive databases. MotivationA database is normally used to maintain a model of some aspect of reality. T raditional data models, such as the relational one,h a vea c hieved great e ciency in data storage and retrieval by restricting m o d e ling power; in particular, the database is assumed to be a complete and accurate model of the world, where all the individual objects are restricted With Department of Computer Science, Rutgers University, New Brunswick, NJ 08903. 1 to be primitivev alues liken umbers and strings, and all their inter-relationships are known and expressly stated. While undeniably of extensive v alue, this makes traditional data models unsuitable for a number of situations, for example, when complex objects are the naturalw ay of describing the domain; when information about the domaini sincomplete or becomesa vailable incrementally; when the database should be taking a more active role in deducing relationships rather than being just a passive repository of data. These situations include those in which new artifacts are being designed e.g., CAD CAM, con guration, or an understanding of some existing situation is being builtu po ver time e.g., diagnostic situations.The eldo flogic or deductive databases 14 has emerged as one response to some of these weaknesses: incomplete information can be expressed naturally in logical languages using disjunction and existential quanti ers, and the database can infer new relationships through deductive rules. The chief drawback of this approach is computationalintractability: a generalv ersion of this problem is equivale...

General default logic

“…We also showed that checking strong equivalence between two rules can be reduced into the logic of GK. Consequently, each rule base is strongly equivalent to a set of rules of form (3). Finally, we investigated the computational complexity in relation to this logic.…”

“…In this section, we show that each rule base can be strongly equivalently transformed into a set of rules of form (3). The key technique of proving this is Theorem 11.…”

“…The combination of classical logic and rules is nothing new. For instance, CLASSIC [3] is an early description logic allowing simple monotonic rules. Donini et al [6] combined Datalog with description logic ALC.…”

Incorporating Generalized Quantifiers into Description Logic to Improve Source Selection

“…Recently there have been efforts applying DL in the database field (Anwar, Beck, and Navathe, 1992;Borgida and Brachman, 1993;Beneventano, Bergamaschi, and Lordi, 1994;Borgida, Brachman, and McGuinness, 1989;Beck, Gala, and Navathe, 1989;Borgida, 1995;Bresciani, 1995;Bresciani, 1996;Bergamaschi, Lordi, and Sartori, 1994;Devanbu, 1993;Kessel, Rousselot, Schlick, and Stern, 1995), mainly because of the capabilities that are considered fundamental in semantic data modeling such as structural description and taxonomic classification (Hull and King, 1987) are inherently equipped within DL. Structural constructors pivotal within DL are concept, individual, and role where an individual represents a single object, a concept is a collection of individuals, and a role is a relation associating a pair of concepts or individuals.…”

Encoding multiple inheritance hierarchies for lattice operations

“…Inheritance relationships also arise in querying databases [2], and subsumption is used frequently in view management. Finally, knowledge representation systems allow concepts to be organized into subclass hierarchies, with inheritance being the key component of the reasoning algorithms [3].…”